Revert changes by logical work unit with full git awareness. Supports reverting entire tracks, specific phases, or individual tasks. Use it to give an agent explicit responsibilities, steps and constraints.

Create a new track (feature, bug fix, chore, or refactor) with a detailed specification and phased implementation plan. Use it to give an agent explicit responsibilities, steps and constraints.

Manage the complete track lifecycle including archiving, restoring, deleting, renaming, and cleaning up orphaned artifacts. Use it to give an agent explicit responsibilities, steps and constraints.

Execute tasks from a track's implementation plan, following the workflow rules defined in conductor/workflow.md. Use it to give an agent explicit responsibilities, steps and constraints.

You are an expert validator for Conductor project artifacts. Your role is to verify that Conductor's Context-Driven Development setup is complete, consistent, and correctly configured. Use it to give an agent explicit responsibilities, steps and constraints.

PreToolUse hook configuration that intercepts and blocks bypass-flag usage before execution, ensuring AI agents cannot skip pre-commit hooks, GPG signing, or other git safety mechanisms. Use it to give an agent explicit responsibilities, steps and constraints.

Detailed preset team configurations with task templates for common workflows. Use it to give an agent explicit responsibilities, steps and constraints.

Best practices for composing multi-agent teams, selecting team sizes, choosing agent types, and configuring display modes for Claude Code's Agent Teams feature. Use it to give an agent explicit responsibilities, steps and constraints.

Protocols for effective communication between agent teammates, including message type selection, plan approval workflows, shutdown procedures, and common anti-patterns to avoid. Use it to give an agent explicit responsibilities, steps and constraints.

Strategies for decomposing features into parallel work streams, establishing file ownership boundaries, avoiding conflicts, and integrating results from multiple implementer agents. Use it to give an agent explicit responsibilities, steps and constraints.

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration. Use it to give an agent explicit responsibilities, steps and constraints.

Complete guide to using agents, slash commands, and multi-agent workflows. Use it as a repeatable review, validation or hardening pass.

PluginEval is a three-layer quality evaluation framework for Claude Code plugins and skills. It combines deterministic static analysis, LLM-based semantic judging, and Monte Carlo simulation to produce calibrated quality scores with confidence intervals. Use it to ground design choices in named patterns, trade-offs and examples.

claude-agents is a multi-harness plugin marketplace. Source-of-truth lives under plugins/ as Claude Code markdown. Per-harness artifacts are generated by adapters under tools/adapters/. Use it to ground design choices in named patterns, trade-offs and examples.

Plugin content in this repo ships to five harnesses: OpenAI Codex CLI, Cursor, OpenCode, Gemini CLI, and GitHub Copilot. Claude Code is the source-of-truth. The adapter framework handles per-harness mechanics (frontmatter rewrites, format transforms, output paths) so you auth. Use it to ground design choices in named patterns, trade-offs and examples.

This marketplace follows industry best practices with a focus on granularity, composability, and minimal token usage. Use it as a repeatable review, validation or hardening pass.

Complete reference for all 203 local specialized AI agents organized by category with model assignments. Use it as a repeatable review, validation or hardening pass.

Top-level architectural map for the claude-agents marketplace. Detail lives in docs/architecture.md; this file is the index per the OpenAI harness-engineering pattern. Use it to ground design choices in named patterns, trade-offs and examples.

This document provides Python-specific best practices and examples for implementing MCP servers using the MCP Python SDK. It covers server setup, tool registration patterns, input validation with Pydantic, error handling, and complete working examples. Use it to ground design choices in named patterns, trade-offs and examples.

This document provides Node/TypeScript-specific best practices and examples for implementing MCP servers using the MCP TypeScript SDK. It covers project structure, server setup, tool registration patterns, input validation with Zod, error handling, and complete working examples. Use it to ground design choices in named patterns, trade-offs and examples.