Official agent skill

Model Routing Golden

by github in github/gh-aw

Maintain model-routing audit golden fixtures and expected outputs.

OfficialMITAuto-check passedAI & LLM Engineering

Install Model Routing Golden

skills CLI
$ npx skills add github/gh-aw --skill model-routing-golden -a claude-code

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

GitHub CLI
$ gh skill install github/gh-aw model-routing-golden --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/github/gh-aw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/model-routing-golden .claude/skills/model-routing-golden && 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
model-routing-golden
GitHub stars
5.4k
Token cost
~453 tokens
SKILL.md length
213 words
Files
1
Skills in repo
54
Repo updated
First seen
Licence
MIT

At a glance

Maintain model-routing audit golden fixtures and expected outputs.

  • Tasks that involve Model routing and gateways
  • SKILL.md covers Verify, Refresh expected outputs and Add or replace a fixture
  • Calls make, python3 and gh

What it does

Model Routing Golden is an agent skill from github/gh-aw, published by the product's own GitHub organization. Maintain model-routing audit golden fixtures and expected outputs.

Its SKILL.md is about 450 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 Model routing and gateways. It works with GitHub. The repository describes itself as: GitHub Agentic Workflows. The licence is MIT.

When your agent uses it

  • Tasks that involve Model routing and gateways

Example prompts

  • “/model-routing-golden”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit e19bf2b. 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

    Shell commands in SKILL.md call:

    • make
    • python3
    • gh

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

  • Network

    No URLs in SKILL.md. Its commands use gh, which can reach the network depending on how they are called.

    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

Model Routing Golden loads about 453 tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 213 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~22
When it runs · the whole SKILL.md, loaded when a task matches
~453

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 github/gh-aw at commit e19bf2b, republished under its MIT licence (© github). 213 words, ~453 tokens.

Download SKILL.mdSave it as .claude/skills/model-routing-golden/SKILL.md (or your agent's skills folder).
name
model-routing-golden
description
Maintain model-routing audit golden fixtures and expected outputs.

Model-Routing Golden Fixtures

Use this skill when a model-routing golden test fails, analysis output changes intentionally, or a new routed workflow run needs coverage.

Verify

Run make verify-model-routing-golden (or python3 scripts/model-routing-golden.py --verify) before changing fixtures. Treat a mismatch as a behavior change to investigate, not a reason to regenerate immediately.

Refresh expected outputs

When analysis behavior intentionally changes, run make update-model-routing-golden. Review the complete diff for each expected*.json file and confirm that routing status, classifier attribution, AWF credits, token totals, warnings, and subagent costs match the intended behavior. Keep separate legacy goldens when missing artifacts cause documented information loss. Do not regenerate outputs merely to silence an unexplained failure.

Add or replace a fixture

Capture a run with:

bash
python3 scripts/model-routing-golden.py --run <id-or-url> --case <case-name>

Use --replace only when intentionally updating an existing source run. The tool downloads through the local gh aw logs artifact path, minimizes and redacts its files, and refuses to write if analysis on the fixture differs from analysis on the full download. Use --source-dir to exercise capture against the synthetic downloaded-run fixture without GitHub access.

Add the case and its source scenario to modelRoutingGoldenCases and the fixture README. Then run make update-model-routing-golden, inspect all generated files, and run make verify-model-routing-golden. Retain the established file layout and logs/ ignore exception so raw proxy-log fixtures remain tracked.

© github, 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 .github/skills/model-routing-golden of github/gh-aw.

Open the folder on GitHubat commit e19bf2b

Compare with similar skills

Model Routing Golden 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.

Model Routing Golden compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Model Routing Golden this skillgithub/gh-aw5.4k—~453Automated safety check: PassMIT
Running Claude Code Via Litellm Copilotaiskillstore/marketplace433—~2.4kAutomated safety check: PassNone
Embeddings via 9Routerdecolua/9router31k—~604Automated safety check: PassMIT
OpenWork Model Alias Managerdifferent-ai/openwork24k—~1.1kAutomated safety check: PassCustom licence
TriageTalAter/annyang6.8k—~810Automated safety check: NotesMIT
Esmfold2JimLiu/science-skills2284 repos~2.5kAutomated safety check: PassApache-2.0

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Works with

Questions about Model Routing Golden

What does Model Routing Golden do?

Maintain model-routing audit golden fixtures and expected outputs. Model Routing Golden is an agent skill from github/gh-aw, published by the product's own GitHub organization. Maintain model-routing audit golden fixtures and expected outputs.

When should I use Model Routing Golden?

Model Routing Golden fits situations like: tasks that involve Model routing and gateways.

How do I install Model Routing Golden in Claude Code?

Run `npx skills add github/gh-aw --skill model-routing-golden -a claude-code`. Or copy the skill folder (.github/skills/model-routing-golden in github/gh-aw) into .claude/skills/model-routing-golden in your project. Claude Code loads it when a task matches its description.

How do I install Model Routing Golden in Codex?

Run `npx skills add github/gh-aw --skill model-routing-golden -a codex`. Or copy the skill folder (.github/skills/model-routing-golden in github/gh-aw) into .agents/skills/model-routing-golden in your project. Codex loads it when a task matches its description.

Can I use Model Routing Golden 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 github/gh-aw --skill model-routing-golden -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/model-routing-golden, .gemini/skills/model-routing-golden, .github/skills/model-routing-golden and .opencode/skills/model-routing-golden in your project.

What does Model Routing Golden need to run?

Going by SKILL.md and its folder, Model Routing Golden needs the command-line tools its instructions call (make, python3 and gh). Our summary lists: Python 3.

Does Model Routing Golden access the network?

SKILL.md contains no URLs. Its commands use gh, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Model Routing Golden 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 Model Routing Golden use?

Model Routing Golden 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 Model Routing Golden use?

About 453 tokens (SKILL.md is roughly 1.8k 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 Model Routing Golden?

Skills that share tags, products or a category with Model Routing Golden: Running Claude Code Via Litellm Copilot (aiskillstore/marketplace, 433 stars), Embeddings via 9Router (decolua/9router, 31k stars), OpenWork Model Alias Manager (different-ai/openwork, 24k stars) and Triage (TalAter/annyang, 6.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Model Routing Golden?

github (a GitHub organization, an official publisher) maintains it in github/gh-aw, which has 5,381 GitHub stars. The repository holds 54 skills in this directory. The repository was last updated on October 11, 2026.

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