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Setup Codebase Harness

OrganizationPopular
AI-Builder-Club
setup-codebase-harness

Master skill — set up the full agent harness for any repo so an agent can work it reliably: legible (map-not-manual docs + custom lints), executable (one-command dev stack), verifiable (e2e gate + a verify-before-ship loop), plus commit hygiene and entropy control. Use when onboarding a new/unfamiliar codebase to agent-driven development — "set up the harness", "make this repo agent-ready", "harness this codebase".

Overview

PublisherAI-Builder-Club
Repositoryskills
Skill namesetup-codebase-harness
Stars
1.3K
Forks
159
Bundled files
Instructions only
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 AI-Builder-Club on GitHub. Read the source before you install it.

Installation

Install the Setup Codebase 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.
  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/AI-Builder-Club/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/setup-codebase-harness .claude/skills/setup-codebase-harness
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Setup Codebase 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 Setup Codebase 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 Setup Codebase 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.

Set up the codebase harness

Harness engineering: the model is fixed — what you engineer is the scaffolding around it (the environment, the docs, the feedback loops) so an agent can build and verify software with minimal human attention. Humans steer; agents execute. Your job is to make the repo legible, executable, and verifiable.

Work incrementally and depth-first: assess what exists, build the one missing capability, use it to unlock the next. Don't boil the ocean — set up what the repo actually needs. When the agent struggles, the fix is almost never "try harder" — ask "what capability is missing, and how do I make it legible and enforceable?" and add it.

This skill orchestrates the focused sub-skills: dev-local-setup, e2e-setup, crabbox-setup (cloud/parallel), and verifier-setup (scaffolds a repo-specific /verify loop; supersedes the older pr skill).

0. Assess

Survey the repo: stack, package manager, services/ports, infra deps, existing docs/tests/CI, and the implicit rules (buried in READMEs, PR comments, people's heads). Note what's missing per pillar below.

1. Legible — the agent can reason about the repo

What the agent can't see doesn't exist. Knowledge in chat threads / heads is invisible — push it into versioned, repo-local artifacts.

  • a) Map, not manual. Shrink the root agent doc (AGENTS.md / CLAUDE.md) to a ~100-line table of contents: one-line overview, project tree, golden rules (the hard invariants), and a "where to look" table. Move the depth into a structured docs/ system-of-record (architecture, frontend, testing, domain topics) with a docs/index.md. A monolithic instruction file rots and crowds out the task — keep the map small and stable, disclose detail progressively.
  • b) Custom lints with remediation. Promote the prose golden rules into mechanical checks — human taste captured once, enforced everywhere, every run. One lint per invariant (layering / dependency direction, naming, no-any, forbidden imports, file-size, structured logging). Write the error message to inject the fix ("X isn't allowed here — do Y") so the remediation lands in agent context. Wire them into the repo's linter + CI.
  • c) Queryable code graph. Index the repo with codebase-memory-mcp so the agent traces callers, data flow, and architecture from a knowledge graph instead of blind grepping — faster, more precise navigation on large codebases.
  • d) (later) Keep docs honest. A freshness / doc-gardening pass that flags docs that no longer match the code and opens fix-up PRs.

2. Executable — the agent can run & drive the app

  • dev-local-setup → a one-command, reproducible local stack (scripts/dev-local.sh up) running every service + infra.
  • Make the app drivable: browser via the playwright-cli skill (installed by verifier-setup); logs reachable.
  • crabbox-setup → an isolated cloud box per agent — the parallel-safe counterpart to dev-local. Reach for it when loops run concurrently: one laptop can't host N full stacks (fixed ports, one Docker daemon, one DB), and per-worktree local doesn't fix it — the worktrees still share the host. crabbox gives each agent its own stack + an in-box browser, so parallel verification never collides.
  • Advanced: a local, ephemeral observability stack (queryable logs/metrics) for perf/reliability prompts.

3. Verifiable — the agent can prove it works

  • e2e-setup → a trustworthy e2e gate: real flows (not bypass), a reusable auth/session helper, layered client → server → product assertions, video/trace evidence, sandbox-only external services.
  • verifier-setup → scaffolds a repo-specific /verify skill (the verify-before-ship loop): a fresh verifier sub-agent drives the real app to confirm the just-built feature works; the main agent fixes until green, runs the codified regression sweep, and opens a PR with a reviewable proof link. Add the session helper so the verifier can reach login-gated features.

4. Others — keep it coherent over time

  • Commit hygiene: conventional commits + format/lint on commit (e.g. husky lint-staged + commitlint). Keep merge gates light — at high agent throughput, corrections are cheap and waiting is expensive.
  • Garbage collection: encode "golden principles", then run periodic cleanup passes that open small refactor PRs — pay tech debt down continuously, not in painful bursts. Human taste captured once, enforced on every line.
  • Agent-to-agent review for correctness-critical changes (independent reviewers, not self-review).

Order & what you leave behind

1a (map) → 2 (dev-local) → 3 (e2e + verifier-setup), then 1b (lints) and 4 as the repo matures. The artifacts — slim map + docs/, scripts/dev-local.sh, an e2e/ suite, the generated /verify skill, and custom lints — are each a reusable, legible capability that compounds. Prefer "boring", composable, stable tech the agent can fully model.

Frequently asked questions

What does the Setup Codebase Harness AI skill do?

Master skill — set up the full agent harness for any repo so an agent can work it reliably: legible (map-not-manual docs + custom lints), executable (one-command dev stack), verifiable (e2e gate + a verify-before-ship loop), plus commit hygiene and entropy control. Use when onboarding a new/unfamiliar codebase to agent-driven development — "set up the harness", "make this repo agent-ready", "harness this codebase".

Why use Setup Codebase Harness on TypingMind?

Because you install it once and use it with any model. Setup Codebase 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 Setup Codebase Harness in TypingMind?

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

Which AI models can use Setup Codebase 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 Setup Codebase Harness?

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

Is the Setup Codebase Harness AI skill free?

It is published on GitHub by AI-Builder-Club. Check the repository for licensing terms. 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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