Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gongyijie85/dsh-ecc --skill ito-traininggit clone --depth 1 https://github.com/gongyijie85/dsh-eccWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gongyijie85/dsh-ecc/ito-training)<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/ito-training"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/ito-training/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/ito-training"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/ito-training.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00068 | $0.01267 |
| Opus 5 | $0.00034 | $0.00633 |
| Sonnet 5 | $0.00014 | $0.00253 |
| Haiku 4.5 | $0.00007 | $0.00127 |
Grade A, and why
ito-training scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- ito-training — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on 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.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 124 lines · 68 tokens per session scan A 43ce53548557
ito-training is a skill published in the GitHub repository gongyijie85/dsh-ecc (6 stars, last pushed 2d ago), licensed MIT. It adds 68 tokens to every session and 1,267 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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