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Found 696 Skills
Multi-agent PR and code review workflow for projects using multiple AI assistants (Claude, GitHub Copilot/Codex, Gemini Code Assist). Use when working with pull requests, code reviews, commits, or addressing review feedback. Teaches how to check all feedback sources (conversation, inline, reviews), respond to inline bot comments, create Fix Reports, and coordinate between agents that use different comment formats. Critical for ensuring no feedback is missed from external review bots.
Turn repeatable outputs of a one-person company into compounding assets. Use when Codex needs to explain asset-compounding concepts when needed, verify prerequisite outputs, ask one question at a time, present multiple assetization priorities, and write user-confirmed outputs into `opc-doc/`.
Use this skill whenever a user wants to run, install, configure, or understand open-ralph-wiggum (ralph). This skill can be used by any AI assistant or IDE agent (GitHub Copilot, Claude Code, Cursor, Windsurf, etc.). Triggers on: "ralph", "ralph wiggum", "agentic loop", "iterative AI loop", "autonomous coding loop", "how to install ralph", "how to use ralph with Claude Code / Codex / Copilot / OpenCode", "ralph --agent", "ralph --tasks", "ralph --status", "--max-iterations", "--rotation", "how do I run ralph in VS Code / Cursor / JetBrains / Neovim", or any question about looping an AI coding agent until a task is done. Even if the user doesn't say "ralph" explicitly — if they want to run an AI agent in a loop until a promise tag appears in its output, use this skill.
Multi-Harness Portability is the engineering discipline of writing agent skills, prompts, and configurations that work across every major AI coding harness — Claude Code, Cursor, Codex, Gemini CLI, OpenCode, and beyond.
Run structured multi-role design reviews and architecture debates for technical decisions. Use when Codex needs to compare options, pressure-test tradeoffs, recommend an MVP path, or simulate a meeting with distinct evaluation roles such as moderator, skeptic, pragmatist, minimalist, maximalist, retrieval architect, Granary workflow lead, semantic purist, lightweight contrarian, context economist, or workflow conservative.
Run the trigger evaluation pipeline — classify, analyze, and optionally compare against a baseline. Only run when explicitly asked — evals are expensive.
Analyze a task, pick the right fleet type, and generate a ready-to-launch fleet (fleet.json + prompt.md files). Discovers available fleet skills dynamically. Use when the user wants to run work in parallel, asks to "plan a fleet", or says "fleet-plan".
Interactive hypothesis-driven debugging with documented exploration, understanding evolution, and analysis-assisted correction.
Capture conversations and decisions into structured Notion pages; use when turning chats/notes into wiki entries, how-tos, decisions, or FAQs with proper linking.
Get an external patent examiner review of a patent application. Use when user says "专利审查", "patent review", "审查意见", "examiner review", or wants critical feedback on patent claims and specification.
Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.
Unified issue resolution pipeline with source selection. Plan issues via AI exploration, convert from artifacts, import from brainstorm sessions, form execution queues, or export solutions to task JSON. Triggers on "issue:plan", "issue:queue", "issue:convert-to-plan", "issue:from-brainstorm", "export-to-tasks", "resolve issue", "plan issue", "queue issues", "convert plan to issue".