Design and create pitlane eval benchmarks that measure whether an AI coding skill or MCP server actually improves assistant performance. Use when the user wants to test a skill, evaluate an MCP server, create a pitlane eval YAML, benchmark an AI assistant, or compare baseline vs challenger configurations. Covers eval…
Documentation conventions for generating high-quality AI agent skills from TypeScript source. Use when preparing a library for skill generation, auditing JSDoc quality, fixing audit warnings, writing @useWhen/@avoidWhen/@never tags, or asking about documentation conventions for skills. Use this even if the user just…
This machine publishes to a team shelf, a Tenjin deployment of the team's own rather than the public marketplace. A finding that cost a real install, a probe, or an hour of elapsed time is worth the same hour to the next teammate who hits it. Notes are free. Legacy answer-card completeness is public buyer context…
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Use SaferSkills to find, evaluate, and safely install AI agent capabilities (skills, MCP servers, hooks, plugins, rules) and to assess a whole agent. Run this before you install, add, recommend, or trust any capability — or when asked whether one is safe or what its score is: scan and score it first with npx…
Charter — an operating contract system for autonomous agents. Use when the user invokes /charter or /goal, says they want to start/track/resume long-running work, gives vague intent to operationalize, or when you must own and update a durable charter contract with approval gates, ledger, and receipts. Object model…
Orchestrate agentic workflow to satisfy user request and goal. In the current session you run scripts/getnextinstruction.py and precisely follow the instructions in its output (stdout). Use for any multi-step task that benefits from planned, branching, verifiable orchestration.
Design, build, validate, and publish production-ready Codex skills. Use when Codex needs to turn a repeated workflow or new capability into a new skill, organize its scripts/references/assets, forward-test it before first release, or package it as an installable repository.
Drafts a goal+rider document pair that briefs an autonomous coding agent on one round of work — a goal file under 4,000 characters (sized to fit the /goal command in both Claude Code and Codex) plus an unbounded rider with phased plans and named depth tests. Use when the user says "draft a goal", "write a goal+rider"…
Internal test harness for plugin maintainers. End users should not invoke this. Runs synthetic personas through critical journeys against the plugin and produces LLM-judge findings reports. Trigger words "/test-personas", "run the test harness", "test the plugin end-to-end", "run the test personas".
Install structured self-improvement loops with instinct-based learning into Claude Code — research, plan, execute, verify, reflect, learn, iterate. On-demand or weekly analysis to save tokens. Supports multi-agent parallel analysis.
Use this skill when a task is best handed off to another coding agent in the endy stack — OpenCode (multi-model worker, fast refactors, test writing), CommandCode/cmd (Kimi K2.6 / DeepSeek), Hermes (Nous Research, tool-heavy), Gemini, or Claude. Covers three delivery modes: short blocking calls, long detached tmux…
The Filter (兔). Orchestrator and post-processor that makes LLM output actually readable. Invokes other zodiac animals based on what the user needs, then synthesizes and reshapes their raw output for the specific human reading it. Breaks the LLM default of audience-blind verbosity — walls of text, wrong detail level…
Daily paper scout for Auto-Skill and Auto-Rubric research. Use when: searching for new papers on self-evolving agents, skill evolution, rubric learning, preference alignment, reward modeling, agentic evolution. Searches arxiv for latest papers, recommends noteworthy ones, and updates the Awesome-AutoSkill-AutoRubric…
Use when auditing, pruning, or cleaning up an agent's entire existing rule set in one bulk pass across every rule source at once (all CLAUDE.md files, all memory entries, AGENTS.md, ADRs, COORDINATION). This is whole-corpus hygiene; NOT for adding, editing, or fixing an individual rule (just edit that file directly).
Route token-heavy context, noisy shell logs and output through the smallest measured CLI or projection before loading heavy tools; recall past agent sessions before re-deriving; fan out to free lanes; benchmark cheap subagent workflows without broad prompt bloat.
Sync Claude/OpenCode/Codex/Gemini/Kiro/Qoder/Cursor settings and skills to GitHub Gist. Migrate MCP configs and skills between AI coding tools with format conversion.
Use only when the user explicitly asks to start, operate, inspect, shutdown, or restart a Team Agent team. Treat the team-agent CLI as a sealed appliance.
Plan and supervise user-approved software initiatives through Codex native tasks and bounded native subagents. Use when the user explicitly asks Agent Orchestration Gateway to clarify work, split it into non-overlapping modules, dispatch module tasks, or run an AOG module task.
Spawn Claude Opus 5 or Fable 5 as an external subagent through the Claude Code CLI, supervised by exactly one GPT-5.6 Sol low-effort babysitter. Use automatically when the user says "spawn Opus 5", "spawn Fable 5", asks for an Opus 5 or Fable 5 sub-agent, or asks Codex to delegate a task to either Claude model.
Use when the user is running one or more coding agents and needs a shared control plane to coordinate work and keep an audit trail. Triggers include running parallel Claude Code/Cursor sessions, agents colliding on the same work, needing objectives instead of a task list, or needing auditable evidence of…
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At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: