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Agent skill

Ito Inference

by affaan-m in affaan-m/ECC

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.

MITAuto-check passedAI & LLM Engineering

Install Ito Inference

skills CLI
$ npx skills add affaan-m/ECC --skill ito-inference -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC ito-inference --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ito-inference .claude/skills/ito-inference && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ito-inference
GitHub stars
276k
Used in
1 other repo
Token cost
~1.5k tokens
SKILL.md length
760 words
Files
1
Skills in repo
683
Repo updated
First seen
Licence
MIT

At a glance

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.

  • Works in 5 steps: Fresh entitlement and serving… → A reviewable immutable manifest and… → A separate single-use confirmation bound… → …
  • Tasks that involve LLM inference and serving
  • SKILL.md covers Current production boundary, Required entitlement, Future CLI and API contract and Confirmation and execution gates, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ito Inference is an agent skill from affaan-m/ECC. 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.

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering LLM inference and serving. It works with Kimi and OpenAI. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • Tasks that involve LLM inference and serving

Example prompts

  • “/ito-inference”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  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
  4. A caller-supplied idempotency key reserved atomically with the workload.
  5. Server-side fabric, capacity, model-policy, storage, and cost validation.

What it can do on your machine

Read from SKILL.md and the folder at commit 4eb71d9. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ito Inference loads about 1.5k tokens when it runs. Until then it costs about 99 tokens; SKILL.md has 760 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~99
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit 4eb71d9, republished under its MIT licence (© affaan-m). 760 words, ~1,535 tokens.

Download SKILL.mdSave it as .claude/skills/ito-inference/SKILL.md (or your agent's skills folder).
name
ito-inference
description
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.
metadata.origin
ECC
metadata.status
scaffold
metadata.aliases
ito-serve, hosted-open-weights

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:

sh
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.

Show full SKILL.md (340 more words)Show less

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.

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/ito-inference of affaan-m/ECC.

Open the folder on GitHubat commit 4eb71d9

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in affaan-m/ECC, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Ito Inference next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Ito Inference compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ito Inference this skillaffaan-m/ECC276k1 repos~1.5kAutomated safety check: PassMIT
Aider DelegateamElnagdy/delegate-skills2.3k2 repos~3kAutomated safety check: PassMIT
Perfupraullenchai/Rapid-MLX4k—~1.6kAutomated safety check: NotesCustom licence
SGLang Model Day-0 SupportBBuf/AI-Infra-Auto-Driven-SKILLS938—~2.3kAutomated safety check: PassNone
Model Serving MinefieldBlackwellboy/model-serving-minefield135—~2.1kAutomated safety check: PassMIT
Bridgic LLMsbitsky-tech/bridgic155—~839Automated safety check: NotesMIT

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Works with

Categories

Questions about Ito Inference

What does Ito Inference do?

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. Ito Inference is an agent skill from affaan-m/ECC. 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.

When should I use Ito Inference?

Ito Inference fits situations like: tasks that involve LLM inference and serving.

How do I install Ito Inference in Claude Code?

Run `npx skills add affaan-m/ECC --skill ito-inference -a claude-code`. Or copy the skill folder (skills/ito-inference in affaan-m/ECC) into .claude/skills/ito-inference in your project. Claude Code loads it when a task matches its description.

How do I install Ito Inference in Codex?

Run `npx skills add affaan-m/ECC --skill ito-inference -a codex`. Or copy the skill folder (skills/ito-inference in affaan-m/ECC) into .agents/skills/ito-inference in your project. Codex loads it when a task matches its description.

Can I use Ito Inference in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill ito-inference -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ito-inference, .gemini/skills/ito-inference, .github/skills/ito-inference and .opencode/skills/ito-inference in your project.

What does Ito Inference need to run?

SKILL.md names no scripts, command-line tools or credentials: Ito Inference is instructions for the agent only.

Does Ito Inference access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Ito Inference safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Ito Inference use?

Ito Inference is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ito Inference use?

About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Ito Inference?

Skills that share tags, products or a category with Ito Inference: Aider Delegate (amElnagdy/delegate-skills, 2.3k stars), Perfup (raullenchai/Rapid-MLX, 4k stars), SGLang Model Day-0 Support (BBuf/AI-Infra-Auto-Driven-SKILLS, 938 stars) and Model Serving Minefield (Blackwellboy/model-serving-minefield, 135 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ito Inference?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 276,111 GitHub stars. The repository holds 683 skills in this directory. The repository was last updated on October 10, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.