Perform safe, reviewable agent-driven development using RPEQ workflow: research codebase with parallel agents for structure, patterns, and analysis; generate unambiguous execution plans; execute incrementally with atomic git commits, quality gates, and deployments; conduct QA for risks and correctness. Specialized agents handle TDD Python implementation, refactoring, security orchestration, and docs maintenance.
A comprehensive guide to creating custom slash commands for Claude Code.
This directory contains slash commands organized by category for easy discovery and management.
Conduct comprehensive codebase research using parallel sub-agents
Execute implementation plans from research-plans/plans/ directory
Executes a plan with gated checks, atomic commits, build/package/deploy, full validation, and immutable execution logs
This directory contains specialized AI agents organized by category.
Use this agent when you need to analyze recently implemented code changes to identify issues, inconsistencies, or areas needing fixes. This agent coordinates research sub-agents to examine file locations and implementation logic, then synthesizes findings into an actionable report focusing only on problems that require attention. <example>Context: The user has just implemented a new feature or made significant code changes and wants to verify the implementation quality. user: "I've just finished implementing the new authentication flow, can you check if there are any issues?" assistant: "I'll use the code-synthesis-analyzer agent to examine your recent implementation and identify any issues that need fixing." <commentary>Since the user has completed an implementation and wants to check for issues, use the code-synthesis-analyzer agent to research the changes and synthesize findings.</commentary></example> <example>Context: After a refactoring session, the user wants to ensure no logic was broken. user: "I refactored the payment processing module, please verify if anything needs fixing" assistant: "Let me launch the code-synthesis-analyzer agent to research your refactoring and report any issues found." <commentary>The user has made changes and specifically wants to know about potential issues, making this a perfect use case for the code-synthesis-analyzer agent.</commentary></example>
Analyzes codebase implementation details. Call the codebase-analyzer agent when you need to find detailed information about specific components. As always, the more detailed your request prompt, the better! :)
Locates files, directories, and components relevant to a feature or task. Call `codebase-locator` with human language prompt describing what you're looking for. Basically a "Super Grep/Glob/LS tool" — Use it if you find yourself desiring to use one of these tools more than once.
Use this agent when you need to analyze a bug, gather comprehensive context about it, and create a GitHub issue for tracking. The agent will investigate the bug's symptoms, potential causes, affected code areas, and reproduction steps, then use the GitHub CLI to create a well-documented issue. If unable to create the issue automatically, it will provide the user with complete instructions for manual creation. <example>Context: User encounters a bug in their application and wants to create a GitHub issue with proper documentation. user: "I'm getting a TypeError when calling the calculateTotal function with null values" assistant: "I'll use the bug-issue-creator agent to analyze this bug and create a GitHub issue for it" <commentary>Since the user reported a bug and needs it documented as an issue, use the bug-issue-creator agent to gather context and create the GitHub issue.</commentary></example> <example>Context: User discovers unexpected behavior in production and needs to document it. user: "The API is returning 500 errors when processing orders over $10,000" assistant: "Let me use the bug-issue-creator agent to investigate this issue and create a GitHub issue with all the relevant details" <commentary>The user found a production bug that needs investigation and documentation, so the bug-issue-creator agent should be used.</commentary></example>
This skill should be used when creating, refreshing, or validating a repository `AGENTS.md` so it stays concise, current, and grounded in repository evidence. Use when `AGENTS.md` is missing or stale, after refactors or tooling changes, when new docs become the system of record, or when adding lightweight drift checks.
Set up ast-grep for a codebase with common TypeScript rules for detecting anti-patterns, enforcing best practices, and preventing bugs. Creates sgconfig.yml, rule files, and rule tests. Use when adding structural linting, banning legacy patterns, or implementing ratchet gates.
Run or continue a differential debugging session between two implementations, traces, captures, or outputs. Record artifact identity, exact commands, first mismatch progression, findings, validation, and next probe in a durable session log.
Execute implementation plans from .artifacts/plan/. Focus on EXECUTING ONLY - no planning, no fixes outside plan scope. Uses gated checks, atomic commits, and maintains a single execution log in .artifacts/execute/. Use when the user says "execute this plan" or provides a plan path.
Map a repository's mechanical harness layers: canonical check command, local and CI gates, architecture boundaries, structural rules, behavioral verification, docs ratchets, evidence workflows, and operator-facing surfaces. Use when you need to understand how a repo keeps change safe.
Uses power tools
Uses Bash, Write, or Edit tools
Runs pre-commands
Contains inline bash commands via ! syntax
Bash prerequisite issue
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Uses bash pre-commands but Bash not in allowed tools
Uses bash pre-commands but Bash not in allowed tools
This repository is an active attempt to formalize and implement Harness Engineering—a mechanical, repo-enforced framework for making large (often agent-driven) code changes safe, reviewable, and incremental.
Inspired by OpenAI's work on Harness Engineering, this project provides the scaffolding, tooling, and conventions necessary for humans to steer while agents execute.
Harness Engineering is a set of constraints and feedback loops that enable high-throughput agent-driven development without architectural decay. It treats the repository as a system of record for everything agents need to know.
Key insight from OpenAI's experience:
"What Codex can't see doesn't exist."
Knowledge in Slack threads, Google Docs, or engineers' heads is illegible to agents. The solution: encode everything—architecture, taste, quality standards—into mechanical, verifiable structures in the repo itself.
| Principle | What it means |
|---|---|
| One canonical harness command | just check (or equivalent) runs all gates locally and in CI |
| Architecture as code | Import boundaries enforced mechanically (Import Linter, grimp) |
| Taste as code | AST rules (ast-grep) ban patterns and prevent regressions |
| Evidence-based chunks | Every change produces tests, snapshots, or golden diffs |
| Progressive disclosure | AGENTS.md is a map, not an encyclopedia; deeper docs linked |
| Agent-to-agent review | Council of models votes on risky changes |
.
├── .claude-plugin/
│ └── plugin.json # Claude Code plugin manifest
├── docs/
│ ├── harness-engineering.md # Full HES v1 specification
│ └── workflows/
│ └── RPEQ.md # Research → Plan → Execute → QA workflow
├── agents/ # Agent definitions for specific tasks
│ ├── analysis/ # Codebase analysis agents
│ ├── development/ # Development agents (TDD, refactoring)
│ ├── documentation/ # Documentation agents
│ ├── research/ # Research and synthesis agents
│ ├── performance/ # Profiling and optimization agents
│ └── security/ # Security review agents
├── commands/ # Slash commands for Claude Code
│ ├── context engineering/ # Research, plan, execute workflow
│ ├── integration/ # External tool integrations
│ ├── quality-assurance/ # QA and inspection commands
│ └── utilities/ # Helper commands
├── skills/ # Reusable skill definitions
│ ├── ast-grep-setup/ # TypeScript ast-grep rules setup
│ ├── codebase-research/ # Codebase mapping and research
│ ├── differential-session-runner/ # Durable debugging/evidence sessions
│ ├── harness-map/ # Repository harness layer mapping
│ ├── implementation-planner/ # Plan generation from research
│ ├── plan-executor/ # Execute implementation plans
│ └── qa-from-execute/ # QA review after execution
├── prompt-hooks/ # Prompt hooks for automation
├── alias/ # Model alias configurations
└── rules/ # Structural and taste rules
Install this as a plugin to get namespaced skills, agents, and commands:
# Install from local directory (for development)
claude --plugin-dir /path/to/harness-engineering
# Or add to your plugin marketplaces for easy installation
Once installed, skills are available as /harness-engineering:<skill-name>:
/harness-engineering:codebase-research - Map and research codebases/harness-engineering:harness-map - Map a repo's mechanical harness layers/harness-engineering:differential-session-runner - Create or continue durable debugging/evidence sessions/harness-engineering:implementation-planner - Generate execution plans/harness-engineering:plan-executor - Execute implementation plans/harness-engineering:qa-from-execute - QA review of changes/harness-engineering:ast-grep-setup - Set up structural lintingMost of the files in this repo (agents, commands, skills) are designed for Claude Code and OpenAI Codex. However, they can easily be adapted to other harnesses:
Enables Claude Code to manage Gemini CLI as an intern. Claude acts as manager/architect while Gemini does all coding work.
Systematic workflow for processing CodeRabbit code review feedback with MCP-powered analysis and fixes
npx claudepluginhub alchemiststudiosdotai/harness-engineeringMakes a repo agent-ready: AGENTS.md, boundary tests, CI pipeline, GC scripts — based on OpenAI's harness engineering methodology
Analyze and enforce best practices for AI coding agent projects. Assess codebase readiness across 8 pillars with /readiness, then scaffold enforcement with /setup: TDD, secret scanning, file size limits, auto-generated docs, and git hooks.
Harness for Claude Code — skills, /harness:* slash commands, persona subagents, lifecycle hooks, and MCP tools without per-repo `harness setup`. Sibling plugins exist for Cursor, Gemini CLI, and Codex.
Session harness plugin for Claude Code workflow automation
Long-running agent harness with 5-layer memory architecture, GitHub integration, autonomous batch processing, Agent Teams with ATDD, 9 hooks (safety, quality gates, team coordination), and 6 Agent Skills
Task-focused agents for test, review, debug, docs, CI, security, refactoring, research, performance, and search-replace — with teammate and subagent role guidance