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+---
+name: ito-inference
+description: Serve a model on a completed Itô compute booking through the canonical Itô backend. Use after ito-compute has booked GPU nodes and the user wants an OpenAI-compatible endpoint on that metal. Chains off a booking record; ECC implements no serving stack of its own.
+metadata:
+  origin: ECC
+---
+
+# Itô Inference
+
+Serve a model on rented Itô metal by delegating to the canonical Itô compute
+backend (Layer 0.2). ECC does not implement a parallel serving stack, launch
+adapter, or inference server, and does no browser automation. This skill chains
+off a **completed booking** produced by `ito-compute`; it never books, reserves,
+or spends.
+
+## Prerequisite
+
+A completed booking from the `ito-compute` skill: booking id, node IPs, SSH
+access, GPU SKU, node count, and fabric, already recorded in harness memory.
+Without a booking record, stop — this skill does not provision.
+
+## Delegation
+
+ECC calls the canonical backend through the `ecc ito` bridge; it never
+re-implements serving. Authenticate once with `ecc ito login` (device
+authorization; no key in arguments, files, logs, or chat), exactly as
+`ito-compute` documents.
+
+```sh
+ecc ito serve \
+  --booking <booking-id> \
+  --model <hf-model-id> \
+  [--quantization <scheme>] \
+  [--ttft-ms <target>] [--tpot-ms <target>]
+```
+
+The `--ttft-ms` / `--tpot-ms` SLO is optional; supplying it turns on
+disaggregated prefill/decode, which is off by default.
+
+## What the backend does (Layer 0.2)
+
+The desk backend, not ECC, runs the stages, and this skill only reports them:
+
+1. Fabric gate — never launch on unverified metal. Blocks below 80% of
+   fabric-expected bus bandwidth; advisory between 80% and 92%; fails loud on
+   silent NCCL socket fallback.
+2. Weights download and shard to the serving layout (desk-side sharded cache
+   keyed by model, quantization, TP degree).
+3. Topology plan (AIConfigurator): TP inside the NVLink domain, PP across nodes;
+   engine flags emitted as a reviewable file before launch.
+4. Launch (vLLM, Dynamo when disaggregating) under systemd, warmup, SLO canary,
+   and registration of the endpoint URL and config to Graphiti memory.
+
+## Unavailable today
+
+The serving operation is not yet wired: the canonical CLI's `inference` verb and
+the desk `serve-on-booking` backend are scaffolds. Until they land, this skill
+reports the missing capability and stops. Never substitute a local runner or a
+purchase endpoint.
diff --git a/skills/ito-training/SKILL.md b/skills/ito-training/SKILL.md
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+---
+name: ito-training
+description: Run an ML training job on a completed Itô compute booking through the canonical Itô backend. Use after ito-compute has booked GPU nodes and the user wants pre-training, fine-tuning, or RL on that metal. Chains off a booking record; ECC implements no training stack of its own.
+metadata:
+  origin: ECC
+---
+
+# Itô Training
+
+Run training work on rented Itô metal by delegating to the canonical Itô compute
+backend (Layer 0.3). ECC does not implement a parallel training stack, trainer,
+or scheduler, and does no browser automation. This skill chains off a
+**completed booking** from `ito-compute`; it never books, reserves, or spends.
+
+## Prerequisite
+
+A completed booking from the `ito-compute` skill (booking id, node IPs, SSH,
+GPU SKU, node count, fabric) in harness memory. Without one, stop.
+
+## Delegation
+
+ECC calls the canonical backend through the `ecc ito` bridge; it never
+re-implements training. Authenticate once with `ecc ito login`, as
+`ito-compute` documents. Never put a key or token in arguments, files, logs, or
+chat.
+
+```sh
+ecc ito train \
+  --booking <booking-id> \
+  --model-size <e.g. 8B> \
+  --data <data-ref> \
+  --target <capability> \
+  --budget-usd <ceiling> \
+  [--post-training sft|dpo|rlvr]
+```
+
+## What the backend does (Layer 0.3)
+
+The desk backend runs a staged, eval-gated pipeline; this skill reports stage
+gates and never overrides one:
+
+1. Data prep — manifest, dedup, decontamination against the eval suite;
+   150M-ladder decision job as the cheap pre-check for custom data.
+2. Parallelism and precision — selected from model size, node count, fabric;
+   wasteful combinations refused.
+3. Checkpointing and fault tolerance — async DCP, torchft; detect < 10 min,
+   resume < 15 min. Loss-spike restart is a proposed, human-gated action.
+4. Curriculum and eval gates — staged pretrain / mid-train / long-context /
+   post-training, each with a fixed eval battery; a failed gate stops the run.
+5. Post-training — SFT → DPO → RLVR (GRPO with DAPO stability fixes),
+   trainer/rollout separation with bounded staleness.
+
+Emits desk telemetry (goodput, interruption rate, checkpoint bandwidth) so the
+desk prices training blocks honestly.
+
+## Unavailable today
+
+Not yet wired: the canonical CLI's `run` verb and the desk `training-run`
+backend are scaffolds. Until they land, this skill reports the missing
+capability and stops. Never substitute a local trainer or a purchase endpoint.