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ito-compute

Query live GPU inventory, submit an authenticated Itô fixed-rate RFQ, inspect RFQ or procurement status, revoke device credentials, and run explicitly gated node qualification through the separately installed canonical CLI. Use when a user asks to find H100/H200 capacity, request a fixed compute rate, check Itô compute status, validate GPU nodes, revoke Itô access, or rent or purchase GPU compute and needs the supported boundary explained.

Was ist ito-compute?

ito-compute is a Claude Code agent skill that query live GPU inventory, submit an authenticated Itô fixed-rate RFQ, inspect RFQ or procurement status, revoke device credentials, and run explicitly gated node qualification through the separately installed canonical CLI. Use when a user asks to find H100/H200 capacity, request a fixed compute rate, check Itô compute status, validate GPU nodes, revoke Itô access, or rent or purchase GPU compute and needs the supported boundary explained.

Funktioniert mit~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/ito-compute

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Dokumentation

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ito-compute-cli is currently unpublished.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:

    ecc ito find \
      --gpu h200 \
      --count 8 \
      --nodes 1 \
      --gpus-per-node 8 \
      --days 30 \
      --storage-tb 1 \
      --start-window 2099-08-15 \
      --max-rate 3.00 \
      --form-factor bare_metal \
      --contract-type reservation \
      --fabric infiniband \
      --region us-east-1
    
  5. Run ecc ito status to inspect RFQs and procurement orders. After an ambiguous transport failure, check status before repeating find.

  6. When a quote is ready and the buyer explicitly approves, accept it:

    ecc ito accept rfq_<ticket-id>
    

    This routes the ticket to the desk for human review. It does not move funds or reserve capacity. Do not accept without explicit buyer authority.

  7. Run ecc ito logout when the user explicitly asks to revoke this device. The canonical CLI keeps the local credential when remote revocation fails so the operator can retry; never delete the token manually as a substitute.

Inventory prices are indicative. An RFQ is not reserved capacity. Treat a rate as fixed only when the canonical result contains a non-null firm quote.

Live node qualification

ecc ito evals exposes the canonical CLI's narrow live adapter to a separately installed sixtytwo-cli==0.3.33. It does not expose local fixture execution through ECC. Require all of the following before invoking it:

  • operator authorization to contact the named nodes;
  • ITO_ENABLE_SIXTYTWO_LIVE=1;
  • --live-sixtytwo;
  • an explicit node list; and
  • an existing absolute config directory containing sixtytwo.yaml.
ecc ito evals \
  --cluster clu_prod_example \
  --live-sixtytwo \
  --nodes gpu-01,gpu-02 \
  --config-dir /absolute/path/to/qualification-config

The canonical adapter can run only the pinned version check and sixtytwo test --full against the explicit nodes. It cannot rent, launch, recover, repair, reset, purchase, or order resources. ECC does not forward ITO_API_KEY or model/cloud credentials into node qualification.

MCP workflow

Build the canonical package, then configure the stdio server with an absolute path:

{
  "mcpServers": {
    "ito-compute": {
      "command": "node",
      "args": [
        "/absolute/path/to/ito-cloud-runtime/cli/ito-compute-cli/dist/bin/ito-mcp.js"
      ]
    }
  }
}

The server exposes only:

  • ito_auth
  • ito_find
  • ito_status
  • ito_accept

ito_auth validates existing credentials; it does not start device login. Use ito_auth, gather explicit buyer authority and every hard constraint, call ito_find, then poll with ito_status when needed. When a quote is ready and the buyer explicitly approves, call ito_accept with the ticket id. Desk quotes are usually indicative and nonbinding until the desk confirms; the result carries quote_class.

Rent or purchase semantics

find submits an RFQ and may return a firm quote, but it does not rent, purchase, reserve, provision, or move funds. accept routes a quote to the desk for human review; it does not move funds or reserve capacity. status is read-oriented, though the provider endpoint may reconcile an existing procurement order. The passive dashboard link in ECC help is a separate user-operated web route; do not open or operate it as a substitute for a missing CLI capability.

Unsupported operations

The supported client surface cannot lock quotes, reserve capacity, execute workloads, or serve inference. accept is a desk handoff, not a purchase. The MCP server does not expose qualification; use the explicit CLI command above. Do not invent additional tools or a purchase path. Do not substitute a browser or fixture when the local CLI is missing or a live operation fails. Report the missing capability and stop.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

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affaan-m/content-engine

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affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

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