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ito-inference

Inspect the availability of model serving on a completed Itô compute booking and, when the canonical backend becomes available, hand off an explicitly confirmed serving manifest. Use after ito-compute has booked GPU nodes and the user asks for an OpenAI-compatible endpoint, ito-serve, hosted Kimi, or self-hosted open-weights inference. ECC implements no serving stack of its own.

ito-inference 是什么?

ito-inference is a Codex agent skill that inspect the availability of model serving on a completed Itô compute booking and, when the canonical backend becomes available, hand off an explicitly confirmed serving manifest. Use after ito-compute has booked GPU nodes and the user asks for an OpenAI-compatible endpoint, ito-serve, hosted Kimi, or self-hosted open-weights inference. ECC implements no serving stack of its own.

兼容平台~Claude CodeCodex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/ito-inference

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文档

Itô Inference

ito-inference is the sole canonical ECC skill for inference serving on Itô compute. Requests naming ito-serve route here; do not create or install a second ito-serve skill. ECC never SSHes to nodes, downloads weights, launches an engine, or exposes an endpoint; it never books, reserves, or spends.

Current production boundary

Managed serving is unavailable today. The ECC bridge exposes only login, auth, find, status, and explicitly gated evals. It has no serve verb. The canonical runtime documents inference only as an unsupported compatibility probe; ECC does not invoke or depend on it. The MCP surface exposes only auth, find, and status. The locally enforceable guarantee is that ECC rejects serve 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 runner, SSH helper, browser workflow, purchase endpoint, or any untracked local ito-serve draft.

Required entitlement

When serving 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 serving eligibility bound to the authenticated account, booking, GPU topology, region, fabric, term, and model policy. Expired, revoked, mismatched, incomplete, or already-released bookings fail closed before confirmation.

Future CLI and API contract

The intended command name is serve; inference may remain only as an explicitly deprecated compatibility alias after the production contract lands. The future handoff must be equivalent to:

ecc ito serve \
  --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 revision, engine and version, quantization, tensor/pipeline topology, endpoint exposure policy, artifact checksums, storage ceiling, runtime limits, optional TTFT/TPOT objectives, and maximum incremental cost. No raw API key, SSH key, node password, or bearer token 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. Serving data must include stable booking, workload, manifest, and idempotency IDs plus a state enum; it must not claim an endpoint is live until health and model checks pass. Errors must include a stable code and safe message without secrets.

Confirmation and execution gates

Before workload creation, require all of the following:

  1. Fresh entitlement and serving eligibility from the canonical backend.
  2. A reviewable immutable manifest and deterministic digest.
  3. 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.
  4. A caller-supplied idempotency key reserved atomically with the workload.
  5. Server-side fabric, capacity, model-policy, storage, and cost validation.

Authentication is identity, not workload authority. A login, API key, quote, or completed booking never substitutes for the serving 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, cancel, and cleanup operations. 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 workload merely because the first response was lost. A revoked credential stops polling and returns control to the originating agent without starting login automatically.

Only report ready after endpoint health, model identity, and canary inference all pass. Report intermediate and terminal failure states honestly. Cleanup must be observable and must not release or modify the underlying booking unless that separate economic action was explicitly authorized.

Proposed backend stages

These stages describe the future backend, not code that exists in ECC:

  1. Verify entitlement, topology, fabric, and cost gates.
  2. Fetch checksum-pinned weights into backend-managed storage.
  3. Emit and validate a reviewable topology/engine plan.
  4. Launch through the provider control plane, never direct root SSH from ECC.
  5. Warm up, test health and model identity, run an SLO canary, then register the endpoint and redacted configuration.

Until every gate and lifecycle operation above exists in the canonical runtime, this skill remains a fail-closed availability check and documentation handoff.

Individual skills in this repo

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

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

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.

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