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Brooks Harness

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hyhmrright
brooks-harness

Maintenance orchestrator for the brooks-lint plugin itself. Runs a sequential subagent pipeline — author → eval → QA → trigger-audit → release — to add or edit a skill, refresh the eval suite, keep the four manifests + all README translations + CHANGELOG + AGENTS/GEMINI in sync, audit trigger boundaries, and cut releases. Drives the five agents in .claude/agents/ (skill-author, eval-curator, consistency-qa, trigger-boundary-auditor, release-manager). Triggers when the maintainer asks to work ON brooks-lint itself: "add a new skill", "edit the brooks-debt guide", "update the eval suite", "fix the trigger descriptions", "make this change and validate it", "release brooks-lint", "bump and publish", and follow-ups: "re-run", "re-validate", "update that skill", "redo the audit", "do the X part again". Do NOT trigger for: USING the brooks-lint analysis skills on some target codebase (that's brooks-review / brooks-audit / brooks-debt / brooks-test / brooks-health / brooks-sweep); generic questions about brooks-lint that don't ask to change it; or maintenance of a different plugin.

Overview

Publisherhyhmrright
Repositorybrooks-lint
Skill namebrooks-harness
Stars
1.6K
Forks
72
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by hyhmrright on GitHub. Read the source before you install it.

Installation

Install the Brooks Harness AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/hyhmrright/brooks-lint/tree/main/.claude/skills/brooks-harness
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/hyhmrright/brooks-lint.git /tmp/brooks-lint
mkdir -p .claude/skills
cp -r /tmp/brooks-lint/.claude/skills/brooks-harness .claude/skills/brooks-harness
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Brooks Harness in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Brooks Harness on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Brooks Harness is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

brooks-lint — Maintenance Harness (Orchestrator)

This skill orchestrates work on the brooks-lint repo itself. It runs a sequential subagent pipeline: each stage is a dedicated agent defined in .claude/agents/. Spawn each with the Agent tool, subagent_type set to the agent name, and always model: "opus". Stages depend on each other in order, so this is a pipeline, not a parallel team.

Pipeline

[orchestrator]
   Phase 0  context check
   Phase 1  classify request → select stages
   Phase 2  run selected stages in order, with a QA loop-back:
            skill-author → eval-curator → consistency-qa ─(FAIL)→ back to author
                                              │ PASS
                                              ▼
                            trigger-boundary-auditor   (only if a description changed)
                                              ▼
                                      release-manager  (only if release requested)
   Phase 3  report + collect feedback

Phase 0 — Context check

Determine the run mode before doing anything:

  • _workspace/brooks-harness/ exists + maintainer asks to redo part of a prior run → partial re-run: invoke only the affected stage(s), reusing prior notes.
  • _workspace/brooks-harness/ exists + a fresh request → new run: move the old folder to _workspace/brooks-harness_prev/, start clean.
  • No _workspace/brooks-harness/ → initial run: create it.

Run notes and the QA report live under _workspace/brooks-harness/. The real artifacts are the repo files themselves — agents edit skills/, evals/, manifests directly; _workspace/ only holds the run's notes and the PASS/FAIL verdict for audit.

Phase 1 — Classify the request

Pick the minimal set of stages. The QA stage is never skipped — every change is gated.

RequestauthorevalQAtrigger-auditrelease
Add a new skill✓ (via new-skill scaffold)✓✓✓—
Edit skill / guide content✓if codes changed✓if description changed—
Edit _shared/ framework✓if risk defs changed✓——
Eval suite only—✓✓——
Fix trigger descriptions✓—✓✓—
Release——✓—✓
Full: change + release✓as needed✓if applicable✓

Phase 2 — Run the pipeline

Spawn each selected stage as a subagent in order. Pass each agent (a) the task contract and (b) the previous stage's summary. Agents write their summaries to _workspace/brooks-harness/; read them between stages.

  1. skill-author — creates/edits the content. For a brand-new skill it invokes the new-skill scaffold. Returns the list of files touched + convention-relevant choices (new risk codes, new Step numbers, changed description trigger phrases).
  2. eval-curator — if skill-author reported new/changed risk codes or modes, adds the paired happy-path + false-positive scenarios and runs npm run evals.
  3. consistency-qa (gate — never skipped) — runs npm run validate + npm test + npm run evals, then the cross-document sync checks (manifests, all version-bearing text files, CHANGELOG, AGENTS/GEMINI book count, eval count). Writes a PASS/FAIL verdict. On FAIL: loop back to the agent named in the verdict (author or eval-curator), fix, then re-run QA. Repeat once; if it still fails, stop and report to the maintainer.
  4. trigger-boundary-auditor — run only if a description field changed. It read-only audits the six shipped skills' trigger surfaces for false-triggering and routing collisions. Surface its findings; if it flags a real collision, loop back to skill-author.
  5. release-manager — run only if a release was requested, and only after QA PASS. Cuts the release via the release skill.

Phase 3 — Report & feedback

Report: stages run, files changed, QA verdict, trigger-audit findings (if any), and the release URL (if any). Then offer the maintainer a feedback opening: "Anything to adjust in the result, the agent roles, or the pipeline order?" Record accepted changes in the CLAUDE.md harness change-log table.

Conventions this harness enforces

  • All Agent calls use model: "opus" — harness quality tracks agent reasoning.
  • consistency-qa must be general-purpose (it runs npm scripts); the trigger-boundary-auditor is read-only.
  • No slash commands are created — short forms are auto-installed by the session-start hook.
  • Direct-to-main: changes push to main without a PR (per repo CLAUDE.md); the global simplify→review→commit gate still applies to non-doc edits, but skill/guide content is markdown and follows the validate gate instead.

Error handling

  • A stage that fails once is retried once with its error as input; a second failure stops the pipeline and reports to the maintainer (no silent skip).
  • QA FAIL never proceeds to release.
  • Conflicting data is reported with provenance, not deleted.
  • High-risk git ops (--no-verify, --force, history rewrites) require explicit maintainer authorization — release-manager stops and asks.

Test scenarios

Normal flow — "add a brooks-security skill": Phase 1 selects author+eval+QA+audit. skill-author runs new-skill brooks-security, creates SKILL.md (with a sibling-carving "Do NOT trigger for:" clause) + guide; eval-curator adds an S-code happy-path + a false-positive scenario; consistency-qa runs the gate → PASS; trigger-boundary-auditor confirms no collision with brooks-review/audit. Report lists files + PASS.

Error flow — QA FAIL on book-count drift: maintainer adds a thirteenth book but edits only source-coverage.md. consistency-qa's cross-doc check finds README still says "twelve" → FAIL, attributed to skill-author. Orchestrator loops back; skill-author updates README/AGENTS/GEMINI wording; QA re-runs → PASS. No release was requested, so the pipeline ends at Phase 3.

Frequently asked questions

What does the Brooks Harness AI skill do?

Maintenance orchestrator for the brooks-lint plugin itself. Runs a sequential subagent pipeline — author → eval → QA → trigger-audit → release — to add or edit a skill, refresh the eval suite, keep the four manifests + all README translations + CHANGELOG + AGENTS/GEMINI in sync, audit trigger boundaries, and cut releases. Drives the five agents in .claude/agents/ (skill-author, eval-curator, consistency-qa, trigger-boundary-auditor, release-manager). Triggers when the maintainer asks to work ON brooks-lint itself: "add a new skill", "edit the brooks-debt guide", "update the eval suite", "fi...

Why use Brooks Harness on TypingMind?

Because you install it once and use it with any model. Brooks Harness is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Brooks Harness in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hyhmrright/brooks-lint/tree/main/.claude/skills/brooks-harness. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Brooks Harness?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Brooks Harness?

As many as you like. As long as a model supports skills, you can use Brooks Harness with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Brooks Harness AI skill free?

Yes. It is published on GitHub by hyhmrright under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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