ito-compute

ito-compute is a skill for Codex from gongyijie85/dsh-ecc. It costs 90 tokens per session (1,409 once invoked), scanned A, original, MIT.

A connector for finding GPU capacity, requesting fixed-rate compute quotes, checking procurement status, revoking device credentials, and qualifying nodes through Itô's official tools.

In plain words
What is it for?
It is for locating H100 or H200 capacity, submitting compute requests, checking their status, managing device credentials, and running approved node checks.
Why use it?
It gives a coding agent a defined way to inspect and request compute resources while keeping authentication and credential handling in the canonical client.

Skill for Codex

Written for Codex: agents/openai.yaml present.

Good fit It is for locating H100 or H200 capacity, submitting compute requests, checking their status, managing device credentials, and running approved node checks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gongyijie85/dsh-ecc/ito-compute
Install

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.

Any agent
npx skills add gongyijie85/dsh-ecc --skill ito-compute
Clone the repo
git clone --depth 1 https://github.com/gongyijie85/dsh-ecc

Made for: Codex.

Wrote 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.

agentmods badge for ito-compute

README.md
[![agentmods](https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/ito-compute/github.svg)](https://agentmods.dev/skills/gongyijie85/dsh-ecc/ito-compute)
Your own site
<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/ito-compute"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/ito-compute/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.

agentmods 80×15 button for ito-compute

Your own site · 80×15
<a href="https://agentmods.dev/skills/gongyijie85/dsh-ecc/ito-compute"><img src="https://agentmods.dev/badge/skills/gongyijie85/dsh-ecc/ito-compute.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,409 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00090 $0.01409
Opus 5 $0.00045 $0.00705
Sonnet 5 $0.00018 $0.00282
Haiku 4.5 $0.00009 $0.00141

Measured 7d ago against content hash 2c3570b5beee, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

ito-compute 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 7d 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.

skills/ito-compute/SKILL.md · 152 lines

How it starts

The opening of the file, as written. The whole thing — 152 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Itô Compute

Use the canonical Itô compute CLI or MCP server. ECC does not implement a parallel client, local simulation, reservation, workload runner, or inference server. ECC itself does no browser automation.

Install the canonical local package

ito-compute-cli is currently unpublished. Build it from its canonical repository instead of using npx, npm exec, or an unverified package:

git clone https://github.com/Ito-Markets/ito-cloud-runtime.git
cd ito-cloud-runtime/cli/ito-compute-cli
npm ci
npm run check

Set ECC_ITO_CLI_EXECUTABLE to the explicit absolute built entry:

/absolute/path/to/ito-cloud-runtime/cli/ito-compute-cli/dist/bin/ito.js

ECC never discovers this credential-bearing client through PATH. ecc ito login performs device authorization and never inherits ITO_API_KEY. The validation-only auth, plus find and status, forward ITO_API_KEY directly when configured; ITO_AUTH_MODE=legacy is not required. Never put a key or token in arguments, tracked files, MCP results, logs, or chat.

CLI workflow

  1. Run ecc ito login before the first operation. ECC delegates this to the canonical CLI's device authorization, which opens the Itô verification page by default and persists a device token in macOS Keychain. Use ecc ito login --no-browser to suppress the page handoff. ECC itself does no browser automation. If the originating agent cannot complete the signed-in browser step, hand the exact command to the user; after approval finishes, return to the originating task and continue with ecc ito auth. Device tokens use macOS Keychain by default. File-token fallback is explicit and its directory and token file must remain owner-only (0700 and 0600).
  2. Run ecc ito auth to validate existing credentials; it never starts login and rejects --no-browser.
  3. Before ecc ito find, obtain explicit buyer authority to submit an RFQ.
    • Require gpu, count, whole days, max-rate, nodes, gpus-per-node, storage-tb, start-window, form-factor, contract-type, fabric, region, and the split-fill decision.
    • Require count == nodes * gpus-per-node; never derive topology.
    • Use any only when the buyer explicitly accepts any fabric or region.
    • Omitted --allow-split means false.
  4. Run the live RFQ command:

Read the full file on GitHub · 152 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 7d ago First seen · 152 lines · 90 tokens per session scan A 2c3570b5beee

Subscribe to this mod's changes

ito-compute is a skill published in the GitHub repository gongyijie85/dsh-ecc (7 stars, last pushed 3d ago), licensed MIT. It adds 90 tokens to every session and 1,409 once invoked, about $0.0005 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.

Related

Other skills, from other repositories

manage-taskboard

Manage work in the native DeepSeek Harness Taskboard with exact task ids and optimistic versions. Use when an Agent must inspect project work, claim an eligible todo, record progress or blockers, verify an implementation, submit it for human review, or release its own claim; also use when a human asks how to accept…

shengsheng90/DSH-taskboard · 88 tokens

bio-workbench

A set of rules for running reproducible bioinformatics analyses, which study biological data with software. It uses small self-contained scripts, recorded inputs and outputs, environment details, and version history so results can be repeated.

poplarity/dsh-science-workbench · 116 tokens

visionary-cli

Analyze images with DeepSeek's vision model via the visionary-server CLI. Use this whenever the user provides an image, photo, screenshot, or document with images - run vision to look at it rather than guessing.

xlight/deepseek-visionary · 46 tokens

cvqnn

Skill for understanding, using, and implementing the Continuous Variable Quantum Neural Network (CVQNN) for binary classification via the CVQNNAlgorithm class, CVSimulator, and CVClassifier. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.

unitarylab/quantum-practices · 61 tokens

vqc

Skill for understanding, using, and implementing the Variational Quantum Classifier (VQC) for Iris dataset classification with data re-uploading and Parameter Shift Rule via the VQCAlgorithm class. Skill-first for covered code generation, runnable examples, execution, debugging, validation, and fixed workflows.

unitarylab/quantum-practices · 61 tokens

pennylane

PennyLane - A versatile quantum machine learning library that supports hybrid quantum-classical computations.

unitarylab/quantum-practices · 23 tokens