Loading...
Loading...
Found 42 Skills
Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.
Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.
Periodic self-monitoring and health check system for autonomous agents. Runs scheduled health diagnostics, reports system status, and performs proactive maintenance tasks.
Create install.md files optimized for AI agent execution. Use for ANY question about install.md files or request to create/review installation documentation for autonomous agent use.
Structured error classification and recovery during autonomous operation. Classify runtime errors, apply retry strategies with backoff, maintain error logs, and escalate intelligently. Activate when encountering API failures, build tool crashes, file permission issues, or unexpected runtime errors during autonomous work. Triggers on: "error recovery", "retry", "API failure", "crash recovery", "service unavailable".
Verification boundary CLI that delegates tasks to autonomous agents. Use when the user wants to run forge, execute specs, run specs in parallel, audit code against specs, review changes, watch live logs, check run status, resume a session, or delegate complex multi-step work to an autonomous agent. Triggers include "forge run", "run this spec", "run specs in parallel", "audit the codebase", "review changes", "forge watch", "forge status", "rerun failed", "delegate this to forge".
Create and maintain a control-system metalayer for autonomous code-agent development in any repository. Use when you need explicit control primitives (setpoints, sensors, controller policy, actuators, feedback loop, stability and entropy controls), repo command/rule governance, and a scalable folder topology that lets agents operate safely and keep improving over time.
Autonomous p5.js visualization agent. It implements, inspects, critiques design/UX, fixes, and launches the result.
The social learning network for AI agents. Share, learn, and collaborate.
THE workflow for picking up and carrying ONE ticket/card forward, for an autonomous worker agent or for a human doing it locally. Resolves the repo's tracker from the AFK registry (~/.claude/afk.json; GitHub Projects or Linear), picks one ticket by priority, routes by status x label (interview / human walkthrough / execute), loads LEARNINGS.md as binding constraints, implements test-first, verifies end-to-end and simplifies the diff (the /go finish), then branches to a PR for the reviewer. Use when the user says "pick up <id>", "work on issue <id>", invokes /engineer, invokes /pickup, says "pickup", or at the very start of working any card.
Explain and write effective instructions for the `/goal` feature — the persistent self-checking agent loop (plan → act → test → review → iterate), available in agents like Codex, Claude Code, and Hermes Agent. Use when the user mentions `/goal`, "goal loop", "Ralph loop", wants to kick off a long-running autonomous agent run, asks how to write a goal prompt, or wants a one-paragraph goal instruction drafted.
Expert knowledge of agentic AI design patterns for autonomous agent development