ito-training
Helps your agent manage machine learning training. It checks availability and hands off training data when the main system is ready.
What it does
This tool checks if you can train AI models on a computer you've booked. It makes sure everything is ready before you start.
When to use it
- ✓After booking a computer with GPUs for model training.
- ✓When you want to ensure the training system is available.
- ✓Before starting training to avoid errors.
Example of what to ask your AI
“Check if AI training is ready to start on my booked computer.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It connects to the computer booking system and verifies if the training environment is set up and available. If everything is okay, it lets you know.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add affaan-m/ECC --skill ito-trainingOr download just the file:
curl -L https://raw.githubusercontent.com/affaan-m/ECC/main/skills/ito-training/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Itô Training
ito-training is the canonical ECC skill for training on Itô compute. ECC
never runs a trainer, scheduler, or data pipeline of its own; it never books,
reserves, or spends. This skill chains off a completed booking from
ito-compute.
Current production boundary
Managed training is unavailable today. The ECC bridge exposes only login,
logout, auth, find, status, and explicitly gated evals. It has no
train verb, and the canonical CLI's run verb and desk training-run
backend remain scaffolds. The locally enforceable guarantee is that ECC rejects
train before resolving or spawning the credential-bearing canonical client.
Therefore stop before authentication or any command invocation. Report the missing capability and return to the originating agent. Never substitute a local trainer, SSH helper, browser workflow, or purchase endpoint.
Required entitlement
When training is implemented, its first gate is a server-verified completed booking. Harness memory, an RFQ, a quote, node IPs, or SSH access are not proof of entitlement. The backend must return fresh training eligibility bound to the authenticated account, booking, GPU topology, region, fabric, and term. Expired, revoked, mismatched, incomplete, or already-released bookings fail closed before confirmation.
Future CLI and API contract
The intended command name is train. The future handoff must be equivalent to:
ecc ito train \
--booking <server-verified-booking-id> \
--manifest <absolute-reviewed-json-file> \
--confirmation-ref <opaque-non-authorizing-reference> \
--idempotency-key <stable-retry-key> \
--json
The reviewed manifest must identify the model size and revision, data references with decontamination provenance, training target, post-training recipe, budget ceiling in USD, checkpoint policy, and maximum incremental cost. No raw API key, SSH key, node password, bearer token, or dataset credential belongs in arguments, manifests, logs, MCP results, or chat.
The client must canonicalize the manifest path, reject symlinks, open a regular file without following links, require appropriate ownership and restrictive permissions, enforce a bounded size, and hash bytes from the opened descriptor. That digest must exactly equal the digest bound into confirmation before any workload mutation. A path swap, digest mismatch, oversized file, or mutable unsafe file fails closed.
The canonical API—not ECC—must own workload creation and return structured JSON
with ok, live_api_contacted, notice, and either data or error.
Training data must include stable booking, run, manifest, and idempotency IDs
plus a state enum. Errors must include a stable code and safe message without
secrets.
Confirmation and execution gates
Before workload creation, require all of the following:
- Fresh entitlement and training eligibility from the canonical backend.
- A reviewable immutable manifest and deterministic digest.
- A separate single-use confirmation bound to account, action, manifest, and cost, with a short expiry and replay protection. CLI arguments carry only an opaque, non-authorizing confirmation reference; the server resolves and consumes the bearer capability out of band.
- A caller-supplied idempotency key reserved atomically with the run.
- Server-side fabric, capacity, data-policy, checkpoint-storage, and cost validation, including the manifest's budget ceiling.
Authentication is identity, not workload authority. A login, API key, quote, or completed booking never substitutes for the training confirmation. Inspection and plan generation must not create a workload. Cancel and cleanup are separate mutations with their own scoped confirmation and idempotency boundaries.
Lifecycle and recovery
The production surface is incomplete until the same canonical client exposes tenant-scoped status, logs, metrics, checkpoint listing, cancel, and cleanup. Every operation needs bounded connect and overall timeouts, revocation-aware errors, and structured output. After an ambiguous transport failure, query status by the idempotency key before retrying; never create a second run merely because the first response was lost. A revoked credential stops polling and returns control to the originating agent without starting login automatically.
Report stage gates honestly; never override a failed eval gate. Cleanup must be
…
FAQ
What is 'AI training'?
It's the process of teaching an artificial intelligence model to perform a task by showing it many examples.
What is 'ito-compute'?
It's the service that books powerful computers (with GPUs) for you to use.
Can I train models directly with this tool?
No, this tool only checks if the training environment is ready. The actual training is done elsewhere.
Details
Original description by the author
Inspect the availability of ML training on a completed Itô compute booking and, when the canonical backend becomes available, hand off an explicitly confirmed training manifest. Use after ito-compute has booked GPU nodes and the user wants pre-training, fine-tuning, or RL on that metal. ECC implements no training stack of its own.
repo: affaan-m/ECC
Popularity
- GitHub stars
- 280k
- Ranking position
- #221
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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