agent-harness
CLAUDE.md2 starsChanged 41 days agoMITView on GitHub
This is the agent-harness project — a production-grade collection of engineering skills and specialist agent personas for AI coding assistants. Define: interview-me, grilling, idea-refine, deep-dive, project-compass, product-saas, spec-driven-development, breakdown-feature-prd, breakdown-feature-implementation, story-mode, design-doc-diagramming, excalidraw-diagramming, acquire-codebase-knowledge Plan: planning-and-task-breakdown, github-issue-planning, slice-to-issues Build: incremental-implementation, test-driven-development, context-engineering, project-scaffolding, source-driven-development, doubt-driven-development, frontend-ui-engineering, figma-design-to-code, api-and-interface-design Verify: browser-testing-with-devtools, debugging-and-error-recovery Review: code-review-and-quality, code-simplification, security-and-hardening, performance-optimization Ship: git-workflow-and-versioning, ci-cd-and-automation, deprecation-and-migration, documentation-and-adrs, observability-and-instrumentation, shipping-and-launch
In this repository191 other files it gives its agents.
- AGENTS.mdAGENTS.md
- docs/agents.mdAGENTS.md
- skills/postgres-best-practices/AGENTS.mdAGENTS.md
- skills/react-best-practices/AGENTS.mdAGENTS.md
- accessibility-compliance-accessibility-auditSkill
- acquire-codebase-knowledgeSkill
- advanced-evaluationSkill
- agent-evaluationSkill
- alexSkill
- ariaSkill
- depSkill
- lunaSkill
- masonSkill
- maxSkill
- quinnSkill
- rexSkill
- agent-squadSkill
- ai-agent-developmentSkill
- ai-agents-architectSkill
- ai-engineering-toolkitSkill
- api-and-interface-designSkill
- api-design-principlesSkill
- api-documentation-generatorSkill
- api-documentationSkill
- api-documenterSkill
- api-endpoint-builderSkill
- api-fuzzing-bug-bountySkill
- api-patternsSkill
- api-security-testingSkill
- api-testing-observability-api-mockSkill
- application-performance-performance-optimizationSkill
- architecture-decision-recordsSkill
- architecture-patternsSkill
- async-python-patternsSkill
- attack-tree-constructionSkill
- autonomous-agent-patternsSkill
- aws-cost-optimizerSkill
- aws-serverlessSkill
- awt-e2e-testingSkill
- azure-functionsSkill
- backend-architectSkill
- backend-dev-guidelinesSkill
- backend-security-coderSkill
- brainstormingSkill
- breakdown-feature-implementationSkill
- breakdown-feature-prdSkill
- broken-authenticationSkill
- browser-testing-with-devtoolsSkill
- ci-cd-and-automationSkill
- clean-codeSkill
- cloud-architectSkill
- cloud-devopsSkill
- cloudformation-best-practicesSkill
- code-refactoring-refactor-cleanSkill
- code-refactoring-tech-debtSkill
- code-review-and-qualitySkill
- code-simplificationSkill
- comprehensive-review-full-reviewSkill
- comprehensive-review-pr-enhanceSkill
- container-security-hardeningSkill
- The first 60 shown.
The file
We split this CLAUDE.md into the rules it gives an agent
5 rules. About 0 are common, 4 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈4
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 50
Every skill lives in skills/<name>/SKILL.md
- Distinctive: line 53
Every skill has: Overview, When to Use, Process, Common Rationalizations, Red Flags, Verification
- Distinctive: line 64
Always: Follow the skill-anatomy.md format for new skills
- Distinctive: line 65
Never: Add skills that are vague advice instead of actionable processes
- Distinctive: line 66
Never: Duplicate content between skills — reference other skills instead
≈ “Common” is an estimate: at least 1 in 5 of the 26,980 CLAUDE.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
agent-harness
This is the agent-harness project — a production-grade collection of engineering skills and specialist agent personas for AI coding assistants.
Project Structure
skills/ → Curated skills (SKILL.md per directory) exposed to the agent
archive/ → Pre-prune bulk-import skills and tooling, kept but not exposed
agents/ → Specialist agent personas (12 total — see below)
hooks/ → Session lifecycle hooks
.claude/commands/ → Slash commands for Claude Code
commands/ → Slash commands for Antigravity CLI
.gemini/commands/ → Slash commands for Gemini CLI
.claude-plugin/ → Plugin manifest (plugin.json) and marketplace listing
scripts/ → Skill validation (validate-skills.js), agent-scopes.json, install/build scripts
references/ → Supplementary checklists (testing, performance, security, accessibility, orchestration)
docs/ → Setup guides for different tools
CONTRIBUTING.md → Guidelines for adding or editing skillsAgent Personas
| Agent | Role | Primary Scope |
code-reviewer | Senior Staff Engineer | Five-axis code review |
security-auditor | Security Engineer | Vulnerability detection, OWASP |
test-engineer | QA Engineer | Test strategy, coverage analysis |
web-performance-auditor | Web Performance Engineer | Core Web Vitals audit |
api-developer | Senior API Engineer | REST/GraphQL, spec-first, auth, versioning |
db-architect | Senior Database Architect | Schema design, indexing, migrations |
senior-devops | Senior DevOps Engineer | CI/CD, IaC, containers, GitOps |
senior-frontend-engineer | Senior Frontend Engineer | React/TS, accessibility, Core Web Vitals |
senior-cloud-architect | Senior Cloud Architect | Multi-region, IAM, HA/DR, cost optimisation |
senior-backend-engineer | Senior Backend Engineer | Service architecture, async, queues, caching |
ai-ml-engineer | AI/ML Engineer | Prompt engineering, RAG, LLM evaluation |
senior-qa-engineer | Senior QA Engineer | Feature verification, fix validation, regression checks |
Skills by Phase
Define: interview-me, grilling, idea-refine, deep-dive, project-compass, product-saas, spec-driven-development, breakdown-feature-prd, breakdown-feature-implementation, story-mode, design-doc-diagramming, excalidraw-diagramming, acquire-codebase-knowledge Plan: planning-and-task-breakdown, github-issue-planning, slice-to-issues Build: incremental-implementation, test-driven-development, context-engineering, project-scaffolding, source-driven-development, doubt-driven-development, frontend-ui-engineering, figma-design-to-code, api-and-interface-design Verify: browser-testing-with-devtools, debugging-and-error-recovery Review: code-review-and-quality, code-simplification, security-and-hardening, performance-optimization Ship: git-workflow-and-versioning, ci-cd-and-automation, deprecation-and-migration, documentation-and-adrs, observability-and-instrumentation, shipping-and-launch
Conventions
- Every skill lives in
skills/<name>/SKILL.md - YAML frontmatter with
nameanddescriptionfields - Description starts with what the skill does (third person), followed by trigger conditions ("Use when...")
- Every skill has: Overview, When to Use, Process, Common Rationalizations, Red Flags, Verification
- References are in
references/, not inside skill directories - Supporting files only created when content exceeds 100 lines
Commands
npm test— Not applicable (this is a documentation project)- Validate: Check that all SKILL.md files have valid YAML frontmatter with name and description
Boundaries
- Always: Follow the skill-anatomy.md format for new skills
- Never: Add skills that are vague advice instead of actionable processes
- Never: Duplicate content between skills — reference other skills instead
- Never: Run
git commit(or stage-then-commit) without the user explicitly asking for it in that message — always draft a commit message and leave the actual commit to the user, even mid-task or mid-plan. This overrides any per-slice "commit" step described byincremental-implementation,git-workflow-and-versioning,build, orsubagent-driven-development— those describe commit hygiene for when the user commits, not an instruction to commit automatically.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

