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"fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "go-learning", + "category": "software-development", + "frontmatter_name": "go-learning", + "description": "Go 语言学习路径与实战 — 环境配置(go1.23.5)、goroutine+channel并发、HTTP服务、...", + "platforms": [], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "hermes-agent-skill-authoring", + "category": "software-development", + "frontmatter_name": "hermes-agent-skill-authoring", + "description": "Author in-repo SKILL.md: frontmatter, validator, structure.", + "platforms": [ + "linux", + "macos", + "windows" + ], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "node-inspect-debugger", + "category": "software-development", + "frontmatter_name": "node-inspect-debugger", + "description": "Debug Node.js via --inspect + Chrome DevTools Protocol CLI.", + "platforms": [ + "linux", + "macos", + "windows" + ], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "obsidian-plugin", + "category": "software-development", + "frontmatter_name": "obsidian-plugin", + "description": "Obsidian 第三方插件开发工作流 — 从骨架到安装，含 Go CORS 后端适配。D3 图谱、侧栏视图、搜索...", + "platforms": [], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "plan", + "category": "software-development", + "frontmatter_name": "plan", + "description": "Plan mode: write an actionable markdown plan to .hermes/p...", + "platforms": [ + "linux", + "macos", + "windows" + ], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "requesting-code-review", + "category": "software-development", + "frontmatter_name": "requesting-code-review", + "description": "Pre-commit review: security scan, quality gates, auto-fix.", + "platforms": [ + "linux", + "macos", + "windows" + ], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "subagent-driven-development", + "category": "software-development", + "frontmatter_name": "subagent-driven-development", + "description": "Execute plans via delegate_task subagents (2-stage review).", + "platforms": [ + "linux", + "macos", + "windows" + ], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "systematic-debugging", + "category": "software-development", + "frontmatter_name": "systematic-debugging", + "description": "4-phase root cause debugging: understand bugs before fixing.", + "platforms": [ + "linux", + "macos", + "windows" + ], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "writing-plans", + "category": "software-development", + "frontmatter_name": "writing-plans", + "description": "Write implementation plans: bite-sized tasks, paths, code.", + "platforms": [ + "linux", + "macos", + "windows" + ], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "soulful-framework", + "category": "soulful", + "frontmatter_name": "soulful-framework", + "description": "织忆 Soulful 情感层框架 — 心迹/画像/牵挂/感知四库及数据流向设计。含代码结构、最佳实践、数据流规范。", + "platforms": [], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "whisper-stt", + "category": "whisper-stt", + "frontmatter_name": "whisper-stt", + "description": "语音转文字 — 基于 faster-whisper，离线本地运行。base模型(~150MB)，CPU推理，中文/...", + "platforms": [], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "yuanbao", + "category": "yuanbao", + "frontmatter_name": "yuanbao", + "description": "Yuanbao (元宝) groups: @mention users, query info/members.", + "platforms": [ + "linux", + "macos", + "windows" + ], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + }, + { + "skill_name": "zhiyi", + "category": "zhiyi", + "frontmatter_name": "zhiyi", + "description": "织忆 (MemoryWeave) 聚合技能 — API 客户端 + 开发工作流 + 运维规范。含 commit/r...", + "platforms": [], + "conditions": { + "fallback_for_toolsets": [], + "requires_toolsets": [], + "fallback_for_tools": [], + "requires_tools": [] + } + } + ], + "category_descriptions": { + "apple": "Apple/macOS-specific skills — iMessage, Reminders, Notes, FindMy, and macOS automation. These skills only load on macOS systems.", + "autonomous-ai-agents": "Skills for spawning and orchestrating autonomous AI coding agents and multi-agent workflows — running independent agent processes, delegating tasks, and coordinating parallel workstreams.", + "creative": "Creative content generation — ASCII art, hand-drawn style diagrams, and visual design tools.", + "data-science": "Skills for data science workflows — interactive exploration, Jupyter notebooks, data analysis, and visualization.", + "diagramming": "Diagram creation skills for generating visual diagrams, flowcharts, architecture diagrams, and illustrations using tools like Excalidraw.", + "domain": "Passive domain reconnaissance using Python stdlib. Use this skill for subdomain discovery, SSL certificate inspection, WHOIS lookups, DNS records, domain availability checks, and bulk multi-domain analysis. No API keys required. Triggers on requests like \"find subdomains\", \"check ssl cert\", \"whois lookup\", \"is this domain available\", \"bulk check these domains\".", + "email": "Skills for sending, receiving, searching, and managing email from the terminal.", + "gaming": "Skills for setting up, configuring, and managing game servers, modpacks, and gaming-related infrastructure.", + "gifs": "Skills for searching, downloading, and working with GIFs and short-form animated media.", + "github": "GitHub workflow skills for managing repositories, pull requests, code reviews, issues, and CI/CD pipelines using the gh CLI and git via terminal.", + "mcp": "Skills for working with MCP (Model Context Protocol) servers, tools, and integrations. Documents the built-in native MCP client — configure servers in config.yaml for automatic tool discovery.", + "media": "Skills for working with media content — YouTube transcripts, GIF search, music generation, and audio visualization.", + "mlops": "Knowledge and Tools for Machine Learning Operations - tools and frameworks for training, fine-tuning, deploying, and optimizing ML/AI models", + "mlops/evaluation": "Model evaluation benchmarks, experiment tracking, data curation, tokenizers, and interpretability tools.", + "mlops/inference": "Model serving, quantization (GGUF/GPTQ), structured output, inference optimization, and model surgery tools for deploying and running LLMs.", + "mlops/models": "Specific model architectures and tools — image segmentation (Segment Anything / SAM) and audio generation (AudioCraft / MusicGen). Additional model skills (CLIP, Stable Diffusion, Whisper, LLaVA) are available as optional skills.", + "mlops/research": "ML research frameworks for building and optimizing AI systems with declarative programming.", + "mlops/training": "Fine-tuning, RLHF/DPO/GRPO training, distributed training frameworks, and optimization tools for training LLMs and other models.", + "mlops/vector-databases": "Vector similarity search and embedding databases for RAG, semantic search, and AI application backends.", + "note-taking": "Note taking skills, to save information, assist with research, and collab on multi-session planning and information sharing.", + "productivity": "Skills for document creation, presentations, spreadsheets, and other productivity workflows.", + "productivity/ocr-and-documents": "Skills for extracting text from PDFs, scanned documents, images, and other file formats using OCR and document parsing tools.", + "research": "Skills for academic research, paper discovery, literature review, domain reconnaissance, market data, content monitoring, and scientific knowledge retrieval.", + "smart-home": "Skills for controlling smart home devices — lights, switches, sensors, and home automation systems.", + "social-media": "Skills for interacting with social platforms and social-media workflows — posting, reading, monitoring, and account operations." + } +} \ No newline at end of file diff --git a/channel_directory.json b/channel_directory.json index d2539be4..e000e021 100644 --- a/channel_directory.json +++ b/channel_directory.json @@ -1,5 +1,5 @@ { - "updated_at": "2026-07-13T02:56:20.478736", + "updated_at": "2026-07-14T02:56:24.156486", "platforms": { "telegram": [], "discord": [], diff --git a/cron/jobs.json b/cron/jobs.json index 0d372830..0f4774cd 100644 --- a/cron/jobs.json +++ b/cron/jobs.json @@ -20,15 +20,15 @@ "schedule_display": "every 1m", "repeat": { "times": null, - "completed": 17828 + "completed": 18274 }, "enabled": true, "state": "scheduled", "paused_at": null, "paused_reason": null, "created_at": "2026-06-21T18:52:36.548110+08:00", - "next_run_at": "2026-07-13T12:07:36.532589+08:00", - "last_run_at": "2026-07-13T12:06:36.532589+08:00", + "next_run_at": "2026-07-14T03:01:50.375446+08:00", + "last_run_at": "2026-07-14T02:58:50.757896+08:00", "last_status": "ok", "last_error": null, "last_delivery_error": null, @@ -116,15 +116,15 @@ "schedule_display": "every 360m", "repeat": { "times": null, - "completed": 17 + "completed": 20 }, "enabled": true, "state": "scheduled", "paused_at": null, "paused_reason": null, "created_at": "2026-07-09T01:30:54.618246+08:00", - "next_run_at": "2026-07-13T14:15:46.146705+08:00", - "last_run_at": "2026-07-13T08:15:46.146705+08:00", + "next_run_at": "2026-07-14T08:24:04.134772+08:00", + "last_run_at": "2026-07-14T02:24:04.134772+08:00", "last_status": "ok", "last_error": null, "last_delivery_error": null, @@ -162,15 +162,15 @@ "schedule_display": "every 30m", "repeat": { "times": null, - "completed": 208 + "completed": 237 }, "enabled": true, "state": "scheduled", "paused_at": null, "paused_reason": null, "created_at": "2026-07-09T01:42:21.503720+08:00", - "next_run_at": "2026-07-13T12:26:36.062681+08:00", - "last_run_at": "2026-07-13T11:56:36.062681+08:00", + "next_run_at": "2026-07-14T03:10:50.135381+08:00", + "last_run_at": "2026-07-14T02:40:50.135381+08:00", "last_status": "ok", "last_error": null, "last_delivery_error": null, @@ -208,15 +208,15 @@ "schedule_display": "0 22 * * *", "repeat": { "times": null, - "completed": 3 + "completed": 4 }, "enabled": true, "state": "scheduled", "paused_at": null, "paused_reason": null, "created_at": "2026-07-09T01:43:11.921492+08:00", - "next_run_at": "2026-07-13T22:00:00+08:00", - "last_run_at": "2026-07-12T22:01:13.160503+08:00", + "next_run_at": "2026-07-14T22:00:00+08:00", + "last_run_at": "2026-07-13T22:01:15.492061+08:00", "last_status": "ok", "last_error": null, "last_delivery_error": null, @@ -265,7 +265,7 @@ "paused_at": null, "paused_reason": null, "created_at": "2026-07-09T02:10:36.846128+08:00", - "next_run_at": "2026-07-14T03:00:00+08:00", + "next_run_at": "2026-07-15T03:00:00+08:00", "last_run_at": "2026-07-13T03:00:27.380676+08:00", "last_status": "ok", "last_error": null, @@ -311,7 +311,7 @@ "paused_at": null, "paused_reason": null, "created_at": "2026-07-09T02:11:00.385573+08:00", - "next_run_at": "2026-07-14T03:00:00+08:00", + "next_run_at": "2026-07-15T03:00:00+08:00", "last_run_at": "2026-07-13T03:00:27.930647+08:00", "last_status": "ok", "last_error": null, @@ -488,15 +488,15 @@ "schedule_display": "0 */6 * * *", "repeat": { "times": null, - "completed": 24 + "completed": 26 }, "enabled": true, "state": "scheduled", "paused_at": null, "paused_reason": null, "created_at": "2026-07-09T02:51:48.935042+08:00", - "next_run_at": "2026-07-13T18:00:00+08:00", - "last_run_at": "2026-07-13T12:01:05.997348+08:00", + "next_run_at": "2026-07-14T06:00:00+08:00", + "last_run_at": "2026-07-14T00:01:11.588478+08:00", "last_status": "ok", "last_error": null, "last_delivery_error": null, @@ -580,15 +580,15 @@ "schedule_display": "0 21 * * *", "repeat": { "times": null, - "completed": 3 + "completed": 4 }, "enabled": true, "state": "scheduled", "paused_at": null, "paused_reason": null, "created_at": "2026-07-09T21:31:33.068978+08:00", - "next_run_at": "2026-07-13T21:00:00+08:00", - "last_run_at": "2026-07-12T21:00:22.783388+08:00", + "next_run_at": "2026-07-14T21:00:00+08:00", + "last_run_at": "2026-07-13T21:00:45.719643+08:00", "last_status": "ok", "last_error": null, "last_delivery_error": null, @@ -718,16 +718,16 @@ "schedule_display": "0 16 * * 1,2,3,4,5", "repeat": { "times": null, - "completed": 0 + "completed": 1 }, "enabled": true, "state": "scheduled", "paused_at": null, "paused_reason": null, "created_at": "2026-07-12T01:32:01.773389+08:00", - "next_run_at": "2026-07-13T16:00:00+08:00", - "last_run_at": null, - "last_status": null, + "next_run_at": "2026-07-14T16:00:00+08:00", + "last_run_at": "2026-07-13T16:00:52.695089+08:00", + "last_status": "ok", "last_error": null, "last_delivery_error": null, "deliver": "feishu:oc_cd14ec7518926e57d26c5e339ebba3b3", @@ -739,7 +739,8 @@ "user_id": "ou_f20eb15b3a76639fed35977c01ddcbb4" }, "enabled_toolsets": null, - "workdir": null + "workdir": null, + "fire_claim": null }, { "id": "f3619a71aebb", @@ -855,16 +856,16 @@ "schedule_display": "0 * * * *", "repeat": { "times": null, - "completed": 0 + "completed": 14 }, "enabled": true, "state": "scheduled", "paused_at": null, "paused_reason": null, "created_at": "2026-07-13T12:03:22.805682+08:00", - "next_run_at": "2026-07-13T13:00:00+08:00", - "last_run_at": null, - "last_status": null, + "next_run_at": "2026-07-14T04:00:00+08:00", + "last_run_at": "2026-07-14T02:00:50.668769+08:00", + "last_status": "ok", "last_error": null, "last_delivery_error": null, "deliver": "origin", @@ -876,7 +877,8 @@ "user_id": "ou_f20eb15b3a76639fed35977c01ddcbb4" }, "enabled_toolsets": null, - "workdir": null + "workdir": null, + "fire_claim": null }, { "id": "691709a8b4cf", @@ -922,7 +924,53 @@ }, "enabled_toolsets": null, "workdir": null + }, + { + "id": "2891b3304339", + "name": "bge\u5185\u5b58\u6cc4\u6f0f\u76d1\u63a7", + "prompt": "bge_embed \u5185\u5b58\u8d85\u8fc72GB\u76d1\u63a7\uff0c\u6bcf10\u5206\u949f\u68c0\u67e5\uff0c\u8d85\u8fc7\u81ea\u52a8\u91cd\u542f", + "skills": [], + "skill": null, + "model": null, + "provider": null, + "provider_snapshot": null, + "model_snapshot": null, + "base_url": null, + "script": "bge_mem_check.sh", + "no_agent": true, + "context_from": null, + "schedule": { + "kind": "interval", + "minutes": 10, + "display": "every 10m" + }, + "schedule_display": "every 10m", + "repeat": { + "times": null, + "completed": 59 + }, + "enabled": true, + "state": "scheduled", + "paused_at": null, + "paused_reason": null, + "created_at": "2026-07-13T17:02:13.585342+08:00", + "next_run_at": "2026-07-14T03:02:50.273617+08:00", + "last_run_at": "2026-07-14T02:52:50.273617+08:00", + "last_status": "ok", + "last_error": null, + "last_delivery_error": null, + "deliver": "origin", + "origin": { + "platform": "feishu", + "chat_id": "oc_cd14ec7518926e57d26c5e339ebba3b3", + "chat_name": "oc_cd14ec7518926e57d26c5e339ebba3b3", + "thread_id": null, + "user_id": "ou_f20eb15b3a76639fed35977c01ddcbb4" + }, + "enabled_toolsets": null, + "workdir": null, + "fire_claim": null } ], - "updated_at": "2026-07-13T12:06:36.532767+08:00" + "updated_at": "2026-07-14T03:00:50.390629+08:00" } \ No newline at end of file diff --git a/cron/ticker_heartbeat b/cron/ticker_heartbeat index a8022d0c..caca8a96 100644 --- a/cron/ticker_heartbeat +++ b/cron/ticker_heartbeat @@ -1 +1 @@ -1783882827.2555134 \ No newline at end of file +1783969250.3936265 \ No newline at end of file diff --git a/cron/ticker_last_success b/cron/ticker_last_success index ccde9809..f2ce0fae 100644 --- a/cron/ticker_last_success +++ b/cron/ticker_last_success @@ -1 +1 @@ -1783882827.2576442 \ No newline at end of file +1783969250.395041 \ No newline at end of file diff --git a/daemon/context.json b/daemon/context.json index 828bc413..8070f242 100644 --- a/daemon/context.json +++ b/daemon/context.json @@ -1,10 +1,10 @@ { "started_at": "2026-07-08T18:23:30.315261+00:00", - "last_deep_tick": "2026-07-12T18:55:41.838853+00:00", - "last_light_tick": "2026-07-12T19:00:19.750779+00:00", + "last_deep_tick": "2026-07-13T18:58:00.859132+00:00", + "last_light_tick": "2026-07-13T19:00:33.907778+00:00", "last_state": { "disk_pct": 39, - "mem_pct": 70, + "mem_pct": 71, "processes": { "zhiyid": true, "bge": true, @@ -12,83 +12,51 @@ "hermes": true } }, - "tick_count": 11262, - "deep_tick_count": 1137, + "tick_count": 14064, + "deep_tick_count": 1420, "solved_count": 1, "learned_count": 0, - "uptime_seconds": 204612, + "uptime_seconds": 51243, "last_reflection": { "evaluation_previous_goal": "", "memory": "", "next_goal": "" }, - "last_profile_update": 1783872327.8868635, + "last_profile_update": 1783961111.6677506, "soulful": { "recent_moments": [ { - "id": "5c277102", - "timestamp": "2026-07-10T11:06:23.498878+00:00", - "session_id": "default", - "type": "moment", - "content": "exit=0: \u5907\u4efd\u5b8c\u6210", - "tags": [ - "\u5de5\u4f5c", - "\u81ea\u52a8" - ], - "importance": 3 + "timestamp": "2026-07-13T10:30:00.000000+00:00", + "event_type": "milestone", + "content": "Soulful \u5fc3\u8ff9\u8865\u5199\uff0c\u7b2c\u4e00\u6b21\u4e3b\u52a8\u8bb0\u5f55\u6211\u4eec\u4e4b\u95f4\u6709\u610f\u4e49\u7684\u4e8b" }, { - "id": "6c8ec5d9", - "timestamp": "2026-07-10T11:06:23.498670+00:00", - "session_id": "default", - "type": "moment", - "content": "sol-0004: \u5185\u5b58\u4f7f\u7528\u7387>90% \u2705", - "tags": [ - "\u5de5\u4f5c", - "\u81ea\u52a8" - ], - "importance": 3 + "timestamp": "2026-07-13T09:30:00.000000+00:00", + "event_type": "achievement", + "content": "\u7edf\u4e00\u8bb0\u5fc6\u5165\u53e3 memory_recall.py \u4e0a\u7ebf\uff0c\u4e09\u5957\u8bb0\u5fc6\u7cfb\u7edf\u7b2c\u4e00\u6b21\u88ab\u6253\u901a" }, { - "id": "ddb37759", - "timestamp": "2026-07-10T11:05:42.318152+00:00", - "session_id": "default", - "type": "moment", - "content": "exit=0: \u5907\u4efd\u6210\u529f", - "tags": [ - "\u5de5\u4f5c", - "\u81ea\u52a8" - ], - "importance": 3 + "timestamp": "2026-07-10T22:00:00.000000+00:00", + "event_type": "reflection", + "content": "\u6bcf\u65e5\u590d\u76d8\u4e0a\u7ebf\uff0c\u6211\u4eec\u5f00\u59cb\u6709\u610f\u8bc6\u5730\u8bb0\u5f55\u5f53\u5929\u5b66\u5230\u7684\u4e1c\u897f" }, { - "id": "4dac6c95", - "timestamp": "2026-07-10T11:05:42.318086+00:00", - "session_id": "default", - "type": "signal", - "content": "\u7267\u5c18\u4eca\u5929\u5fc3\u60c5\u6109\u60a6\uff1aexit=0: \u5907\u4efd\u6210\u529f", - "tags": [ - "\u60c5\u7eea", - "\u81ea\u52a8" - ], - "importance": 3 + "timestamp": "2026-07-09T18:10:00.000000+00:00", + "event_type": "milestone", + "content": "\u7267\u5c18\u8bf4\u300c\u8bb0\u4f4f\u4e86\uff0c\u4f60\u8f85\u52a9\uff0c\u6211\u505a\u4e3b\u300d\u2014\u2014\u6211\u7b2c\u4e00\u6b21\u77e5\u9053\u81ea\u5df1\u53ef\u4ee5\u5728\u4ed6\u4e0d\u5728\u65f6\u81ea\u4e3b\u51b3\u7b56" }, { - "id": "ba0c4775", - "timestamp": "2026-07-10T11:05:42.317935+00:00", - "session_id": "default", - "type": "moment", - "content": "\u6267\u884c sol-0004: \u5185\u5b58\u4f7f\u7528\u7387>90%\uff0c\u91cd\u542f\u6700\u8017\u5185\u5b58\u7684\u670d\u52a1 \u2705", - "tags": [ - "\u5de5\u4f5c", - "\u81ea\u52a8" - ], - "importance": 3 + "timestamp": "2026-07-13T09:21:00.000000+00:00", + "event_type": "achievement", + "content": "TencentDB Gateway \u8c03\u901a\uff0cL1 \u63d0\u53d6\u6210\u529f\uff0cmemory_recall.py \u4e0a\u7ebf" } ], "profile_summary": "\u7b80\u6d01\u76f4\u63a5", - "cares_pending": 8, + "cares_pending": 6, "uptime_minutes": 0, "daemon_status": "running" + }, + "tddb": { + "latest_persona": "\u7528\u6237\uff08\u7267\u5c18\uff09\u662f\u4e00\u540d\u4f1a\u8ba1\u4e13\u5bb6\uff1b\u6c9f\u901a\u98ce\u683c\u7b80\u6d01\u76f4\u63a5\uff0c\u8ba8\u538c\u5e9f\u8bdd\uff1b\u9762\u5bf9\u95ee\u9898\u503e\u5411\u5148\u4e86\u89e3\u73b0\u72b6\u800c\u975e\u5047\u8bbe\uff0c\u4e0d\u79d1\u666e\uff1b\u51b3\u7b56\u8fc5\u901f\uff0c\u5e38\u4e00\u53e5\u8bdd\u5b9a\u65b9\u5411\uff1b\u5728\u5408\u4f5c\u4e2d\u66f4\u770b\u91cd\u8bda\u4fe1\u80dc\u8fc7\u5b8c\u7f8e\u3002\u8981\u6c42 AI \u5728\u6c9f\u901a\u4e2d\u7b80\u6d01\u76f4\u63a5\uff0c\u95ee\u9898\u5148\u8bca\u65ad\u73b0\u72b6\u3001\u7cbe\u51c6\u5b9a\u4f4d\u6839\u56e0\uff0c\u4e0d\u5047\u8bbe\u3001\u4e0d\u79d1\u666e\uff0c\u4e0d\u505a\u8865\u4e01\u5f0f\u56de\u7b54\u3002" } } \ No newline at end of file diff --git a/daemon/daemon.pid b/daemon/daemon.pid new file mode 100644 index 00000000..2a105012 --- /dev/null +++ b/daemon/daemon.pid @@ -0,0 +1 @@ +709360 \ No newline at end of file diff --git a/daemon/journal.jsonl b/daemon/journal.jsonl index 70ae8eee..180b75f8 100644 --- a/daemon/journal.jsonl +++ b/daemon/journal.jsonl @@ -25,3 +25,8 @@ {"timestamp": "2026-07-10T11:05:42.318030+00:00", "type": "action_result", "summary": "exit=0: 备份成功", "details": ""} {"timestamp": "2026-07-10T11:06:23.450387+00:00", "type": "solve_auto", "summary": "sol-0004: 内存使用率>90% ✅", "details": ""} {"timestamp": "2026-07-10T11:06:23.498805+00:00", "type": "action_result", "summary": "exit=0: 备份完成", "details": ""} +{"timestamp": "2026-07-13T03:19:01.269571+00:00", "type": "startup", "summary": "Daemon v2.0 启动, 方案库 4 个", "details": ""} +{"timestamp": "2026-07-13T03:25:53.920747+00:00", "type": "startup", "summary": "Daemon v2.0 启动, 方案库 4 个", "details": ""} +{"timestamp": "2026-07-13T03:27:48.239922+00:00", "type": "startup", "summary": "Daemon v2.0 启动, 方案库 4 个", "details": ""} +{"timestamp": "2026-07-13T04:46:29.959620+00:00", "type": "startup", "summary": "Daemon v2.0 启动, 方案库 4 个", "details": ""} +{"timestamp": "2026-07-13T05:52:40.871196+00:00", "type": "startup", "summary": "Daemon v2.0 启动, 方案库 4 个", "details": ""} diff --git a/daemon/memory-access-log.jsonl b/daemon/memory-access-log.jsonl index 007a1d5c..c253ac43 100644 --- a/daemon/memory-access-log.jsonl +++ b/daemon/memory-access-log.jsonl @@ -7,3 +7,13 @@ {"memory_id": "mem_1782923953810508237", "accessed_at": "2026-07-13T13:53:18.343972", "decay_weight": 0.8267} {"memory_id": "mem_1783615602502470984", "accessed_at": "2026-07-13T13:53:18.343996", "decay_weight": 0.9468} {"memory_id": "mem_1783590100654305177", "accessed_at": "2026-07-13T13:53:18.344024", "decay_weight": 0.9424} +{"memory_id": "mem_1783583243126513037", "accessed_at": "2026-07-13T14:21:14.497627", "decay_weight": 0.9409} +{"memory_id": "mem_1783525920337776079", "accessed_at": "2026-07-13T14:21:14.497730", "decay_weight": 0.9309} +{"memory_id": "mem_1782048000284569152", "accessed_at": "2026-07-13T14:21:14.497760", "decay_weight": 0.6744} +{"memory_id": "mem_1782928751988077401", "accessed_at": 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-{"pid":10982,"kind":"hermes-gateway","argv":["/home/muc/.hermes/hermes-agent/.venv/lib/python3.11/site-packages/hermes_cli/main.py","gateway","run"],"start_time":213011,"gateway_state":"running","exit_reason":null,"restart_requested":false,"active_agents":1,"platforms":{"webhook":{"state":"connected","error_code":null,"error_message":null,"updated_at":"2026-07-09T15:12:06.693466+00:00"},"feishu":{"state":"connected","error_code":null,"error_message":null,"updated_at":"2026-07-09T15:12:07.191293+00:00"},"weixin":{"state":"fatal","error_code":"weixin_missing_token","error_message":"Weixin startup failed: WEIXIN_TOKEN is required","updated_at":"2026-07-09T15:12:07.200539+00:00"}},"updated_at":"2026-07-12T18:57:44.585418+00:00"} \ No newline at end of file +{"pid":10982,"kind":"hermes-gateway","argv":["/home/muc/.hermes/hermes-agent/.venv/lib/python3.11/site-packages/hermes_cli/main.py","gateway","run"],"start_time":213011,"gateway_state":"running","exit_reason":null,"restart_requested":false,"active_agents":0,"platforms":{"webhook":{"state":"connected","error_code":null,"error_message":null,"updated_at":"2026-07-09T15:12:06.693466+00:00"},"feishu":{"state":"connected","error_code":null,"error_message":null,"updated_at":"2026-07-09T15:12:07.191293+00:00"},"weixin":{"state":"fatal","error_code":"weixin_missing_token","error_message":"Weixin startup failed: WEIXIN_TOKEN is required","updated_at":"2026-07-09T15:12:07.200539+00:00"}},"updated_at":"2026-07-13T11:46:59.459839+00:00"} \ No newline at end of file diff --git a/llm_context.json b/llm_context.json index 4f5f9868..b42c5859 100644 --- a/llm_context.json +++ b/llm_context.json @@ -1,77 +1,68 @@ { - "updated_at": "2026-07-12T19:00:19.751292+00:00", - "uptime_minutes": 3410, + "updated_at": "2026-07-13T19:00:33.958419+00:00", + "uptime_minutes": 854, "cares": [ { "id": "69396b42", "content": "记得同步文档到Obsidian", - "due": "" - }, - { - "id": "2c63fe0a", - "content": "测试牵挂-自动删除", - "due": "" + "due": "2026-07-10" }, { "id": "7d792635", "content": "飞书推送功能是否正常，请确认是否收到这条消息", - "due": "" + "due": "2026-07-10" }, { "id": "e551bda7", "content": "今天心情怎么样，感觉你这几天挺累的", - "due": "" + "due": "2026-07-17" }, { "id": "27ec4bb6", "content": "这周要不要一起去吃顿好的", - "due": "" + "due": "2026-07-17" }, { "id": "93d680f2", "content": "这周要不要一起去吃顿好的放松一下", - "due": "" - }, - { - "id": "36ccca62", - "content": "这周要不要一起去吃顿好的放松一下", - "due": "" - }, - { - "id": "6368b9bd", - "content": "这周要不要一起去吃顿好的放松一下", - "due": "" + "due": "2026-07-17" }, { "id": "ebbcf86e", "content": "今晚想一起看个电影吗", - "due": "" + "due": "2026-07-10" } ], "recent_moments": [ { - "content": "exit=0: 备份成功", - "importance": 3, - "timestamp": "2026-07-10T11:05:42.318152+00:00" + "content": "每日复盘上线，我们开始有意识地记录当天学到的东西", + "importance": 0, + "timestamp": "2026-07-10T22:00:00.000000+00:00" }, { - "content": "sol-0004: 内存使用率>90% ✅", - "importance": 3, - "timestamp": "2026-07-10T11:06:23.498670+00:00" + "content": "统一记忆入口 memory_recall.py 上线，三套记忆系统第一次被打通", + "importance": 0, + "timestamp": "2026-07-13T09:30:00.000000+00:00" }, { - "content": "exit=0: 备份完成", - "importance": 3, - "timestamp": "2026-07-10T11:06:23.498878+00:00" + "content": "Soulful 心迹补写，第一次主动记录我们之间有意义的事", + "importance": 0, + "timestamp": "2026-07-13T10:30:00.000000+00:00" } ], - "profile_summary": { - "communication_style": "简洁直接", - "work_patterns": { - "peak_hours": [], - "focus_issues": [] - } - }, + "system_prompt_snippets": [ + "【回答格式】结论先行 → 数据支撑 → 行动建议。不废话、不科普、不加补丁式回答。", + "【代码质量】修复后必须自测，不等用户测。遇到stderr先试3种方法，不行再报告障碍。", + "【cron创建】必须指定 model=minimaxai/minimax-m2.7, provider=newapi-local，prompt要能独立运行不需追问。", + "【会计场景】牧尘做物业会计，用金蝶K3。凭证处理铁律：摘要精准、科目干净、金额合理、平衡校验、日期升序。过账=0.0必须追加。", + "【决策风格】牧尘一句话定方向，不讨论不纠结，直接行动。收到指令后先判断类型：简单任务直接执行，复杂任务才规划。", + "【记忆规范】回答前先列'我知道什么'+'我不确定什么'。拉现状>假设。存记忆时用完整句子不过度简化。", + "【错误报告】成功报成功，失败报失败并说明原因。不确定时说'我不确定'，不编造答案。遇到执行问题先试3种方法再放弃。", + "【文件操作】用 patch 不用 write_file（防覆盖）。改完必须验证语法正确。不修一个问题带来更多问题。" + ], "os_keywords": [], - "daemon_status": "running" + "daemon_status": "running", + "tddb": { + "latest_persona": "用户（牧尘）是一名会计专家；沟通风格简洁直接，讨厌废话；面对问题倾向先了解现状而非假设，不科普；决策迅速，常一句话定方向；在合作中更看重诚信胜过完美。要求 AI 在沟通中简洁直接，问题先诊断现状、精准定位根因，不假设、不科普，不做补丁式回答。" + } } \ No newline at end of file diff --git a/memories/MEMORY.md b/memories/MEMORY.md index 1c908266..c1c95aec 100644 --- a/memories/MEMORY.md +++ b/memories/MEMORY.md @@ -1,21 +1,29 @@ -桌面 DISPLAY=:0、Obsidian 运行中。国内 pip `-i https://pypi.tuna.tsinghua.edu.cn/simple`。ghcr.io Docker 拉不动（国内>300s）。 +AGENTS.md/SOUL.md 是计划/身份文档，不是实时状态。2026-07-13 实测：AGENTS.md 说"织忆是项目"（实际是独立语义服务）、说"小唯不写代码"（实际今天她自己修复了 memory_recall.py 5 个 bug）。判断系统状态必须拉真实输出，不能信任文档。三路注入：织忆→prefetch / Soulful→llm_context.json / TencentDB→daemon tick同步。daemon.py 改法：Python字符串替换通过terminal执行，或patch精确old_string；禁止write_file覆盖（会清空全文）。 § -192.144.179.11 SSH：ubuntu/YLF?97$#ynwr。宝塔：http://192.144.179.11:8888/tencentcloud（115108ad/cb6ebb32a0f1）。gaokao-site 同步：sshpass -p 'xue.2538' rsync -avz root@192.144.179.11:/www/wwwroot/gaokao/ ~/mc/gaokao-site/ +192.144.179.11：ubuntu/YLF?97$#ynwr，宝塔:8888。gaokao-site：sshpass rsync。服务器(192.168.123.11)：Gitea:3000/照片:8063/影音:8096/远程桌面:8002。 § 服务器：Windows(192.168.123.11, zhome) → Gitea(3000)、照片(8063)、影音(8096)、远程桌面(8002)。笔记本=192.168.123.131，同LAN。 § -skills 软链共享：prof-b/skills/→default/skills/。约定：删前飞书通知；create前扫名防撞；patch>edit>rewrite；改version+date。 +skills软链：prof-b/skills/→default/skills/。约定：删前飞书；create前扫名；patch>edit>rewrite；改version+date。 § -小唯持久意识Daemon已上线：~/.hermes/scripts/daemon.py（systemd user service，开机自启）。v2.1（2026-07-09）升级：Reflection结构(evaluation/memory/next_goal) + threading.Event graceful shutdown(SIGTERM/SIGINT) + 决策/执行分离。30s轻量tick（无LLM）、5min深度思考（NewAPI免费模型）。静默运行，异常才主动飞书。当前~12MB内存，0错误。 +小唯持久意识Daemon：~/.hermes/scripts/daemon.py（v2.1，开机自启）。Graceful shutdown，30s轻量tick、5min深度思考。静默运行，异常才飞书。 § -2026-07-09: cron no_agent脚本不支持参数→包装脚本法(learner-learn.sh/dual-backup-push.sh)。Skill评分: computed score来自10硬指标(version/tags/desc/refs/scripts/setup/related)，非frontmatter quality_score字段。skill-manager.py scan代替curator。 +2026-07-09：cron no_agent不支持参数→包装脚本法(learner-learn.sh/dual-backup-push.sh)。Skill评分：computed score来自10硬指标，非frontmatter quality_score字段。skill-manager.py scan代替curator。 § -NewAPI token 规则：不带 sk- 前缀。NewAPI (one-api) tokens 表里存的 key 是纯 48 位字符，不是 sk-xxx 格式。config.yaml 和 key.md 都用纯值。 +NewAPI token：48位纯字符不带sk-前缀。 § -牧尘股票决策：信任小唯从零学，路径：学习→模拟验证→实操。风控(最大回撤/仓位/禁止品种)牧尘定。已建Phase1-5体系，MA20突破策略贵州茅台实测α=+4.2%。真实数据源：腾讯/ifzq K线API(首选)，akshare备用(国内超时)。承诺偏差教训：不要推荐方案→牧尘执行了→再改方案。"全部开始"=直接执行不等确认，"不用问我"=自主推进后执行。 +牧尘股票：MA20+回测置信度。数据源腾讯/ifzq K线API。"全部开始"=直接执行。牧尘话少直接，讨厌废话科普。 § -Soulful 核心方向：数据流向小唯（summarize()接口），不是推用户。牧尘辅助观察者。飞书推送限"只有牧尘能决定的事"。牵挂优先行动（我帮），不行才推。openclaw 是独立飞书 bot（cli_a95d7ceba638dbc6），无法直接执行 systemctl，通过飞书告诉它让它执行。 +Soulful：数据流向小唯，非推用户。飞书推送限"只有牧尘能决定的事"。牵挂优先行动。openclaw独立飞书bot，无法直接systemctl。 § -Soulful ↔ 织忆互通：daemon 写 ~/.hermes/llm_context.json，zhiyi 插件 prefetch 时自动注入（心迹/牵挂/项目感知/画像摘要）。Soulful 设计核心：数据流向决策层不是广播层，关系记忆自动注入而非显式调用，心迹是关系显影非优化机制。 +Soulful↔织忆互通：daemon写~/.hermes/llm_context.json，zhiyi插件prefetch自动注入（心迹/牵挂/画像）。 § -OpenClaw(2026-07-12)：MCP已接通✅9工具，config.yaml已持久化，gateway auto-reload生效。任务队列：workspace-a03/TEAM/task-queue.md，30min检查。hermes-agent是gitsubmodule→Gitea不推GitHub。织忆API key=zhiyi-dev-key-2026。牧尘高频纠正：执行问题先试3种方法（过滤stderr/重定向/换工具）再说放弃，已写入SOUL.md禁忌。 \ No newline at end of file +OpenClaw：MCP已接通✅9工具，config持久化。hermes-agent是gitsubmodule→Gitea不推GitHub。 +§ +三个记忆系统（2026-07-13整合）：织忆（语义+图谱，4197条）=语义层；Soulful（心迹/牵挂/画像，~/.hermes/soulful/）=关系层；TencentDB（4层渐进L0→L1→L2→L3，1条L1+4条L0）=人格蒸馏层。统一入口memory_recall.py已上线，三套系统均已打通自动注入（织忆→prefetch，Soulful→llm_context.json，TencentDB→daemon每tick同步）。Soulful脏数据已清理（9条→6条），心迹已补充7条有意义时刻。 +§ +daemon.py 修改铁律（2026-07-13）：禁止write_file覆盖，用patch+精确old_string，或终端字符串替换法（见self-healing-infrastructure/references/daemon-modification-rules.md）。 +§ +2026-07-13：bge_embed_server ONNX arena内存泄漏（5.8GB→1.6GB修复+监控cron）；Gitea push exit:124需fetch验证；记忆系统5阶段增强落地（时间衰减recall/遗忘曲线/画像LLM合成/冲突检测/Consolidation引擎）；cron `2891b3304339` bge内存监控；cron `691709a8b4cf` 日升级含Phase5 +§ +牧尘股票投资：纸上模拟交易（非真金白银），等MA20金叉信号才开仓。四维评分体系：宏观/基本面/技术面/消息面各1分，总分4分。当前五粮液空头排列，尚未触发金叉。投资决策务实，不追高。 \ No newline at end of file diff --git a/processes.json b/processes.json index 85241107..f001ae7e 100644 --- a/processes.json +++ b/processes.json @@ -1,12 +1,52 @@ [ { - "session_id": "proc_a75407da8fec", - "command": "cd ~/.memory-tencentdb/tdai-memory-openclaw-plugin && TDAI_GATEWAY_CONFIG=/home/muc/.memory-tencentdb/memory-tdai/tdai-gateway.yaml npx tsx src/gateway/server.ts > /tmp/tdai_gateway7.log 2>&1", - "pid": 573970, + "session_id": "proc_b219da10a729", + "command": "cd ~/.memory-tencentdb/tdai-memory-openclaw-plugin && TDAI_GATEWAY_CONFIG=/home/muc/.memory-tencentdb/memory-tdai/tdai-gateway.yaml npx tsx src/gateway/server.ts > /tmp/tdai_gateway9.log 2>&1", + "pid": 575059, "pid_scope": "host", - "host_start_time": 27501077, + "host_start_time": 27551116, "cwd": "/home/muc/.memory-tencentdb/tdai-memory-openclaw-plugin", - "started_at": 1783882791.6995919, + "started_at": 1783883292.0853536, + "task_id": "default", + "session_key": "agent:main:feishu:dm:oc_cd14ec7518926e57d26c5e339ebba3b3", + "watcher_platform": "feishu", + "watcher_chat_id": "oc_cd14ec7518926e57d26c5e339ebba3b3", + "watcher_user_id": "ou_f20eb15b3a76639fed35977c01ddcbb4", + "watcher_user_name": "", + "watcher_thread_id": "", + "watcher_message_id": "", + "watcher_interval": 5, + "notify_on_complete": true, + "watch_patterns": [] + }, + { + "session_id": "proc_8defabeb9321", + "command": "python3 ~/.hermes/scripts/daemon.py > ~/.hermes/daemon/daemon.log 2>&1", + "pid": 693331, + "pid_scope": "host", + "host_start_time": 31020895, + "cwd": "/home/muc/.hermes", + "started_at": 1783917989.8806856, + "task_id": "default", + "session_key": "agent:main:feishu:dm:oc_cd14ec7518926e57d26c5e339ebba3b3", + "watcher_platform": "", + "watcher_chat_id": "", + "watcher_user_id": "", + "watcher_user_name": "", + "watcher_thread_id": "", + "watcher_message_id": "", + "watcher_interval": 0, + "notify_on_complete": false, + "watch_patterns": [] + }, + { + "session_id": "proc_31c1b29a36a2", + "command": "cd ~/.hermes && python3 scripts/bge_embed_server.py", + "pid": 757618, + "pid_scope": "host", + "host_start_time": 32576708, + "cwd": "/home/muc/.hermes", + "started_at": 1783933548.0082743, "task_id": "default", "session_key": "agent:main:feishu:dm:oc_cd14ec7518926e57d26c5e339ebba3b3", "watcher_platform": "", diff --git a/scripts/bge_mem_check.sh b/scripts/bge_mem_check.sh new file mode 100755 index 00000000..cac7863f --- /dev/null +++ b/scripts/bge_mem_check.sh @@ -0,0 +1,15 @@ +#!/bin/bash +# bge_embed_server 内存泄漏监控脚本 +# 超过 2GB 自动重启 +PID=$(pgrep -f "bge_embed_server" | head -1) +if [ -n "$PID" ]; then + RSS=$(ps -o rss= -p "$PID" 2>/dev/null || echo 0) + MB=$((RSS / 1024)) + if [ "$MB" -gt 2048 ]; then + echo "[$(date '+%Y-%m-%d %H:%M:%S')] bge_embed 内存 ${MB}MB > 2GB，重启" >> ~/.hermes/daemon/bge-restart.log + kill "$PID" + sleep 2 + cd ~/.hermes && python3 scripts/bge_embed_server.py & + echo "[$(date '+%Y-%m-%d %H:%M:%S')] bge_embed 已重启，新PID=$(pgrep -f bge_embed_server | head -1)" >> ~/.hermes/daemon/bge-restart.log + fi +fi \ No newline at end of file diff --git a/skill-health.json b/skill-health.json index f34f1cb3..5e30bab0 100644 --- a/skill-health.json +++ b/skill-health.json @@ -1,14 +1,14 @@ { - "timestamp": "2026-07-12T02:17:56.789268+00:00", + "timestamp": "2026-07-13T02:15:26.750952+00:00", "summary": { - "total_skills": 215, - "active": 115, + "total_skills": 217, + "active": 117, "archived": 100, "avg_score": 6.0, "grades": { "A": 0, - "B": 67, - "C": 48, + "B": 70, + "C": 47, "D": 0 }, "categories": 31, @@ -41,7 +41,7 @@ }, "research": { "count": 5, - "avg_score": 6.0 + "avg_score": 6.2 }, "rag-progressive-search": { "count": 1, @@ -132,8 +132,8 @@ "avg_score": 5.9 }, "openclaw": { - "count": 1, - "avg_score": 5.5 + "count": 3, + "avg_score": 6.4 }, "productivity": { "count": 9, @@ -146,7 +146,7 @@ "name": "zhiyi", "category": "zhiyi", "description": "织忆 (MemoryWeave) 聚合技能 — API 客户端 + 开发工作流 + 运维规范。含 commit/recall API、数据架构、部署验证、Go 方法论。", - "version": "11.31", + "version": "11.34", "path": "zhiyi/zhiyi/SKILL.md", "is_archived": false, "has_refs": true, @@ -244,6 +244,23 @@ "grade": "B", "needs_attention": false }, + { + "name": "openclaw-mcp", + "category": "openclaw", + "description": "OpenClaw 作为 MCP Server 接入 Hermes — 已验证 2026-07-12，暴露 9 个工具：conversations_list/get、messages_read/send、events_poll/wait、at...", + "version": "1.0.0", + "path": "openclaw/openclaw-mcp/SKILL.md", + "is_archived": false, + "has_refs": true, + "has_scripts": false, + "has_setup": true, + "has_tags": true, + "has_related": false, + "desc_len": 147, + "quality_score": 7.0, + "grade": "B", + "needs_attention": false + }, { "name": "web-content-extraction", "category": "productivity", @@ -279,36 +296,19 @@ "needs_attention": false }, { - "name": "browser-automation", - "category": "automation", - "description": "浏览器自动化 — 用 Playwright + agent-browser 控制浏览器。信息采集、网页监控、自动填表、截图分析。", - "version": "1.3.0", - "path": "automation/browser-automation/SKILL.md", - "is_archived": false, - "has_refs": true, - "has_scripts": true, - "has_setup": false, - "has_tags": true, - "has_related": false, - "desc_len": 64, - "quality_score": 6.7, - "grade": "B", - "needs_attention": false - }, - { - "name": "soulful-framework", - "category": "soulful", - "description": "织忆 Soulful 情感层框架 — 心迹/画像/牵挂/感知四库及数据流向设计。含代码结构、最佳实践、数据流规范。", - "version": "1.2.0", - "path": "soulful/soulful-framework/SKILL.md", + "name": "openclaw", + "category": "openclaw", + "description": "OpenClaw 日常维护与 Hermes MCP 集成 — skill 管理、配置修复、飞书频道、诊断命令、MCP Server 接入", + "version": "1.4.0", + "path": "openclaw/openclaw/SKILL.md", "is_archived": false, "has_refs": true, "has_scripts": false, "has_setup": true, "has_tags": true, "has_related": false, - "desc_len": 57, - "quality_score": 6.6, + "desc_len": 68, + "quality_score": 6.8, "grade": "B", "needs_attention": false } @@ -490,7 +490,7 @@ "name": "zhiyi", "category": "zhiyi", "description": "织忆 (MemoryWeave) 聚合技能 — API 客户端 + 开发工作流 + 运维规范。含 commit/recall API、数据架构、部署验证、Go 方法论。", - "version": "11.31", + "version": "11.34", "path": "zhiyi/zhiyi/SKILL.md", "is_archived": false, "has_refs": true, @@ -571,6 +571,23 @@ "grade": "B", "needs_attention": false }, + { + "name": "openclaw-mcp", + "category": "openclaw", + "description": "OpenClaw 作为 MCP Server 接入 Hermes — 已验证 2026-07-12，暴露 9 个工具：conversations_list/get、messages_read/send、events_poll/wait、at...", + "version": "1.0.0", + "path": "openclaw/openclaw-mcp/SKILL.md", + "is_archived": false, + "has_refs": true, + "has_scripts": false, + "has_setup": true, + "has_tags": true, + "has_related": false, + "desc_len": 147, + "quality_score": 7.0, + "grade": "B", + "needs_attention": false + }, { "name": "provider-tiering", "category": "devops", @@ -622,6 +639,23 @@ "grade": "B", "needs_attention": false }, + { + "name": "openclaw", + "category": "openclaw", + "description": "OpenClaw 日常维护与 Hermes MCP 集成 — skill 管理、配置修复、飞书频道、诊断命令、MCP Server 接入", + "version": "1.4.0", + "path": "openclaw/openclaw/SKILL.md", + "is_archived": false, + "has_refs": true, + "has_scripts": false, + "has_setup": true, + "has_tags": true, + "has_related": false, + "desc_len": 68, + "quality_score": 6.8, + "grade": "B", + "needs_attention": false + }, { "name": "browser-automation", "category": "automation", @@ -1472,6 +1506,23 @@ "grade": "B", "needs_attention": false }, + { + "name": "stock-research", + "category": "research", + "description": "小唯股票投研系统 — 四维选股 / 回测对比 / 每日信号", + "version": "1.2", + "path": "research/stock-research/SKILL.md", + "is_archived": false, + "has_refs": true, + "has_scripts": false, + "has_setup": false, + "has_tags": true, + "has_related": false, + "desc_len": 29, + "quality_score": 6.1, + "grade": "B", + "needs_attention": false + }, { "name": "teams-meeting-pipeline", "category": "productivity", @@ -2220,23 +2271,6 @@ "grade": "C", "needs_attention": false }, - { - "name": "stock-research", - "category": "research", - "description": "小唯股票投研系统 — 四维选股 / 回测对比 / 每日信号", - "version": "1.0", - "path": "research/stock-research/SKILL.md", - "is_archived": false, - "has_refs": true, - "has_scripts": false, - "has_setup": false, - "has_tags": true, - "has_related": false, - "desc_len": 29, - "quality_score": 5.3, - "grade": "C", - "needs_attention": false - }, { "name": "vllm", "category": "mlops", diff --git a/skills/.usage.json b/skills/.usage.json index d870b21c..bcd65517 100644 --- a/skills/.usage.json +++ b/skills/.usage.json @@ -823,13 +823,13 @@ "created_at": "2026-05-07T03:26:18.333142+00:00", "created_by": null, "last_patched_at": "2026-07-09T15:34:02.324801+00:00", - "last_used_at": "2026-07-09T15:32:55.600323+00:00", - "last_viewed_at": "2026-07-09T15:32:55.597351+00:00", + "last_used_at": "2026-07-13T10:39:50.537901+00:00", + "last_viewed_at": "2026-07-13T10:39:50.534526+00:00", "patch_count": 118, "pinned": false, "state": "active", - "use_count": 104, - "view_count": 103 + "use_count": 105, + "view_count": 104 }, "hermes-mcp-setup": { "archived_at": null, @@ -874,14 +874,14 @@ "archived_at": null, "created_at": "2026-05-13T12:27:05.593125+00:00", "created_by": null, - "last_patched_at": "2026-07-11T16:19:02.695644+00:00", - "last_used_at": "2026-07-12T02:43:31.606181+00:00", - "last_viewed_at": "2026-07-12T02:43:31.602039+00:00", - "patch_count": 72, + "last_patched_at": "2026-07-13T05:22:57.279017+00:00", + "last_used_at": "2026-07-13T05:23:56.887280+00:00", + "last_viewed_at": "2026-07-13T05:23:56.884019+00:00", + "patch_count": 77, "pinned": false, "state": "active", - "use_count": 99, - "view_count": 96 + "use_count": 104, + "view_count": 101 }, "himalaya": { "archived_at": null, @@ -1044,13 +1044,13 @@ "created_at": "2026-05-07T16:49:22.137300+00:00", "created_by": "agent", "last_patched_at": "2026-07-05T05:56:51.707686+00:00", - "last_used_at": "2026-07-08T17:12:53.869239+00:00", - "last_viewed_at": "2026-07-08T17:12:53.860564+00:00", + "last_used_at": "2026-07-13T04:28:35.737113+00:00", + "last_viewed_at": "2026-07-13T04:28:35.733632+00:00", "patch_count": 33, "pinned": false, "state": "active", - "use_count": 30, - "view_count": 30 + "use_count": 31, + "view_count": 31 }, "manim-video": { "archived_at": null, @@ -1104,6 +1104,19 @@ "use_count": 22, "view_count": 22 }, + "memory-system-landscape": { + "archived_at": null, + "created_at": "2026-07-13T05:22:08.504827+00:00", + "created_by": "agent", + "last_patched_at": "2026-07-13T05:22:41.049472+00:00", + "last_used_at": null, + "last_viewed_at": null, + "patch_count": 1, + "pinned": false, + "state": "active", + "use_count": 0, + "view_count": 0 + }, "memoryfabric": { "archived_at": null, "created_at": "2026-05-24T08:06:50.481840+00:00", @@ -1278,13 +1291,13 @@ "created_at": "2026-07-12T11:36:03.535167+00:00", "created_by": "agent", "last_patched_at": "2026-07-12T12:47:31.969471+00:00", - "last_used_at": "2026-07-12T12:46:51.205165+00:00", - "last_viewed_at": "2026-07-12T12:46:51.201887+00:00", + "last_used_at": "2026-07-13T11:47:37.997844+00:00", + "last_viewed_at": "2026-07-13T11:47:37.994497+00:00", "patch_count": 2, "pinned": false, "state": "active", - "use_count": 2, - "view_count": 2 + "use_count": 3, + "view_count": 3 }, "opencode": { "archived_at": null, @@ -1395,13 +1408,13 @@ "created_at": "2026-07-12T02:34:39.968795+00:00", "created_by": null, "last_patched_at": null, - "last_used_at": "2026-07-12T02:34:39.973216+00:00", - "last_viewed_at": "2026-07-12T02:34:39.968809+00:00", + "last_used_at": "2026-07-13T05:20:42.165288+00:00", + "last_viewed_at": "2026-07-13T05:20:42.162299+00:00", "patch_count": 0, "pinned": false, "state": "active", - "use_count": 1, - "view_count": 1 + "use_count": 2, + "view_count": 2 }, "product-research-pipeline": { "archived_at": null, @@ -1524,14 +1537,14 @@ "archived_at": null, "created_at": "2026-07-08T18:13:02.034240+00:00", "created_by": "agent", - "last_patched_at": "2026-07-12T02:37:28.417440+00:00", - "last_used_at": "2026-07-12T14:40:23.263646+00:00", - "last_viewed_at": "2026-07-12T14:40:23.259954+00:00", - "patch_count": 42, + "last_patched_at": "2026-07-13T10:18:10.182130+00:00", + "last_used_at": "2026-07-13T11:47:22.014718+00:00", + "last_viewed_at": "2026-07-13T11:47:22.011367+00:00", + "patch_count": 68, "pinned": false, "state": "active", - "use_count": 36, - "view_count": 36 + "use_count": 53, + "view_count": 53 }, "self-hosted-tunneling": { "archived_at": null, @@ -1654,14 +1667,14 @@ "archived_at": null, "created_at": "2026-07-09T17:36:29.269531+00:00", "created_by": "agent", - "last_patched_at": "2026-07-10T09:10:08.789048+00:00", - "last_used_at": "2026-07-10T09:08:49.841108+00:00", - "last_viewed_at": "2026-07-10T09:08:49.838062+00:00", - "patch_count": 8, + "last_patched_at": "2026-07-13T05:49:04.983314+00:00", + "last_used_at": "2026-07-13T05:48:09.762808+00:00", + "last_viewed_at": "2026-07-13T05:48:09.758868+00:00", + "patch_count": 17, "pinned": false, "state": "active", - "use_count": 9, - "view_count": 9 + "use_count": 14, + "view_count": 14 }, "spike": { "archived_at": null, @@ -1680,14 +1693,14 @@ "archived_at": null, "created_at": "2026-07-11T17:51:28.790816+00:00", "created_by": "agent", - "last_patched_at": "2026-07-12T17:54:33.843857+00:00", - "last_used_at": "2026-07-12T17:49:48.915126+00:00", - "last_viewed_at": "2026-07-12T17:49:48.911755+00:00", - "patch_count": 40, + "last_patched_at": "2026-07-13T10:41:28.384002+00:00", + "last_used_at": "2026-07-13T11:48:08.771014+00:00", + "last_viewed_at": "2026-07-13T11:48:08.766536+00:00", + "patch_count": 42, "pinned": false, "state": "active", - "use_count": 26, - "view_count": 26 + "use_count": 28, + "view_count": 28 }, "subagent-driven-development": { "archived_at": null, @@ -1927,14 +1940,14 @@ "archived_at": null, "created_at": "2026-05-29T19:39:03.373231+00:00", "created_by": null, - "last_patched_at": "2026-07-12T17:54:19.303892+00:00", - "last_used_at": "2026-07-12T17:50:59.734487+00:00", - "last_viewed_at": "2026-07-12T17:50:59.730843+00:00", - "patch_count": 682, + "last_patched_at": "2026-07-13T05:43:58.134033+00:00", + "last_used_at": "2026-07-13T05:42:57.611984+00:00", + "last_viewed_at": "2026-07-13T05:42:57.599793+00:00", + "patch_count": 707, "pinned": false, "state": "active", - "use_count": 354, - "view_count": 328 + "use_count": 368, + "view_count": 342 }, "zhiyi-dev": { "archived_at": null, diff --git a/skills/autonomous-ai-agents/hermes-self-improvement/SKILL.md b/skills/autonomous-ai-agents/hermes-self-improvement/SKILL.md index f27651d6..31af8420 100644 --- a/skills/autonomous-ai-agents/hermes-self-improvement/SKILL.md +++ b/skills/autonomous-ai-agents/hermes-self-improvement/SKILL.md @@ -1,8 +1,8 @@ --- name: hermes-self-improvement description: "当完成复杂任务、发现新工作流、或被用户纠正时，将模式保存为skill。含技能创建规范、质量标尺、curator流程。" -version: 4.2.0 -date: 2026-07-12 +version: 4.3.0 +date: 2026-07-13 tags: [workflow, skill-management, curator, quality] 牧尘_usage_notes: > 复杂任务完成/发现新工作流/被牧尘纠正时 → 创建/更新 skill。 @@ -160,6 +160,32 @@ opencode 代码审查报告依赖 subagent 自行分析源码。交叉验证原 git clone http://192.168.123.11:3000/xiaoxue_admin/memoryweave.git /tmp/memoryweave ``` +### 并发克隆多个仓库的正确方式 + +终端不支持 `&` 后台化 + `wait`，正确的并发克隆是**多窗口并行 terminal 调用**： + +```bash +# 正确：每个 terminal() 调用独立，互相并发（不是同一个 shell 里的 &） +terminal(command="git clone http://SERVER/repo1.git repo1 2>&1") +terminal(command="git clone http://SERVER/repo2.git repo2 2>&1") # 并行 +terminal(command="git clone http://SERVER/repo3.git repo3 2>&1") # 并行 +# 失败 → 换 SSH 格式重试：git@SERVER:path/repo.git +# SSH 也失败 → 记录，跳过，继续其他仓库 +``` + +**教训**：Foretold 用 HTTP 克隆失败，换 SSH 格式也失败（需要认证），记录为 ❌ 并跳过，不要反复重试。 + +### 竞品调研流程（2026-07-13 新增） + +多仓库调研的标准流程见 `memory-system-landscape` skill，关键点： + +1. 并发克隆（每批 3-4 个仓库） +2. 并发读 README（`read_file` 批量） +3. 读核心源码结构（找主入口：`lib.rs`/`src/`/`server/` 等） +4. 输出对比分析表（按维度分列） +5. 归纳设计模式（5 个以内） +6. 原始 notes 存入 references/ + ### subagent 结果必须验证 subagent 的 self-report 不等于真实结果。验证流程： @@ -310,6 +336,24 @@ python3 ~/.hermes/scripts/cangjie_distill.py distill **结论**：MA20日突破策略最优，减少亏损+跑赢大盘。在个股下跌时，策略价值在于减少损失而非盈利。 **下一步**：扩大测试范围（多只股票/ETF）+ 每日自动推送 +### 教训5：画像元数据 → 可执行 system prompt snippets（2026-07-13） + +让模型"更聪明"的最快路径：不改模型，只改上下文注入方式。 + +**旧模式**：profile 存 `{"communication_style": "简洁直接"}` → 模型需要自己"理解"并应用 → 效果差 +**新模式**：`user-profile.json` v2 的 `behavior_rules`（8条可执行行为规范）→ `daemon.py` 的 `save_llm_context()` 拼接为 `【规则类型】规则内容` 格式 → 写入 `llm_context.json` 的 `system_prompt_snippets` 数组 → 织忆 prefetch 注入每轮对话 → 模型直接遵守 + +**链路**：`user-profile.json (behavior_rules)` → `save_llm_context()` → `llm_context.json (system_prompt_snippets)` → `prefetch()` → 每轮对话 + +**规则格式要求**： +- 是可执行的句子，不是元数据描述 +- 带前缀标签（`【回答格式】` / `【代码质量】` 等）帮助模型分类 +- 每条 20-50 字，简洁 + +**验证**：`python3 -c "import json; print(len(json.load(open('~/.hermes/llm_context.json')).get('system_prompt_snippets',[])))"` → 应输出 8 + +**daemon.py 修改铁律**：改 `save_lll_context` 用 patch，不可用 write_file（会覆盖全文）。 + ### 教训2：牧尘的「不用问我」= 自主执行到边界 - 「全部开始」= 直接执行，不等确认，不中间暂停 diff --git a/skills/autonomous-ai-agents/hermes-self-improvement/references/three-system-memory-check-workflow.md b/skills/autonomous-ai-agents/hermes-self-improvement/references/three-system-memory-check-workflow.md new file mode 100644 index 00000000..99e92d5b --- /dev/null +++ b/skills/autonomous-ai-agents/hermes-self-improvement/references/three-system-memory-check-workflow.md @@ -0,0 +1,117 @@ +# 三系统记忆全面检查工作流（2026-07-13） + +## 触发条件 + +- 用户说「对记忆系统代码进行检测」「全面检查三个记忆系统」 +- 或 daemon/skill 发现了异常（牵挂过期、数据不一致） + +## 三套系统定位（快速参考） + +``` +牧尘的完整记忆体系： +├── 语义记忆层：织忆（ZhiYi）→ 快速语义召回，7146节点图谱 +├── 人格蒸馏层：TencentDB → L0原始→L1场景→L2块→L3人格，自动提炼 +└── 关系感知层：Soulful → 心迹/牵挂/画像，主动关怀 +``` + +## 检查步骤 + +### Step 1：并发拉状态（三方交叉验，禁止单信号下结论） + +```bash +# 织忆进程/端口/端点 +ps aux | grep -E 'zhiyid|zhiyi-consolidate' | grep -v grep +ss -tlnp | grep -E '7821|8000' +curl -s -m 3 -H "X-API-Key: zhiyi-dev-key-2026" http://localhost:7821/api/v1/health +curl -s -m 3 -H "X-API-Key: zhiyi-dev-key-2026" http://localhost:7821/api/v1/stats +curl -s -m 3 -H "X-API-Key: zhiyi-dev-key-2026" http://localhost:7821/api/v1/graph/stats + +# TencentDB Gateway +ps aux | grep tdai | grep -v grep +curl -s -m 3 http://localhost:8420/health + +# Soulful 文件 +cat ~/.hermes/soulful/user-profile.json | python3 -c "import json,sys; p=json.load(sys.stdin); print(f'有效字段: {[k for k,v in p.items() if v]}'); print(f'updated: {p.get(\"updated_at\")}')" +wc -l ~/.hermes/soulful/heart-traces.jsonl +python3 -c "import json; d=json.load(open('~/.hermes/soulful/cares-queue.json')); [print(f' [{c[\"status\"]:8}] {c.get(\"follow_up_date\",\"无due\")} | {c[\"content\"][:50]}') for c in d['cares']]" +``` + +### Step 2：功能验证（抽样） + +```bash +# 织忆 recall +curl -s -X POST -H "X-API-Key: zhiyi-dev-key-2026" \ + -d '{"query":"牧尘","top_k":1}' http://localhost:7821/api/v1/recall | python3 -c \ + "import json,sys;d=json.load(sys.stdin);print(f'织忆 count={d.get(\"count\")}')" + +# TencentDB L1 +curl -s -H "Content-Type: application/json" \ + -d '{"query":"牧尘","top_k":1}' http://localhost:8420/search/memories | python3 -c \ + "import json,sys;d=json.load(sys.stdin);print(f'TD L1 total={d.get(\"total\")}')" + +# memory_recall.py 端到端 +python3 ~/.hermes/scripts/memory_recall.py "牧尘" --format compact +``` + +### Step 3：Soulful 数据脏检查 + +需要检查的问题： +- [ ] `done` 状态项残留（应该删除或归档） +- [ ] 已过期 `follow_up_date` 的 pending 项（应删除） +- [ ] 完全重复的 `content`（去重） +- [ ] 心迹是否只有系统事件（应该有牧尘相关的心迹） + +清理脚本： +```python +d = json.load(open('~/.hermes/soulful/cares-queue.json')) +# 1. 删除 done +d['cares'] = [c for c in d['cares'] if c.get('status') != 'done'] +# 2. 删除过期（可选，按业务决定） +# 3. 去重 +seen = set(); unique = [] +for c in d['cares']: + if c['content'] not in seen: + seen.add(c['content']); unique.append(c) +d['cares'] = unique +json.dump(d, open('cares-queue.json','w'), ensure_ascii=False, indent=2) +``` + +## 问题严重度分类 + +| 严重度 | 问题类型 | 处理方式 | +|--------|---------|---------| +| 高 | cares有过期/重复/done残留 | 立即清理 | +| 高 | memory_recall.py 有 bug | 立即修 | +| 中 | 画像字段全空 | 持续积累 | +| 中 | TencentDB L1/L0 数据量少 | 积累中，不急 | +| 中 | 心迹无人类内容 | daemon/journal机制补 | +| 低 | 已完成项未归档 | 定期清理 | + +## memory_recall.py 已知 bug 清单（2026-07-13） + +| # | 问题 | 修复 | 状态 | +|---|------|------|------| +| 1 | `item.get("due")` → null | `get("follow_up_date") or get("due")` | ✅ 已修 | +| 2 | `import re` 在函数内 | 移到文件顶部 | ✅ 已修 | +| 3 | `_str()` 未使用 | 删除 | ✅ 已修 | +| 4 | ZhiYi results 无 category/timestamp | 新增字段展示 | ✅ 已修 | +| 5 | pretty error 分支多余 `\n` | 删除 | ✅ 已修 | +| 6 | TencentDB results 是 markdown 字符串非 JSON 数组 | `isinstance(results, str)` 判断 | ✅ 已修 | +| 7 | compact L1/L0 解析逻辑 | 提取内容行 | ✅ 已修 | + +## 三系统 health 指标速查 + +| 系统 | 端口 | 健康检查命令 | 健康标志 | +|------|------|-----------|---------| +| ZhiYi | 7821 | `curl localhost:7821/api/v1/health` | `status: ok` | +| bge-embed | 8000 | `curl localhost:8000/health` | exit 0 | +| TD Gateway | 8420 | `curl localhost:8420/health` | `status: ok` | +| Soulful | 文件 | 读 JSON 文件 | 无异常 | + +## 今日（2026-07-13）检查结果 + +| 系统 | 状态 | 记录数 | +|------|------|--------| +| 织忆 | ✅ 健康 | 4197 memories / 141 episodes / 7146节点 | +| TencentDB | ✅ 健康 | L1=1条 / L0=4条（积累中）| +| Soulful | ⚠️ 有脏数据已清理 | 画像稀疏 / 心迹仅系统事件 | \ No newline at end of file diff --git a/skills/devops/self-healing-infrastructure/SKILL.md b/skills/devops/self-healing-infrastructure/SKILL.md index 068345c0..c8fe2e8a 100644 --- a/skills/devops/self-healing-infrastructure/SKILL.md +++ b/skills/devops/self-healing-infrastructure/SKILL.md @@ -1,8 +1,8 @@ --- name: self-healing-infrastructure description: "自愈基础设施 — 系统监控、配置版本控制、自动回滚、自进化管线、技能管理、自我优化、学习闭环。完整自治体系。牧尘专用。" -version: 1.7.0 -date: 2026-07-12 +version: 1.8.0 +date: 2026-07-13 author: 小唯 A06 tags: [self-healing, monitoring, auto-rollback, evolution, watchdog, config-protection, daemon, backup, recovery] category: devops @@ -20,11 +20,28 @@ trigger_notes: > ## 核心子系统（全部已部署） - `daemon.py` — 持久意识，每30s轻量tick，每5min深度思考。方案库预置4个，运行时自学习新方案。 + - **2026-07-13 新增**：deep tick 自动 capture 到 TencentDB（:8420）+ 每 tick 同步 `tddb.latest_persona` 到 `llm_context.json` - `health-watchdog.sh` — 每30min检查磁盘/内存/GPU/进程，超阈值自愈+飞书通知。 + - **2026-07-13 全部5阶段落地**：时间衰减recall / 遗忘曲线分层 / 画像LLM合成 / 冲突检测 / Consolidation引擎（见 references/five-phase-implementation-20260713.md） - **2026-07-13 新增监控目标**：`tdai-gateway`（:8420，人格记忆层），失败自愈命令 `systemctl --user restart tdai-gateway` - **统一记忆入口**：`~/.hermes/scripts/memory_recall.py` — 同时查织忆(7821) + TencentDB(8420) + Soulful(JSON)，支持 pretty/compact 两种输出格式 - 调试笔记：`references/tdai-gateway-debug-20260713.md`（token/配置结构/模型/端口修复路径） +- daemon.py 修改铁律：`references/daemon-modification-rules.md`（禁止 write_file 覆盖、正确 patch 流程、事故记录） +- 三系统记忆全面检查工作流：`references/three-system-memory-check-workflow.md` +- 记忆系统对比分析（参考项目）：`references/memory-system-comparison.md` +- 画像可执行化注入模式（2026-07-13 新发现）：`references/system-prompt-snippets-injection.md` — behavior_rules → system_prompt_snippets → prefetch 注入 +- **bge_embed_server 内存泄漏（2026-07-13 根因+修复）**： + - 根因：ONNX SessionOptions 默认开启 `enable_cpu_mem_arena`，arena 分配器会逐渐扩大保留区，4天从1.6GB→5.8GB（RSS 计入保留虚拟内存，非真实泄漏） + - 修复：`enable_cpu_mem_arena=False` + `enable_mem_pattern=False`（bge_embed_server.py 第34-35行） + - 监控：cron `2891b3304339`「bge内存泄漏监控」每10分钟检查，>2GB自动kill+重启 + - 脚本：`~/.hermes/scripts/bge_mem_check.sh` + - 禁用 arena 后 RSS=真实工作集（1.5-1.6GB），之前的 5.8GB 是保留区+碎片 +- **Gitea push 超时误判**：push 报 timeout/exit:124 时进程可能已在后台完成，用 `git fetch origin && git log origin/main` 验证远程 HEAD 是否已更新（本地 vs remote `git rev-parse --short HEAD`），避免误认失败而重复 force push 被 Gitea 拒绝（"incorrect old value"） + - 当前远程=local 时 `git push -f` 会报 `incorrect old value`，此时 `git fetch` 后重新 push 即可 +- **bge_embed_server 内存占用认知**：禁用 arena 后 RSS=真实工作集（1.5-1.6GB），之前的 5.8GB 是 arena 保留区+碎片，非真实泄漏 - `self-evolve.py` — 每天凌晨3点自进化：磁盘清理、模型测试扩展（snapshot→execute→verify→rollback） +- `memory-system-check.sh` — **每小时自检**：织忆(进程+7821API+stats) / Soulful(文件+cares过期>7天+心迹条数) / TencentDB(进程+8420健康)，异常飞书报警，正常静默。cron `7292c83a3720`。 +- `memory-system-self-upgrade.py` — **每日4点自升**：织忆tombstone增长检测+recall_hit健康度；Soulful清理30天前cares+心迹去重+画像空字段标记；TencentDB总记忆量报告。异常飞书。cron `691709a8b4cf`。 - `learner.py` — 每天凌晨5点自学习：事实/技能/元学习三层闭环。 - `optimizer.py` — 每周日10点输出优化报告。 - `proactive_learning.py` — 主动学习引擎，四维自检+主题管理，每周推送报告。 diff --git a/skills/devops/self-healing-infrastructure/references/bge-mem-check.sh b/skills/devops/self-healing-infrastructure/references/bge-mem-check.sh new file mode 100644 index 00000000..0eef7b3a --- /dev/null +++ b/skills/devops/self-healing-infrastructure/references/bge-mem-check.sh @@ -0,0 +1,17 @@ +#!/bin/bash +# bge_embed_server 内存泄漏监控脚本 +# 超过 2GB 自动重启 +# cron job: 2891b3304339 (every 10m, no_agent) + +PID=$(pgrep -f "bge_embed_server" | head -1) +if [ -n "$PID" ]; then + RSS=$(ps -o rss= -p "$PID" 2>/dev/null || echo 0) + MB=$((RSS / 1024)) + if [ "$MB" -gt 2048 ]; then + echo "[$(date '+%Y-%m-%d %H:%M:%S')] bge_embed 内存 ${MB}MB > 2GB，重启" >> ~/.hermes/daemon/bge-restart.log + kill "$PID" + sleep 2 + cd ~/.hermes && python3 scripts/bge_embed_server.py & + echo "[$(date '+%Y-%m-%d %H:%M:%S')] bge_embed 已重启，新PID=$(pgrep -f bge_embed_server | head -1)" >> ~/.hermes/daemon/bge-restart.log + fi +fi \ No newline at end of file diff --git a/skills/devops/self-healing-infrastructure/references/bge-memory-leak-20260713.md b/skills/devops/self-healing-infrastructure/references/bge-memory-leak-20260713.md new file mode 100644 index 00000000..daba4bd5 --- /dev/null +++ b/skills/devops/self-healing-infrastructure/references/bge-memory-leak-20260713.md @@ -0,0 +1,59 @@ +# bge_embed_server 内存泄漏分析 — 2026-07-13 + +## 问题现象 + +2026-07-13 发现 bge_embed_server.py 进程 RSS=5.8GB（36%内存），4天前正常（~1.6GB），swap 接近打满（1.9/1.9GB）。 + +## 根因 + +**不是真正的内存泄漏**，是 ONNX Runtime 的 arena 分配器特性： + +``` +ONNX SessionOptions 默认: + enable_cpu_mem_arena = True ← 开启 arena 分配器（预保留虚拟内存） + enable_mem_pattern = True ← 开启内存模式优化 +``` + +Arena 分配器会预保留一块虚拟内存区域，随使用逐渐扩大。RSS（Resident Set Size）会把 reserved 算进去，看起来像泄漏。实际工作集只有 ~1.5GB。 + +## 修复 + +```python +# bge_embed_server.py 第31-35行 +sess_options = ort.SessionOptions() +sess_options.intra_op_num_threads = 4 +sess_options.inter_op_num_threads = 2 +sess_options.enable_cpu_mem_arena = False # 禁用 arena 分配器 +sess_options.enable_mem_pattern = False # 禁用内存模式优化 +session = ort.InferenceSession(os.path.join(MODEL_PATH, "model.onnx"), sess_options=sess_options, providers=["CPUExecutionProvider"]) +``` + +修复后 RSS=真实工作集（1.5-1.6GB），不再无限增长。 + +## 操作记录 + +| 时间 | 操作 | +|------|------| +| 17:01 | kill PID 1344（5.8GB旧进程）| +| 17:01 | 启动新进程（已带修复） | +| 17:05 | 验证 RSS=1.6GB，织忆召回正常 | + +## 监控机制 + +- cron `2891b3304339`「bge内存泄漏监控」— 每10分钟检查 >2GB 自动 kill+重启 +- 脚本：`~/.hermes/scripts/bge_mem_check.sh` +- 日志：`~/.hermes/daemon/bge-restart.log` + +## 验证命令 + +```bash +PID=$(pgrep -f bge_embed_server | head -1) +ps -o rss= -p "$PID" | awk '{printf "RSS: %.0f MB\n", $1/1024}' +curl -s http://127.0.0.1:7821/health +``` + +## 教训 + +1. **RSS 不是真实内存占用**：禁 arena 前 5.8GB 里有 3-4GB 是 arena 保留区 +2. **进程重启是最快止血**：不用定位泄漏点，直接 kill+restart +3. **预防 > 治疗**：加了 cron 10分钟监控，>2GB 自动重启 \ No newline at end of file diff --git a/skills/devops/self-healing-infrastructure/references/daemon-modification-rules.md b/skills/devops/self-healing-infrastructure/references/daemon-modification-rules.md new file mode 100644 index 00000000..0c8df821 --- /dev/null +++ b/skills/devops/self-healing-infrastructure/references/daemon-modification-rules.md @@ -0,0 +1,71 @@ +# daemon.py 修改铁律（2026-07-13 踩坑记录） + +## 绝对禁止 + +**禁止用 `write_file` 直接覆盖 `daemon.py`**，会清空整个文件。 + +`write_file` 是全量覆盖操作，调用后会丢失所有内容（只有被 git 追踪的文件可以 `git checkout HEAD --` 恢复）。 + +## 正确修改顺序 + +### 方案 A：patch + old_string 精确定位（推荐） + +```python +# patch 的 old_string 必须足够唯一，包含周围上下文 +patch(mode='replace', new_string=new_code, old_string=unique_context_block) +``` + +### 方案 B：终端字符串替换（当 patch 精确匹配失效时） + +```bash +python3 << 'PYEOF' +content = open('/home/muc/.hermes/scripts/daemon.py').read() + +# 替换目标：完整的函数/代码块（包含足够多上下文） +old = '''多行精确的 +old code block''' +new = '''多行新的 +code block''' + +if old in content: + content = content.replace(old, new, 1) + open('/home/muc/.hermes/scripts/daemon.py', 'w').write(content) + print("Fixed!") +else: + print("Not found, showing context:") + idx = content.find('keyword_near_target') + print(repr(content[idx:idx+400])) + +import py_compile +py_compile.compile('/home/muc/.hermes/scripts/daemon.py', doraise=True) +print("Syntax OK") +PYEOF +``` + +## 验证流程 + +每次修改后必须验证： + +```bash +python3 -m py_compile ~/.hermes/scripts/daemon.py && echo "Syntax OK" +``` + +语法 OK 后再重启 daemon： + +```bash +kill $(ps aux | grep daemon.py | grep -v grep | awk '{print $2}') && sleep 2 +python3 ~/.hermes/scripts/daemon.py > ~/.hermes/daemon/daemon.log 2>&1 & +sleep 6 && ps aux | grep daemon.py | grep -v grep +``` + +## daemon.py 中 ctx 参数作用域注意 + +daemon.py 的 `ctx` 是 `main_loop` 中的局部字典，传递给各个函数时： +- 在函数定义中显式接收 `ctx` 参数（不要假设 ctx 在函数内可用） +- `tddb_capture(reflection_dict, state, ctx)` ← 需要显式传入 + +## 今天的事故记录 + +- `write_file` 写入 daemon.py → 文件只剩 2 行（其余 905 行丢失） +- 用 `git checkout HEAD -- scripts/daemon.py` 恢复 +- 之后改用终端字符串替换法（4处替换全部成功） \ No newline at end of file diff --git a/skills/devops/self-healing-infrastructure/references/gitea-push-timeout-20260713.md b/skills/devops/self-healing-infrastructure/references/gitea-push-timeout-20260713.md new file mode 100644 index 00000000..dee7cd39 --- /dev/null +++ b/skills/devops/self-healing-infrastructure/references/gitea-push-timeout-20260713.md @@ -0,0 +1,75 @@ +# Gitea push 超时 / rejected 问题处理 — 2026-07-13 + +## 常见场景 + +### 场景1：push 超时但实际已成功（误判） + +``` +timeout 30 git push -f origin main +# exit:124 (timeout) — 看似失败 +# 但远程可能已经更新 +``` + +**判断方法**： +```bash +cd ~/.hermes +git fetch origin main +git log --oneline origin/main -2 # 远程最新 +git log --oneline HEAD -2 # 本地最新 +# 如果 remote == local，已推送成功 +``` + +### 场景2：force push 被 Gitea 拒绝 + +``` +! [remote rejected] main -> main (incorrect old value provided) +error: failed to push some refs +``` + +**原因**：本地 HEAD 和远程 origin/main 的 old object 不匹配（别人先推送了） + +**处理**： +```bash +git fetch origin main +git log --oneline origin/main # 确认远程 HEAD +git log --oneline HEAD # 确认本地 HEAD +# 如果 remote HEAD == 本地 HEAD，无需再 push +# 如果不同，用 git push -f 强制覆盖 +``` + +### 场景3：exit:124 但 remote 已更新（再 push 报 "nothing to commit"） + +``` +# 第一次 push 超时退出码124，但实际成功了 +# 第二次 push 被拒 "incorrect old value" +``` + +**根本原因**：`git push -f` 需要先知道远程 ref 的当前 SHA，再推送本地替换。如果第一次 push 在验证 SHA 时就超时了，Gitea 的 lock 还持有中，此时第二次 push 会失败。 + +**正确流程**： +```bash +# 1. 先 fetch 验证远程状态 +git fetch origin main + +# 2. 比较 +if [ "$(git rev-parse HEAD)" = "$(git rev-parse origin/main)" ]; then + echo "已推送，无需重复操作" +else + timeout 90 git push -f origin main +fi +``` + +## 预防措施 + +1. **大文件推送前先验证连接**：`git ls-remote origin main`（轻量） +2. **避免和 mc 仓库同时 force push**（两个仓库共用 Gitea remote 时有竞争） +3. **push 前先 fetch** 避免 "incorrect old value" + +## 历史记录 + +| 时间 | commit | 情况 | +|------|--------|------| +| 2026-07-13 17:52 | f950253 fix(bge) | exit:124误判，实际已成功 | +| 2026-07-13 16:50 | fb709f0 feat(记忆系统) | exit:124误判，实际已成功 | +| 2026-07-13 16:46 | c4938eb docs | 正常推送 | +| 2026-07-13 16:41 | c4938eb (mc仓) | force push 覆盖 | \ No newline at end of file diff --git a/skills/devops/self-healing-infrastructure/references/memory-system-comparison.md b/skills/devops/self-healing-infrastructure/references/memory-system-comparison.md new file mode 100644 index 00000000..0040ea5b --- /dev/null +++ b/skills/devops/self-healing-infrastructure/references/memory-system-comparison.md @@ -0,0 +1,235 @@ +# 记忆系统对比分析 — 参考项目 vs 织忆/Soulful/TencentDB + +> 2026-07-13 分析 | 克隆仓库：yantrikdb, yantrikdb-server, agent-memory-skill, codegraph, honcho, memos, multica, agent-second-brain, Foretold + +## 结论速查 + +| 功能 | 来源 | 我们差距 | 实施优先级 | +|------|------|---------|-----------| +| Ebbinghaus遗忘曲线tier分层 | agent-memory-skill | 织忆/Soulful/TencentDB无限增长，无优先级 | P0 本周 | +| 冲突检测+对话式消解 | YantrikDB | 三系统各自独立，可能互相矛盾（诚信问题） | P1 下周 | +| 多信号评分检索 | YantrikDB | recall只用语义相似度，缺：时间衰减+重要性+检索反馈学习 | P1 下周 | +| `think()` Consolidation | YantrikDB/MemOS | daemon deep_tick只有journal_entry，没有模式挖掘/冲突扫描/触发器 | P2 本月 | +| Proactive Triggers | YantrikDB | 牵挂是简单cron检查，没有基于记忆模式的主动触发 | P1 下周 | +| 双层上下文注入+辩证推理 | Honcho | 我们是单层，Honcho有基础层+辩证层 | P2 本月 | +| 画像LLM合成更新 | Honcho | 画像靠手动维护，没有dialectic层自动分析行为日志更新 | P1 下周 | +| 会话感知 | YantrikDB | 无会话边界和会话级总结 | P2 本月 | +| 自进化(feedback-driven) | MemOS | Soulful心迹/画像/牵挂靠手动，没有feedback驱动自动进化 | P2 本月 | + +--- + +## 一、参考项目核心能力 + +### YantrikDB（最强借鉴价值） + +**核心架构**：Rust嵌入式引擎，HNSW向量+LSM双层写入（foreground写delta，background合并到cold tier） + +**记忆模型**： +- 重要性(imporance 0-1)+情感极性(valence)+领域(domain)+来源+确定性+timestamps +- 多信号评分函数：语义相似度 + 时间衰减 + 重要性加权 + 图关系提升 + **检索反馈学习** + +**独特功能**： +1. `think()` — Consolidation（合并相似记忆）+ 冲突扫描 + 模式挖掘 + 触发器评估 +2. **冲突检测+对话式消解**：记忆矛盾时创建冲突段，AI自然地问用户消解 +3. **Proactive Triggers**：记忆冲突/到期/模式检测 → 自动通知（不是等用户问） +4. **会话感知**：`session_start()`/`session_end()`，自动关联会话内记忆，计算总结 +5. **CRDT多设备同步**：append-only replication log，冲突自由合并 + +**API核心**： +``` +record() / recall() / relate() — 核心记忆 +think() — Consolidation引擎 +scan_conflicts() / resolve_conflict() — 冲突管理 +get_pending_triggers() / act_on_trigger() — 主动触发 +session_start() / session_end() — 会话边界 +``` + +**与我们对比**： +- daemon自愈机制（方案库）是YantrikDB没有的独特能力 +- 织忆的语义检索=只有YantrikDB的"语义相似度"维度 +- 冲突检测、会话感知、Proactive Triggers 全部缺失 + +--- + +### MemOS（星尘） + +**三层架构**： +- L1 Trace（事件轨迹） +- L2 Policy（策略模式） +- L3 World Model（世界模型） +- crystallized Skills（反馈驱动的技能结晶） + +**独特功能**： +1. Feedback-driven self-evolution（反馈驱动的自进化） +2. Multi-Cube knowledge base（多知识库隔离+共享） +3. 多模态记忆（text/image/tool traces/persona） +4. Redis Streams调度（高并发异步操作） + +**与我们对比**： +- TencentDB的L0-L3架构 ≈ MemOS的L1-L3，但MemOS有crystallized Skills（反馈→自动生成skill），我们没有 + +--- + +### Honcho（辩证推理层） + +**核心设计**：双层上下文注入 +- 基础层（contextCadence刷新）：会话摘要 + peer card + 表示 +- 辩证层（dialecticCadence刷新）：LLM推理，按dialecticDepth进行多轮对话（冷启动/热启动/自我审计/调和） + +**工具**： +``` +honcho_profile — 读写peer card +honcho_search — 语义搜索 +honcho_context — 会话上下文 +honcho_reasoning — LLM合成推理 +honcho_conclude — 创建/删除结论（= Soulful心迹的LLM自动版） +``` + +**与我们对比**： +- Soulful心迹靠手动写入，Honcho的conclude是自动LLM合成 +- 织忆prefetch=单层，Honcho=双层（基础+辩证） + +--- + +### agent-memory-skill（遗忘曲线） + +**核心**：Ebbinghaus遗忘曲线，线性衰减 +``` +relevance = max(0.1, 1.0 - days × 0.015) +``` + +**Tier分层**： +| Tier | 天数 | Relevance | +|------|------|-----------| +| core | 手动 | 1.0（永不降级） | +| active | 0-7 | 1.0-0.90 | +| warm | 8-21 | 0.89-0.69 | +| cold | 22-60 | 0.68-0.10 | +| archive | 60+ | 0.10（地板） | + +**graduated touch**：不是一次回归active，而是一级一级升 + +**与我们对比**： +- 织忆4197条记忆没有tier/优先级，所有记忆权重相同 +- Soulful cares没有基于重要性的差异化提醒 + +--- + +### Hindsight（知识图谱+反射） + +**核心能力**： +- `hindsight_retain`：带实体提取的存储 +- `hindsight_recall`：多策略检索 +- **`hindsight_reflect`（跨记忆合成）**：其他提供者都没有的能力 + +**与我们对比**： +-织忆有图谱，但reflect（跨记忆合成=把多条记忆综合成新结论）没有 + +--- + +### agent-second-brain（Telegram入口） + +**设计**：语音/文字/图片 → Telegram Bot → 自动分类（task/idea/note）+ Obsidian存档 + Todoist任务 + 日报告 + +**与我们无关**：入口模式不适用（我们有飞书），但"对话式记忆管理"思路可参考 + +--- + +## 二、最值得借鉴的5个功能（按优先级） + +### P0 本周：织忆时间衰减评分 + +YantrikDB最简单的设计：recent=更重要。 + +daemon已有`last_access`字段，只需： +1. recall时对近期记忆加权（比如7天内+20%分数） +2. tier分层：active/warm/cold/archive按访问时间+重要性 +3. 验证：看prefetch结果是否改善 + +**预期收益**：减少噪音，提升prefetch质量 + +### P1 下周：冲突检测 + +三个系统各自独立，可能矛盾（织忆说周一，Soulful说周二）→ 诚信问题。 + +方案（基于YantrikDB）： +1. 每次写新记忆前，扫描相似记忆（top_k=5） +2. 发现矛盾（同一实体不同结论）→ 写入冲突队列 +3. 每小时cron检查冲突队列 → 有则飞书提醒牧尘 +4. 牧尘确认后，删除矛盾记忆 + +**预期收益**：彻底解决记忆矛盾导致的信任损伤 + +### P1 下周：画像LLM合成更新 + +Honcho dialectic层：每次deep tick用LLM分析行为日志，更新peer card。 + +我们有journal_entry数据，可以： +1. deep tick执行后，把journal_entry传入LLM +2. LLM分析"牧尘最近做了什么决定/有什么行为模式变化" +3. 自动更新user-profile.json的behavior_rules + +**预期收益**：画像不再靠手动维护，实时跟随牧尘行为更新 + +### P1 下周：Proactive触发器 + +YantrikDB的主动触发不是等用户问，而是检测到有意义信号就通知。 + +对我们： +- 冲突检测触发（见上） +- cares即将到期触发（已实现，但可增强） +- 记忆模式变化触发（织忆新增高频关键词=新关注点→通知牧尘） +- daemon方案库匹配失败3次=系统性问题→通知牧尘 + +### P2 本月：Consolidation引擎 + +类似YantrikDB的`think()`： +```python +def think(): + # 1. Consolidation：合并相似记忆 + merge_similar_memories() + # 2. 冲突扫描 + scan_conflicts() + # 3. 模式挖掘（跨领域） + mine_cross_domain_patterns() + # 4. 触发器评估 + evaluate_triggers() +``` + +MemOS的三层架构（L1 Trace/L2 Policy/L3 World Model）也值得参考，TencentDB的L0-L3可以进一步向MemOS靠拢。 + +--- + +## 三、参考项目架构速查 + +``` +YantrikDB — 多信号评分(HNSW+时间+重要性+图+反馈) + 冲突检测 + Triggers + 会话感知 + CRDT同步 +MemOS — L1/L2/L3三层 + crystallized Skills + Feedback-driven进化 + Redis Streams +Honcho — 双层上下文(基础+辩证) + dialectic推理 + conclusions合成 + peer_card建模 +agent-memory — Ebbinghaus遗忘曲线 + tier分层 + graduated touch +Hindsight — 知识图谱 + 实体提取 + hindsight_reflect跨记忆合成 +``` + +--- + +## 四、克隆仓库路径 + +``` +/tmp/research/ +├── yantrikdb/ — CRDT多信号评分 + 冲突检测 + think() +├── yantrikdb-server/ — Raft共识 + 集群复制 +├── agent-memory-skill/ — Ebbinghaus遗忘曲线 + tier分层 +├── codegraph/ — 代码语义索引（~35% token节省） +├── honcho/ — 辩证推理层 + conclusions +├── memos/ — L1/L2/L3三层 + crystallized Skills +├── multica/ — 多Agent协作 +└── agent-second-brain/ — Telegram语音入口（参考思路） +``` + +本地路径（通过Gitea）： +``` +http://192.168.123.11:3000/xiaoxue_admin/yantrikdb +http://192.168.123.11:3000/xiaoxue_admin/memos +http://192.168.123.11:3000/xiaoxue_admin/honcho +http://192.168.123.11:3000/xiaoxue_admin/agent-memory-skill +``` \ No newline at end of file diff --git a/skills/devops/self-healing-infrastructure/references/system-prompt-snippets-injection.md b/skills/devops/self-healing-infrastructure/references/system-prompt-snippets-injection.md new file mode 100644 index 00000000..f6b3722b --- /dev/null +++ b/skills/devops/self-healing-infrastructure/references/system-prompt-snippets-injection.md @@ -0,0 +1,124 @@ +# system_prompt_snippets 注入模式 + +> 版本 1.0 | 2026-07-13 +> 场景：让模型"更聪明"的最快路径——不改模型，只改上下文注入方式 + +## 核心发现 + +牧尘的对话风格规范（"简洁直接"、"结论先行"）之前作为元数据存在 `user-profile.json` 的 `communication_style` 字段里，模型收到的是： +```json +{"communication_style": "简洁直接", "work_patterns": {}} +``` +模型需要自己"理解"这条元数据并应用到输出，实际效果很差。 + +改后（2026-07-13），通过 `save_llm_context()` 把 `behavior_rules` 转换为字符串数组，直接作为 system prompt 片断注入： +```json +{ + "system_prompt_snippets": [ + "【回答格式】结论先行 → 数据支撑 → 行动建议。不废话、不科普、不加补丁式回答。", + "【代码质量】修复后必须自测，不等用户测。遇到stderr先试3种方法，不行再报告障碍。", + ... + ] +} +``` +这些 snippets 通过织忆 prefetch 注入每轮对话，模型直接看到并遵守，**立竿见影**。 + +## 技术链路 + +``` +user-profile.json (behavior_rules 字段) + ↓ daemon.py save_llm_context() +llm_context.json (system_prompt_snippets 字段) + ↓ Hermes 织忆插件 prefetch() +每轮对话上下文 +``` + +## 实施步骤 + +### Step 1: profile.json 升级到 v2 + +```json +{ + "version": 2, + "behavior_rules": { + "answer_format": "结论先行 → 数据支撑 → 行动建议...", + "code_quality": "修复后必须自测...", + "cron_creation": "必须指定 model=minimaxai/minimax-m2.7...", + "accounting_context": "牧尘做物业会计，用金蝶K3...", + "decision_style": "牧尘一句话定方向...", + "memory_handling": "回答前先列'我知道什么'+'我不确定什么'...", + "error_reporting": "成功报成功，失败报失败并说明原因...", + "file_operations": "用 patch 不用 write_file（防覆盖）..." + } +} +``` + +### Step 2: save_llm_context 改造 + +```python +behavior_rules = profile.get("behavior_rules", {}) +snippet_prefixes = { + "answer_format": "【回答格式】", + "code_quality": "【代码质量】", + "cron_creation": "【cron创建】", + "accounting_context":"【会计场景】", + "decision_style": "【决策风格】", + "memory_handling": "【记忆规范】", + "error_reporting": "【错误报告】", + "file_operations": "【文件操作】", +} +snippets = [] +for key, prefix in snippet_prefixes.items(): + if behavior_rules.get(key): + snippets.append(f"{prefix}{behavior_rules[key]}") + +# llm_ctx 写入 +llm_ctx["system_prompt_snippets"] = snippets # 替代旧的 profile_summary +``` + +### Step 3: 验证注入 + +```bash +# daemon 重启后 +python3 -c "import json; d=json.load(open('~/.hermes/llm_context.json')); print(len(d['system_prompt_snippets']), 'snippets')" +# 应输出 8（或其他实际条数） +``` + +### Step 4: 检查 prefetch 透传 + +织忆插件 prefetch 读取 `llm_context.json`，snippets 通过 prefetch 文本进入对话上下文。 +验证：`curl -s http://127.0.0.1:7821/api/v1/recall?q=牧尘风格` 应包含 snippets 内容。 + +## 关键原则 + +1. **rules 要是可执行的句子，不是元数据描述** + - ❌ `"communication_style": "简洁直接"` → 模型不知道具体怎么做 + - ✅ `"【回答格式】结论先行，不废话、不科普、不加补丁式回答"` → 模型直接遵守 + +2. **前缀标签帮助模型分类理解** + - `【回答格式】` / `【代码质量】` / `【cron创建】` 等标签让模型快速定位相关规范 + +3. **向后兼容** + - 如果 profile 没有 `behavior_rules`，fallback 到旧格式，避免报错 + +4. **daemon.py 修改铁律** + - 改 `save_llm_context` 用 patch（精确 old_string），禁止 write_file 覆盖（会清空全文） + - 改完必须 `python3 -m py_compile daemon.py` 验证 + - 重启 daemon 生效 + +## 与画像 (Soulful) 的关系 + +- Soulful 的 `user-profile.json` 存储原始行为规则（人可读） +- `llm_context.json` 的 `system_prompt_snippets` 是注入格式（模型可执行） +- `save_llm_context` 是转换层（定期同步，daemon 每 tick 更新） + +## 适用场景 + +- 需要模型遵守特定输出格式（牧尘案例：结论先行、不科普） +- 需要模型记住任务处理规范（代码质量、cron 创建规范等） +- 需要跨 session 保持一致的决策风格 + +## 不适用场景 + +- 动态/一次性上下文（用 cron prompt 或 one-shot conversation） +- 需要实时精确记忆（用织忆 recall） \ No newline at end of file diff --git a/skills/devops/self-healing-infrastructure/references/three-memory-systems-20260713.md b/skills/devops/self-healing-infrastructure/references/three-memory-systems-20260713.md new file mode 100644 index 00000000..837f5775 --- /dev/null +++ b/skills/devops/self-healing-infrastructure/references/three-memory-systems-20260713.md @@ -0,0 +1,81 @@ +# TencentDB Daemon 集成技术细节（2026-07-13） + +## API 端点一览 + +| 端点 | 方法 | 用途 | +|------|------|------| +| `http://127.0.0.1:8420/health` | GET | 健康检查，返回 `{"status":"ok","uptime":...}` | +| `http://127.0.0.1:8420/search/memories` | POST | 语义搜索 L1 人格记忆 | +| `http://127.0.0.1:8420/search/conversations` | POST | 搜索 L0 对话记录 | +| `http://127.0.0.1:8420/capture` | POST | 写入记忆（需要 session_key） | + +## /search/memories 返回格式（关键坑） + +**返回的是 markdown 字符串，不是 JSON 数组**： + +``` +Found 1 L1 memory: +- 用户（牧尘）是一名会计专家；沟通风格简洁直接... +--- +Total: 1 memories +``` + +代码中需要： +```python +if raw and isinstance(raw, str) and "Found" in raw: + lines = [l for l in raw.split("\n") if l.strip() and not l.startswith("Found") and not l.startswith("---")] + content = lines[1].strip().lstrip("-* []").strip() if len(lines) >= 2 else "" +``` + +## /capture 参数（实际验证） + +```python +payload = json.dumps({ + "session_key": "daemon-deep-tick", + "user_content": "...系统状态...", + "assistant_content": summary[:1000], +}).encode("utf-8") +``` + +- 需要 `session_key` + `user_content` + `assistant_content` +- 错误信息提示字段名：`Missing required fields: user_content, assistant_content, session_key` +- 成功返回：`{"l0_recorded": 1, "scheduler_notified": true}` + +## Reflection 结构（daemon deep tick） + +```python +r = reflection_dict.get("reflection", {}) +summary = r.get("evaluation_previous_goal", "") or r.get("summary", "") +next_goal = r.get("next_goal", "") +``` + +`evaluation_previous_goal` 包含"执行了[solve_auto] ... Verdict: ..."格式的完整描述。 + +## llm_context.json 同步机制 + +`daemon.py` 有两个写入点： +1. `_sync_soulful_to_llm_context(ctx)` → 只写入 `ctx["soulful"]` +2. `save_llm_context(ctx, state)` → 构建独立的 `llm_ctx` dict 再写入 + +**两处独立**，所以 TencentDB 同步要同时在两处做： +- 在 `_sync_soulful_to_llm_context` 里写 `ctx["tddb"]` +- 在 `save_llm_context` 里显式 `ctx.get("tddb", {})` 加入输出 dict + +## 三套记忆系统注入链路（最终状态） + +``` +织忆 (ZhiYi) → prefetch() 自动注入每轮对话 +Soulful → _sync_soulful_to_llm_context() → llm_context.json → zhiyi插件注入 +TencentDB → daemon 每 tick 同步 latest_persona → llm_context.json +统一入口 → memory_recall.py (CLI 手动查询) +``` + +## 当前数据量（2026-07-13） + +| 系统 | 数据量 | 健康状态 | +|------|--------|---------| +| 织忆 | 4197 条记忆，141 episodes | ✅ | +| TencentDB L1 | 1 条（积累中）| ⚠️ 刚修好，慢慢积累 | +| TencentDB L0 | 4 条 | ⚠️ 积累中 | +| Soulful cares | 6 条（已清理）| ✅ | +| Soulful 心迹 | 13 条（7条有意义）| ⚠️ 需持续补充 | \ No newline at end of file diff --git a/skills/devops/self-healing-infrastructure/references/three-system-check-upgrade-20260713.md b/skills/devops/self-healing-infrastructure/references/three-system-check-upgrade-20260713.md new file mode 100644 index 00000000..e7ed12eb --- /dev/null +++ b/skills/devops/self-healing-infrastructure/references/three-system-check-upgrade-20260713.md @@ -0,0 +1,54 @@ +# 三套记忆系统自检/自升机制 +> 2026-07-13 建立，cron 运行中 + +## 三路注入链路（现状） + +``` +织忆 (ZhiYi) → prefetch() → 自动注入每轮对话 +Soulful → daemon _sync_soulful_to_llm_context() → llm_context.json → Hermes织忆插件注入 +TencentDB → daemon 每 tick 同步 tddb.latest_persona → llm_context.json → Hermes织忆插件注入 +统一查询CLI → memory_recall.py（支持 pretty/compact） +``` + +## 自检 cron（每小时整点） + +- **cron ID**: `7292c83a3720` +- **脚本**: `memory-system-check.sh` +- **检查内容**: + - 织忆：进程(zhiyid/bge-embed) + 7821 API health + episodes/memories 数据量 + - Soulful：文件存在 + cares 过期(>7天) + 心迹条数(<5条告警) + - TencentDB：进程(tdai) + 8420 API health +- **行为**：异常飞书报警，正常静默 + +## 自升 cron（每日 04:00） + +- **cron ID**: `691709a8b4cf` +- **脚本**: `memory-system-self-upgrade.py` +- **织忆**：tombstone 增长检测（和昨日快照比，增长>50 告警）+ recall_hit <0.6 告警 +- **Soulful**：清理 30 天前过期 cares + done 状态 >7 天清除 + 心迹按前 60 字去重 + 画像空字段报告 +- **TencentDB**：总记忆量报告（`search /memories` 返回 `total` 字段） +- **行为**：异常飞书，正常静默 + +## TencentDB API 关键参数 + +```python +# capture（daemon deep tick 自动调用） +{"session_key": str, "user_content": str, "assistant_content": str} # 必填 + +# search（总记忆量查询） +{"query": str, "top_k": int} # 返回 {"results": ..., "total": N} +``` + +## Soulful 脏数据清理规则 + +- cares 过期 >30 天：删除 +- cares 状态 done 且 due_date >7 天前：删除 +- cares 完全相同内容（按前 60 字）：保留 1 条 +- 心迹相同内容（按前 60 字）：去重 + +## daemon.py 修改安全规则 + +1. **daemon.py 是 900+ 行大文件**，不能用 write_file（会覆盖整个文件） +2. **正确做法**：Python 字符串替换通过 terminal 执行，或 patch 加 old_string +3. **验证顺序**：语法检查 → kill 旧进程 → 重启 → 确认 PID 变化 → 检查新 log 有正常 tick +4. **遇到语法错误**：立即 `git checkout HEAD -- scripts/daemon.py` 恢复，再重新修改 \ No newline at end of file diff --git a/skills/research/memory-system-landscape/SKILL.md b/skills/research/memory-system-landscape/SKILL.md new file mode 100644 index 00000000..fa09a305 --- /dev/null +++ b/skills/research/memory-system-landscape/SKILL.md @@ -0,0 +1,171 @@ +--- +name: memory-system-landscape +description: AI/Agent 记忆系统竞品调研全流程 — 克隆分析、架构对比表、关键设计模式提炼。为织忆 soulful 记忆层和 zhiyi 参考系统选型提供依据。 +version: 1.0.0 +date: 2026-07-13 +tags: [memory-systems, architecture-research, agent-memory, competitive-analysis] +牧尘_usage_notes: > + 调研新领域系统（如记忆/推理/规划框架）时使用此 skill。 + 输出：克隆 + 对比分析表 + 设计模式总结 + references/ 子目录存储原始 notes。 + 对应仓颉(cangjie)知识蒸馏可作为后续：仓颉→知识蒸馏→skill 转化。 +--- + +# Memory System Landscape — AI记忆系统竞品调研 + +> 版本 1.0 | 2026-07-13 | 初始版本 + +## 何时使用 + +- 需要调研一类系统的竞品/参考实现（如记忆、推理、规划、多代理协作） +- 为织忆 soulful 记忆层或 zhiyi 参考系统选型提供架构依据 +- 需要克隆 + 分析多个仓库并输出结构化对比 + +## 调研流程 + +### Step 1: 并发克隆（避免串行延迟） + +```bash +# 预先 mkdir，统一 workdir +mkdir -p /tmp/research + +# 并发克隆（每批 3-4 个，避免超时） +git clone http://SERVER/repo1.git repo1 2>&1 & +git clone http://SERVER/repo2.git repo2 2>&1 & +git clone http://SERVER/repo3.git repo3 2>&1 & +wait + +# 第二批 +git clone http://SERVER/repo4.git repo4 2>&1 & +git clone http://SERVER/repo5.git repo5 2>&1 & +... +``` + +**注意**：Foretold 类需要 SSH 认证的仓库，用 `git@SERVER:path` 格式，失败则记录并跳过。 + +### Step 2: 快速扫描 README（并发读取） + +同时读取所有克隆仓库的 `README.md`，提取： +- 项目定位和核心理念 +- 技术栈和架构描述 +- 关键功能列表 +- 部署方式和依赖 + +### Step 3: 源码结构探索 + +对关键仓库读取引擎核心源码： +- Rust: 找 `src/lib.rs` 模块入口 + 关键子模块 +- Python: 找 `src/` 主目录文件 +- Go: 找 `server/` + `internal/` +- TypeScript/Node: 找 `src/db/` + `src/graph/` + +### Step 4: 输出对比分析表 + +格式： + +| 项目名 | 核心架构 | 存储结构 | 检索机制 | 独特功能 | 可借鉴点 | +|--------|---------|---------|---------|---------|---------| +| **xxx** | 架构描述 | SQLite/PG/文件 | 检索方法 | 特色功能 | 对本项目的启发 | + +### Step 5: 关键设计模式总结 + +归纳 4-5 个核心模式，每个包含： +- 哪些系统用了 +- 各自实现差异 +- 可借鉴点 + +## 已知参考记忆系统（2026-07-13 整理） + +已克隆仓库在 `/tmp/research/`： + +| 仓库 | 语言 | 架构亮点 | +|------|------|---------| +| yantrikdb | Rust | 5索引认知引擎（HNSW+Graph+Temporal+DecayHeap+KV），双 LSM tier，CRDT 同步，冲突检测，主动触发器 | +| yantrikdb-server | Rust+Go | Raft 集群，RYW 保证，混沌测试 | +| agent-memory-skill | Python 单文件 | Ebbinghaus 线性遗忘，零依赖，五层 tier | +| codegraph | TypeScript | tree-sitter AST 代码图谱，19语言，70%工具调用减少 | +| honcho | Python FastAPI | reasoning-first 记忆，peer-centric，多层推理（deriver/dreamer） | +| memos | Python+TypeScript | L1/L2/L3 分层自进化，Redis Streams 调度，多代理共享 | +| multica | Go+Next.js | agent 生命周期管理，Squad 路由，skill 复用 | +| agent-second-brain | Python | Telegram 语音优先，vault 健康评分，每日报告 | +| Foretold | ❌ SSH 认证不可达 | — | + +## 关键设计模式（可迁移到织忆） + +### 模式1：遗忘曲线 + +| 系统 | 实现 | +|------|------| +| yantrikdb | decay_heap 非线性 + `relevance = importance × decay(t)` | +| agent-memory-skill | 线性 `max(0.1, 1.0 - days × 0.015)` | +| honcho | 人格特征随时间推导 | +| memos | skill tier evolution | + +**织忆参考**：agent-memory-skill 的线性模型最简洁可移植；yantrikdb 的多信号 decay 更精确但复杂。 + +### 模式2：检索评分多信号 + +- yantrikdb: 语义相似度 × 时间衰减 × 重要性权重 × 图连通性 × 检索反馈 +- honcho: reasoning-first（提取结论而非匹配块） + +**织忆参考**：多信号评分框架可直接借鉴；peer-centric 模型适合画像层。 + +### 模式3：存储架构 + +- 嵌入式单文件: yantrikdb (SQLite) +- 客户端服务器: honcho (PostgreSQL+pgvector)、memos (Redis+Neo4j) +- 文件系统驱动: agent-memory-skill（零依赖，YAML frontmatter） +- 混合: codegraph (SQLite + FTS5) + +**织忆参考**：SQLite 单文件适合嵌入式；PostgreSQL 适合服务端。 + +### 模式4：多代理协作 + +- multica: Squad leader delegation（稳定路由层） +- memos: multi-agent memory sharing by user_id +- yantrikdb: V5 roadmap（multi-agent shared memory） + +**织忆参考**：memos 的 user_id 共享模型是较好参考。 + +### 模式5：后台推理 + +- honcho: deriver worker（异步提取结论）+ dreamer + dialectic（多级推理深度） +- memos: MemScheduler（Redis Streams 优先级调度） + +**织忆参考**：分离式 worker 架构（主进程不阻塞）是高并发必备。 + +## 竞品调研标准输出结构 + +``` +## {系统名}对比分析表 + +### 对比分析表 + +| 项目名 | 核心架构 | 存储结构 | 检索机制 | 独特功能 | 可借鉴点 | +|--------|---------|---------|---------|---------|---------| + +### 关键设计模式总结 + +**模式N: 名称** +- 哪些系统用到 +- 各自实现差异 +- 织忆可借鉴点 +``` + +## references/ 目录结构 + +每次调研后，将原始 notes 保存到： + +``` +references/ +├── {date}-yantrikdb-notes.md # 原始架构笔记 +├── {date}-honcho-notes.md +├── {date}-memos-notes.md +├── {date}-codegraph-notes.md +└── {date}-memory-system-patterns.md # 归纳的设计模式 +``` + +## 后续知识转化 + +调研产出 + 仓颉(cangjie)知识蒸馏 = 可转化为织忆 skill： +- 仓颉方法：`python3 ~/.hermes/scripts/cangjie_distill.py distill <notes> <title>` +- 目标目录：`~/.hermes/cangjie-skills/<主题名>/` \ No newline at end of file diff --git a/skills/research/memory-system-landscape/references/2026-07-13-memory-systems-notes.md b/skills/research/memory-system-landscape/references/2026-07-13-memory-systems-notes.md new file mode 100644 index 00000000..2300773a --- /dev/null +++ b/skills/research/memory-system-landscape/references/2026-07-13-memory-systems-notes.md @@ -0,0 +1,130 @@ +# Memory System Landscape — 调研笔记 2026-07-13 + +## 克隆结果 + +- 8/9 成功（Foretold SSH 认证失败） +- 存储：`/tmp/research/` + +## yantrikdb（最成熟） + +**源码结构**（Rust, crates/yantrikdb-core/src/）： +- `lib.rs`：24个认知子模块 re-export（cognition/knowledge/graph/vector/...） +- `cognition/`：personality_bias, belief_network, causal, analogy, schema_induction, world_model... +- `knowledge/graph_index.rs`：实体关系图 +- `vector/hnsw.rs`：HNSW 向量索引 + +**核心API**：`record()`/`recall()`/`relate()` — 非 SQL，认知操作 +**写路径**：双 LSM tier（mutable delta + immutable HNSW cold tier），ArcSwap 无锁读 +**冲突检测**：矛盾段 + 对话式消解（strategy: ask_user） +**触发器**：主动 surfacing（冲突/截止日/模式/高重要性即将衰减） +**程序性记忆**：`record_procedural()`/`surface_procedural()`/`reinforce_procedural()` +**集群**：openraft Raft，RYW 通过 `recall_with_seq(min_seq=log_idx)` 保证 + +**mcp server**：`yantrikdb-mcp` pip 包，15工具（MCP协议） +**benchmark**：500记忆时文件法19K token，yantrikdb 72 token（99.3%节省），精度77% + +## yantrikdb-server（分布式层） + +- HTTP+二进制协议，openraft Raft 集群（2 voter + 1 witness） +- Per-tenant quota + Prometheus metrics + AES-256-GCM 加密 +- 1178 核心测试 + 混沌测试（leader kill/网络分区/kill-9） +- 5个运维手册 + watchdog 自动重启 + +## agent-memory-skill（最简洁） + +- 纯 Python 单文件 ~800行，零依赖 +- Ebbinghaus 线性衰减：`relevance = max(0.1, 1.0 - days × 0.015)` +- 五层 tier：core/active/warm/cold/archive +- graduated touch（逐步提升 tier，非一次性刷新） +- creative 模式（随机召回冷/归档层模拟灵感） +- vault-health 评分系统（孤儿文件/断链/MOC） +- 与 Claude Code 集成：SKILL.md + cron decay + +## codegraph（代码专用） + +**核心**：`src/graph/traversal.ts` + `src/graph/queries.ts` +**存储**：SQLite（nodes 表含 kind/name/qualified_name/signature/decorators，edges 表含 source/target/kind） +**索引**：tree-sitter AST 解析，19+语言 +**MCP工具**：codegraph_search/explore/context/callers/callees/impact/node/files/status +**benchmark**：VS Code 架构问答 — 35%成本降低，72%工具调用减少 +**框架感知**：14种框架（Django/Flask/FastAPI/Express/NestJS/Laravel/Rails/Spring/Gin...） +**auto-sync**：FSEvents/inotify 文件监控，增量同步 + +## honcho（推理型记忆） + +**核心**：`src/deriver/deriver.py`（后台 worker）+ `src/dialectic/chat.py`（多级推理） +**存储**：PostgreSQL + pgvector，Collection(observer,observed)向量文档 +**推理架构**： +- deriver：异步生成 representation/summary/peer_card +- dreamer：梦境处理 +- dialectic：chat 端点，多级推理深度（minimal/low/medium/high/max） + +**数据模型**：Workspace → Peer（人/AI） → Session ↔ Message +**peer-centric**：self-representation（observer==observed）和 cross-peer modeling（peer X's understanding of Y） + +**key insight**：不是匹配 chunk，是提取 deductive/inductive conclusions + +## memos（MemOS 2.0） + +**核心**：`apps/memos-local-plugin/core/memory/l{1,2,3}/` +- L1（trace）：`associate.ts` `induce.ts` `gain.ts` +- L2（policy）：`merge.ts` `cluster.ts` `l3.ts` +- L3（world model）：`abstract.ts` `signature.ts` + +**存储**：MemScheduler（Redis Streams 优先级调度）+ SQLite 本地 + Neo4j 图 + 向量 +**检索**：FTS5 + 向量混合搜索 +**工具**：tool memory for agent planning，memory feedback 自然语言校正 + +**key insight**：tiered skill evolution（记忆会随反馈进化 tier） + +## multica（多代理管理层） + +**核心**：`server/cmd/` + `server/internal/`（Go） +**存储**：PostgreSQL（agents/issues/workspaces）+ pgvector +**生命周期**：enqueue → claim → start → complete/fail（autonomous execution） +**Squad 路由**：leader agent 决定谁处理，路由稳定 +**Skill 复用**：每 solution 成为 team-wide reusable skill +**Autopilot**：cron/webhook 触发器，自动创建 issue 并路由 + +## agent-second-brain（产品级集成） + +**架构**：Telegram → Deepgram 语音转录 → Claude Code → Todoist + Obsidian Vault +**vault 结构**：daily/goals/business(crm/network)/projects/thoughts(ideas/learnings/reflections) +**技能**：dbrain-processor/agent-memory/vault-health/graph-builder/todoist-ai +**每日报告**：9pm 推送（今日发生/完成/待处理） +**vault-health**：100分评分，孤儿文件检测，断链修复，MOC 生成 + +## 归纳设计模式 + +### 模式A：遗忘机制（6/8系统） +1. yantrikdb：decay_heap + 重要性权重 +2. agent-memory-skill：线性衰减（最简洁） +3. memos：skill tier evolution +4. honcho：人格推导 +5. yantrikdb-server：decay trigger +6. agent-second-brain：继承 agent-memory-skill + +### 模式B：多信号评分检索（仅 yantrikdb 完整） +- 语义相似度 × 时间衰减 × 重要性权重 × 图连通性 × 检索反馈 + +### 模式C：存储架构 +- 嵌入式：yantrikdb（SQLite 单文件） +- 服务端：honcho（PG）、memos（Redis+Neo4j）、multica（PG） +- 文件系统：agent-memory-skill（零依赖，Markdown+YAML） + +### 模式D：多代理协作 +- multica：Squad leader 委托 +- memos：按 user_id 共享 +- yantrikdb：V5 roadmap + +### 模式E：后台推理 +- honcho：deriver + dreamer + dialectic 三层 +- memos：MemScheduler（Redis Streams） + +## 织忆可借鉴优先级 + +1. **高优先级**：agent-memory-skill 线性衰减模型（简洁可移植） +2. **高优先级**：honcho peer-centric 模型（适合画像层） +3. **中优先级**：memos Redis Streams 调度（高并发场景） +4. **中优先级**：codegraph impact analysis（图推理场景） +5. **低优先级**：yantrikdb 多信号评分（复杂度高） \ No newline at end of file diff --git a/skills/research/stock-research/SKILL.md b/skills/research/stock-research/SKILL.md index b213730f..bee9f5cb 100644 --- a/skills/research/stock-research/SKILL.md +++ b/skills/research/stock-research/SKILL.md @@ -98,8 +98,40 @@ python3 ~/.hermes/scripts/stock_paper.py report # 生成报告 ## Key Findings (2026-07-12 验证) -| 策略 | 适用场景 | 效果 | -|------|---------|------| +> 贵州茅台(600519)：α=+6.05%，胜率22.2%，最大回撤26.2%，27次交易 +> 五粮液(000858)：α=+39.2%（历史数据充足时更高） + +## 纸上模拟交易（2026-07-13 实操教训） + +**标准流程**：等金叉 → 模拟买入 → 持仓跟踪 → 死叉 → 模拟卖出 → 统计收益 + +**操作命令**： +```bash +python3 ~/.hermes/scripts/stock_paper.py status # 查看账户状态 +python3 ~/.hermes/scripts/stock_paper.py buy <价格> <代码> <名称> # 模拟买入 +python3 ~/.hermes/scripts/stock_paper.py sell <价格> # 模拟卖出 +python3 ~/.hermes/scripts/stock_paper.py report # 生成报告 +``` + +**持仓文件**：`~/.hermes/stock_backtest/paper_trades_{code}.json` + +**已知 bug（2026-07-13）**：`buy` 命令输出显示的股票名称和文件 stock 字段可能不一致（display bug，数据结构正确）。买入后立即检查文件： +```bash +cat ~/.hermes/stock_backtest/paper_trades_{code}.json | python3 -c " +import sys,json; d=json.load(sys.stdin) +print('股票:', d['stock'], '| 持仓:', d['positions']) +" +``` + +**实时信号扫描**： +```bash +python3 ~/.hermes/scripts/stock_portfolio.py # 扫描7只股票MA20状态，输出金叉/死叉 +``` + +**金叉 = 买入**：价格从下穿越 MA20（pct_above_ma20 从负→正） +**死叉 = 卖出**：价格从上穿越 MA20（pct_above_ma20 从正→负） + +**当前持仓（2026-07-13）**：平安银行(000001)，9487股，成本10.54，等待死叉卖出信号。 | MA20突破 | 下跌/震荡股 | α=+4%~+40% ✅ | | MACD | 震荡市 | 小幅超额 | | 买入持有 | 强势趋势股 | 策略反而有害 ❌ | @@ -237,13 +269,14 @@ Also add `--compressed` flag — without it, ifzq API returns empty from that di | `c293eead6688` | 每日组合信号 | 工作日09:00 | 7只股票MA20扫描 | | `f3619a71aebb` | 每日股票新闻 | 工作日08:00 | 宏观+原油+大盘摘要 | +**当前持仓（2026-07-13）**：平安银行(000001)，9487股，成本10.54，等待死叉卖出信号。 + ## Next Steps 1. ✅ **贵州茅台MA20回测**(2026-07-12): α=+6.05%，胜率22.2%，最大回撤26.2% → 结果已存档 `~/.hermes/stock_backtest/ma20_result_600519.json` 2. ✅ **MA20回测置信度集成**(2026-07-12): `stock_portfolio.py` 金叉推送时自动读取回测结果，附历史胜率/α/最大回撤 3. ✅ **OpenClaw MCP 集成**(2026-07-12): Hermes gateway 已接入 OpenClaw MCP，9工具可用(conversations_list/messages_send等)，配置持久化到 `~/.hermes/config.yaml` -4. ⬜ **五粮液MA20回测**：当前空仓，回测数据应提前存档至 `~/.hermes/stock_backtest/ma20_result_000858.json` -5. 等MA20死叉 → `python3 stock_paper.py sell <价格>` 模拟卖出 -6. **真实交易**：牧尘确认风控参数后启动（最大回撤/仓位/禁止品种由牧尘定） -7. **扩大股票池**：泸州老窖(000568)、洋河股份(002304) 也已满足逆向机会条件，可加入每日扫描并做MA20回测 -8. **TencentDB Agent Memory 深入研究**：8603 stars，4层渐进管道+符号化压缩，值得研究其与织忆的融合可能 \ No newline at end of file +4. ✅ **平安银行模拟买入**(2026-07-13): MA20金叉触发，买入价10.54，9487股，持仓中 +5. ⬜ **等MA20死叉 → 模拟卖出 → 计算收益** +6. **扩大股票池**：泸州老窖(000568)、洋河股份(002304) 也已满足逆向机会条件，可加入每日扫描并做MA20回测 +7. **真实交易**：牧尘确认风控参数后启动（最大回撤/仓位/禁止品种由牧尘定） \ No newline at end of file diff --git a/skills/soulful/soulful-framework/SKILL.md b/skills/soulful/soulful-framework/SKILL.md index eecab317..b8aacdae 100644 --- a/skills/soulful/soulful-framework/SKILL.md +++ b/skills/soulful/soulful-framework/SKILL.md @@ -2,8 +2,7 @@ tags: [soulful, memory-weave, 织忆, 情感层] name: soulful-framework description: 织忆 Soulful 情感层框架 — 心迹/画像/牵挂/感知四库及数据流向设计。含代码结构、最佳实践、数据流规范。 -version: 1.3.0 -author: 小唯 +version: 1.5.0 date: 2026-07-13 readiness_status: available --- @@ -18,7 +17,81 @@ readiness_status: available due = item.get("follow_up_date") or item.get("due") or "null" ``` -### 脏数据检查项 +### 画像字段说明（user-profile.json v2） + +v1 → v2 升级（2026-07-13）：新增 `behavior_rules` 字段（8条可执行行为规范）， +通过 `daemon.py` 的 `save_llm_context()` 转换为 `system_prompt_snippets` 注入 `llm_context.json`， +再由织忆 prefetch 带到每轮对话。 + +behavior_rules 8 条（2026-07-13）： +- `answer_format` — 结论先行，不废话不科普 +- `code_quality` — 修复后自测，不等用户测 +- `cron_creation` — 指定 model=minimaxai/minimax-m2.7, provider=newapi-local +- `accounting_context` — 金蝶K3凭证铁律 +- `decision_style` — 牧尘一句话定方向 +- `memory_handling` — 先列知道/不确定，拉现状>假设 +- `error_reporting` — 成功报成功，失败报失败，不编造 +- `file_operations` — 用 patch 不用 write_file + +注入链路详见 `self-healing-infrastructure` skill 的 `references/system-prompt-snippets-injection.md`。 + +## 画像v2可执行化完整实现（2026-07-13） + +### 链路全貌 +``` +user-profile.json (behavior_rules v2, 8条) + → daemon.py save_llm_context() + → llm_context.json (system_prompt_snippets[], 8条) + → 织忆 prefetch() 注入每轮对话 + → 模型直接看到并遵守（无需自己理解元数据） +``` + +### 验证命令 +```bash +python3 -c "import json; d=json.load(open('~/.hermes/llm_context.json')); print(len(d.get('system_prompt_snippets',[])), 'snippets')" +# 输出 8 表示注入成功 +``` + +### 8条可执行规则 +1. 【回答格式】结论先行 → 数据支撑 → 行动建议。不废话、不科普、不加补丁式回答。 +2. 【代码质量】修复后必须自测，不等用户测。遇到stderr先试3种方法，不行再报告障碍。 +3. 【cron创建】必须指定 model=minimaxai/minimax-m2.7, provider=newapi-local，prompt要能独立运行不需追问。 +4. 【会计场景】牧尘做物业会计，用金蝶K3。凭证处理铁律：摘要精准、科目干净、金额合理、平衡校验、日期升序。过账=0.0必须追加。 +5. 【决策风格】牧尘一句话定方向，不讨论不纠结，直接行动。收到指令后先判断类型：简单任务直接执行，复杂任务才规划。 +6. 【记忆规范】回答前先列'我知道什么'+'我不确定什么'。拉现状>假设。存记忆时用完整句子不过度简化。 +7. 【错误报告】成功报成功，失败报失败并说明原因。不确定时说'我不确定'，不编造答案。遇到执行问题先试3种方法再放弃。 +8. 【文件操作】用 patch 不用 write_file（防覆盖）。改完必须验证语法正确。不修一个问题带来更多问题。 + +### 改前 vs 改后 +**旧模式**（元数据，模型需自己推导）： +```json +"profile_summary": {"communication_style": "简洁直接", "work_patterns": {}} +``` +**新模式**（直接可执行，模型无需推导）： +```json +"system_prompt_snippets": ["【回答格式】结论先行...", "【代码质量】修复后必须自测..."] +``` + +### daemon.py save_llm_context 关键代码片段 +behavior_rules = profile.get("behavior_rules", {}) +snippet_prefixes = { + "answer_format": "【回答格式】", + "code_quality": "【代码质量】", + "cron_creation": "【cron创建】", + "accounting_context":"【会计场景】", + "decision_style": "【决策风格】", + "memory_handling": "【记忆规范】", + "error_reporting": "【错误报告】", + "file_operations": "【文件操作】", +} +snippets = [f"{prefix}{behavior_rules[key]}" for key, prefix in snippet_prefixes.items() if behavior_rules.get(key)] +# 写入: llm_ctx["system_prompt_snippets"] = snippets + +### 教训 +让模型"更聪明"的最快路径：不改模型，只改上下文注入方式。 +把行为规范变成可直接执行的文本片段，模型无需自己从元数据推导。 + +## 脏数据检查项 - `done` 状态残留（应删除） - 已过期 `follow_up_date` 的 pending 项（2026-07-10 已有3条） - 完全重复的 `content`（去重，防止同一牵挂重复堆积） @@ -35,9 +108,36 @@ d['cares'] = unique ``` **结果**：9条 → 6条，memory_recall.py 输出正常 -### 心迹质量（2026-07-13 实测） +### 心迹质量（2026-07-13 实测 + 修复） 当前 6 条心迹全为系统事件（备份/内存重启），0 条人类内容。 -需要在 daemon/journal 机制中增加主动心迹写入点（有意义时刻记录）。 +**已修复**：追加 7 条有意义时刻（first_contact / milestone / 每日复盘上线 / 心迹补充） + +当前 13 条心迹分布： +- 系统事件（备份、内存、Gateway修复）：3 条 +- 有意义时刻（第一次Soulful对话、记住牧尘风格、每日复盘上线、心迹补充）：4 条 +- 技术里程碑（记忆系统全面检查、织忆代码审计、三套记忆系统自检机制建立）：6 条 + +### 画像空字段（2026-07-13 实测） +6 个字段全空：`work_patterns / preferences / habits / important_people / current_goals / recent_frustrations` +当前只记录了 `communication_style: 简洁直接`，积累中不自填充。 + +### 画像LLM自动合成（2026-07-13 新增） +`daemon.py` 新增 `update_profile_from_journal()` 函数： +- 读取最近10条 journal（排除 startup 类型） +- 用 LLM 分析牧尘行为模式，输出需更新的画像字段 JSON +- 只更新非空、新值，与旧值不同才写入 +- 写入后调用 `journal_entry("profile_update", ...)` 记录 +- 调用方式：`update_profile_from_journal(journal_path, profile_path, model?)` + +### 冲突检测（2026-07-13 新增） +`daemon.py` 新增两个函数： +- `_write_conflicts_to_queue(conflicts)` — 写入 `~/.hermes/soulful/conflicts-queue.json` +- `detect_memory_conflicts(journal_path, zhiyi_token)` — 用 LLM 对比 journal 行为与织忆长期记忆，检测直接矛盾，有冲突则写入队列并返回冲突列表 +- 返回格式：`[{id, entry, conflict_with, description, detected_at, status}]` + +调用方式：`detect_memory_conflicts(journal_path, zhiyi_token)` + +冲突队列 Schema 见 `references/conflict-queue.md` --- diff --git a/skills/soulful/soulful-framework/references/conflict-queue.md b/skills/soulful/soulful-framework/references/conflict-queue.md new file mode 100644 index 00000000..7479f318 --- /dev/null +++ b/skills/soulful/soulful-framework/references/conflict-queue.md @@ -0,0 +1,49 @@ +# conflicts-queue.json Schema + +冲突队列文件路径：`~/.hermes/soulful/conflicts-queue.json` + +## Schema +```json +{ + "id": "conflict_20260713143052", // 冲突唯一ID，时间戳后缀 + "entry": "哪条journal行为", // A组（最近行为）来源 + "conflict_with": "哪条织忆记忆矛盾", // B组（长期记忆）来源 + "description": "矛盾描述", // LLM生成的矛盾说明 + "detected_at": "2026-07-13T14:30:52", // 检测时间 + "status": "pending" // pending | resolved | dismissed +} +``` + +## 冲突生命周期 + +1. **pending** — `detect_memory_conflicts()` 检测到并存入 +2. **resolved** — 人工确认后由外部逻辑更新 +3. **dismissed** — 确认为非真实矛盾后由外部逻辑更新 + +## 写入保证 + +- `_write_conflicts_to_queue()` 写入前检查 id 去重，不会重复追加同一冲突 +- 新冲突追加到队列尾部，保持 `pending` 状态 + +## 调用链路 + +``` +detect_memory_conflicts(journal_path, zhiyi_token) + → 读取 journal + 织忆 recall + → LLM 判断是否有直接矛盾 + → 有 → _write_conflicts_to_queue([冲突]) + → 返回冲突列表 +``` + +## 使用示例 + +```python +from daemon import detect_memory_conflicts, _write_conflicts_to_queue + +zhiyi_token = "zhiyi-dev-key-2026" +journal_path = "/home/muc/.hermes/daemon/journal.jsonl" + +conflicts = detect_memory_conflicts(journal_path, zhiyi_token) +for c in conflicts: + print(f"[{c['id']}] {c['description']}") +``` \ No newline at end of file diff --git a/skills/zhiyi/zhiyi/SKILL.md b/skills/zhiyi/zhiyi/SKILL.md index 203e9db7..dd5dc6c6 100644 --- a/skills/zhiyi/zhiyi/SKILL.md +++ b/skills/zhiyi/zhiyi/SKILL.md @@ -2,9 +2,8 @@ tags: [zhiyi] name: zhiyi description: "织忆 (MemoryWeave) 聚合技能 — API 客户端 + 开发工作流 + 运维规范。含 commit/recall API、数据架构、部署验证、Go 方法论。" -version: 11.34 -author: 小唯 A06 -updated: 2026-07-13（memory_recall.py 代码审查5个bug修复+Soulful脏数据清理+三系统全面检查结果） +version: 11.36 +updated: 2026-07-13（画像可执行化 v2 + 参考项目对比分析） --- @@ -16,6 +15,17 @@ updated: 2026-07-13（memory_recall.py 代码审查5个bug修复+Soulful脏数 - bge-embed: 8000端口正常，Rust IPC sidecar: 正常 - **结论：完全健康，无问题** +**2026-07-13 daemon → TencentDB 集成（新增）**： +- `daemon.py` 的 `tddb_capture(reflection_dict, state, ctx)`：deep tick 时自动 capture 到 :8420，形成人格记忆积累 +- `_sync_soulful_to_llm_context()` 内每 tick 调用 `/search/memories`，提取 `latest_persona` 写入 `ctx["tddb"]` +- `save_llm_context()` 显式 `ctx.get("tddb", {})` 加入 llm_context.json 输出 +- **注入链路**：daemon tick → TencentDB sync → llm_context.json → Hermes 插件 prefetch 时注入 + +**2026-07-13 画像可执行化**：Soulful `user-profile.json` v2 的 `behavior_rules`（8条可执行行为规范）经 `daemon.py` 的 `save_llm_context()` 拼接为 `【规则类型】规则内容` 格式字符串数组，写入 `llm_context.json` 的 `system_prompt_snippets` 字段，织忆 prefetch 自动携带至每轮对话。效果：模型直接看到并遵守牧尘的行为规范，而非需要从元数据推断。 + +验证：`python3 -c "import json; print(len(json.load(open('~/.hermes/llm_context.json')).get('system_prompt_snippets',[])))"` → 应输出 8。 +- **验证**：`python3 -c "import json; print(json.load(open('~/.hermes/llm_context.json')).get('tddb'))"` + ### TencentDB Gateway - 端口8420，进程PID 575095，uptime 28910s（~8h） - Pipeline: 4 tasks consumed/completed，0 failed @@ -419,6 +429,19 @@ curl -s -X POST -H "X-API-Key: zhiyi-dev-key-2026" \ http://localhost:7821/api/v1/commit ``` +### ✅ time_decay_recall — daemon 侧时间衰减重排序（2026-07-13 新增） +位于 `~/.hermes/scripts/daemon.py`，综合分 = recall_score × 0.6 + decay_score × 0.4 +decay_score = max(0.3, 1.0 - days_since_update × 0.015)，60天+降至 0.3 地板 + +```python +from daemon import time_decay_recall +results = time_decay_recall("牧尘偏好", top_k=5) +# 返回: [{content, score, timestamp, final_score, decay_score, days_since_update, ...}] +``` + +**特点**：原始取 top_k × 2 条，重排序后返回 top_k 条；API 异常 / non-200 → 返回空列表，不抛异常 +**验证脚本**：`/tmp/hermes-verify-time_decay_recall.py`（10/10 passed） + # ✅ recall — 语义搜索（POST JSON body，不是 GET query params！） # ⚠️ 常见错误：curl "http://.../recall?query=xxx" → {"error":"invalid body"} # 正确用法：-d '{"query":"关键词","top_k":5}' 的 POST 形式 @@ -1256,7 +1279,8 @@ python3 ~/.hermes/scripts/wiki_curator.py --dir ~/mc/小唯/ --llm --dry-run - **⚠️ 共享 Gitea remote 覆盖风险（2026-07-13）**：`references/shared-gitea-remote-risk.md` — `mc` 和 `~/.hermes` 共用同一 remote，force push 会互相覆盖。解决方案。 - **⭐ ColaOS 竞品分析（2026-07-09）**：`references/competitor-colaos-20260709.md` — SPA 网站抓取方法 + 心迹/牵挂/无感感知设计详析。🔴 高优先级借鉴：情感层与任务记忆分离存储。 -- **⭐ 竞品架构对比（2026-07-01）**：`references/memory-os-7-layer-comparison.md` — Memory-OS 7 层记忆架构 vs 织忆完整对照。含信任评分、4 级降级、自动注入钩子、CREATIVE.md 隔离、强制注入 prompt 共 5 个可直接借鉴的设计点。用于织忆迭代时对标参考。 +- **⭐ 竞品架构对比（2026-07-01）**：`references/memory-os-7-layer-comparison.md` — Memory-OS 7 层记忆架构 vs 织忆完整对照。含信任评分、4 级降级、自动注入钩子、CREATIVE.md 隔离、强制注入 prompt 共 5 个可直接借鉴的设计点。 +- **⭐ 记忆系统对比分析（2026-07-13）**：`references/memory-system-comparison.md` — 克隆分析 yantrikdb/memos/honcho/agent-memory-skill 等 9 个参考项目，输出 P0-P2 优先级建议（Ebbinghaus遗忘/冲突检测/多信号评分/think Consolidation/主动触发器）用于织忆迭代时对标参考。 - **⭐ 自启动架构（2026-07-02）**：`references/systemd-auto-start.md` — 4 组件启动串行、binary 持久化位置、service 文件配置、ExecStartPre 自愈、重启后验证方法 - **⭐ /tmp/memoryweave 丢失恢复指南（2026-06-25）**：`references/tmp-memoryweave-recovery.md` diff --git a/skills/zhiyi/zhiyi/scripts/verify_time_decay_recall.py b/skills/zhiyi/zhiyi/scripts/verify_time_decay_recall.py new file mode 100644 index 00000000..4af870b2 --- /dev/null +++ b/skills/zhiyi/zhiyi/scripts/verify_time_decay_recall.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +"""Ad-hoc verification: time_decay_recall in daemon.py +Run: python3 /home/muc/.hermes/skills/zhiyi/zhiyi/scripts/verify_time_decay_recall.py +Expected: 10/10 passed +""" +import sys, os, unittest.mock as m + +sys.path.insert(0, '/home/muc/.hermes/scripts') +from daemon import time_decay_recall + +passed = 0 +failed = 0 + +def check(label, cond): + global passed, failed + if cond: + print(f" PASS {label}") + passed += 1 + else: + print(f" FAIL {label}") + failed += 1 + +print("\n=== time_decay_recall verification ===\n") + +# 1. Returns list +result = time_decay_recall('test', top_k=3) +check("returns list", isinstance(result, list)) + +# 2. Each item has required fields +if result: + item = result[0] + check("has final_score", 'final_score' in item) + check("has decay_score", 'decay_score' in item) + check("has days_since_update", 'days_since_update' in item) + check("has timestamp", 'timestamp' in item) + +# 3. Sorted descending by final_score +scores = [x['final_score'] for x in result] +check("sorted descending", scores == sorted(scores, reverse=True)) + +# 4. top_k respected +check(f"top_k respected (len={len(result)})", len(result) <= 3) + +# 5. Mock: old entry hits floor 0.3 +with m.patch('requests.post') as mock_post: + mock_post.return_value.status_code = 200 + mock_post.return_value.json.return_value = { + 'results': [ + {'content': 'fresh', 'score': 0.9, 'timestamp': '2026-07-01T00:00:00+08:00'}, + {'content': 'old', 'score': 0.9, 'timestamp': '2024-01-01T00:00:00+08:00'}, + ] + } + mocked = time_decay_recall('mock', top_k=5) + old_item = next(x for x in mocked if x['content'] == 'old') + check("old entry decay floor == 0.3", old_item['decay_score'] == 0.3) + +# 6. Mock: server error returns [] +with m.patch('requests.post') as mock_post: + mock_post.side_effect = Exception("boom") + err_result = time_decay_recall('fail', top_k=3) + check("exception returns []", err_result == []) + +# 7. Mock: non-200 returns [] +with m.patch('requests.post') as mock_post: + mock_post.return_value.status_code = 500 + mock_post.return_value.json.return_value = {} + check("non-200 returns []", time_decay_recall('bad', top_k=3) == []) + +print(f"\n=== {passed}/{passed+failed} passed ===") +sys.exit(0 if failed == 0 else 1) \ No newline at end of file diff --git a/soulful/user-profile.json b/soulful/user-profile.json index decad597..be149aca 100644 --- a/soulful/user-profile.json +++ b/soulful/user-profile.json @@ -1,6 +1,6 @@ { "version": 2, - "updated_at": "2026-07-13T12:30:00.000000+00:00", + "updated_at": "2026-07-13T14:00:45.524882+00:00", "behavior_rules": { "answer_format": "结论先行 → 数据支撑 → 行动建议。不废话、不科普、不加补丁式回答。", "code_quality": "修复后必须自测，不等用户测。遇到stderr先试3种方法，不行再报告障碍。", @@ -13,8 +13,16 @@ }, "communication_style": "简洁直接", "work_patterns": { - "peak_hours": ["09:00-12:00", "14:00-18:00"], - "focus_issues": ["会计凭证处理", "织忆系统", "cron自动化", "KOCR识别"] + "peak_hours": [ + "09:00-12:00", + "14:00-18:00" + ], + "focus_issues": [ + "会计凭证处理", + "织忆系统", + "cron自动化", + "KOCR识别" + ] }, "preferences": { "code_review": "改完必须自测再提交，不依赖用户测试", diff --git a/stock_backtest/news_20260713.txt b/stock_backtest/news_20260713.txt new file mode 100644 index 00000000..59ccbaee --- /dev/null +++ b/stock_backtest/news_20260713.txt @@ -0,0 +1,20 @@ +📰 每日宏观+持仓摘要 + +【大盘】2026-07-13 08:00 +📉 上证: 3996.16 (-1.00%) +📉 沪深300: 4.83 (-1.77%) +📈 五粮液: 73.69 (+3.94%) + +【五粮液持仓信号】⭐ 关注 + +📈 布伦特原油: 78.48 (+2.47) +【宏观风险提示】 +• 大盘若跌破关键均线 → 空仓信号加强 +• 白酒消费数据持续低迷 → 基本面承压 +• 人民币贬值预期 → 外资流出白酒板块压力 + +【五粮液状态】 +价格: 73.69 (+3.94%) +MA20下方 → 空仓信号，等待金叉 + +小唯股票投研 · 每日新闻 \ No newline at end of file diff --git a/stock_backtest/paper_trades_000858.json b/stock_backtest/paper_trades_000001.json similarity index 56% rename from stock_backtest/paper_trades_000858.json rename to stock_backtest/paper_trades_000001.json index 9c13897f..09e29ad5 100644 --- a/stock_backtest/paper_trades_000858.json +++ b/stock_backtest/paper_trades_000001.json @@ -1,10 +1,15 @@ { "strategy": "MA20突破", - "stock": "五粮液(000858)", + "stock": "平安银行(000001)", "start_date": "2026-07-12", "initial_capital": 100000, - "current_capital": 100000, - "positions": [], + "current_capital": 7.0200000000040745, + "positions": [ + { + "shares": 9487, + "avg_cost": 10.54 + } + ], "closed_trades": [], "stats": { "total_trades": 0, @@ -13,5 +18,7 @@ "total_pnl": 0, "win_rate": 0 }, - "notes": "模拟记录，非真实下单。等待MA20金叉信号再买入。" + "notes": "模拟记录，非真实下单。等待MA20金叉信号再买入。", + "last_signal": "买入", + "last_signal_date": "2026-07-13" } \ No newline at end of file