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Found 4,976 Skills
AI-first knowledge base and startup OS with file-based storage, AI agents, scheduled jobs, and embedded apps
2-layer parallel agent hierarchy. Layer 1 deploys 3-50+ agents, each with independent context. Layer 2 adds 2+ sub-agents per member. No upper limit on either layer.
Set up or update the agent-first engineering harness for any repository. Implements the complete scaffolding that makes AI coding agents effective: knowledge maps (AGENTS.md as a concise TOC), structured documentation, architecture boundaries, enforcement rules (.harness/*.yml specs), quality scoring, and process patterns for agent-driven development. Use this skill whenever someone wants to make a repo agent-ready, set up AGENTS.md or docs/ structure, define domain boundaries or golden principles, generate .harness/ configuration, audit agent readiness, or update an existing harness. Also trigger when a user reports problems with agent effectiveness, context management, or architectural drift — these are symptoms of a missing or stale harness. Trigger on: "harness this repo", "set up harness", "agent-first setup", "make this agent-ready", "update the harness", "assess agent readiness", "set up AGENTS.md", "organize for agents", or any discussion about structuring a codebase for AI agent workflows.
Build MCP (Model Context Protocol) servers including tool definition, schema design, authentication, error handling, and Claude Code integration. Use this skill when the user needs to create an MCP server, expose APIs or databases to AI agents, design tool schemas, or integrate with Claude Code — even if they say 'build an MCP server', 'connect Claude to our database', 'expose our API to AI', or 'create a tool for Claude Code'.
Apply Agency Theory (Jensen and Meckling, 1976) to diagnose principal-agent problems — moral hazard, adverse selection — and design governance mechanisms to align interests. Use this skill when the user needs to analyze conflicts of interest between owners and managers, design incentive or monitoring structures, evaluate corporate governance effectiveness, or when they ask 'how do we ensure managers act in shareholders interest', 'why is this incentive plan failing', or 'what governance mechanisms reduce agency costs'.
Comprehensive security auditor for AI agent skills, prompts, and instructions. Checks for typosquatting, dangerous permissions, prompt injection, supply chain risks, and data exfiltration patterns — before you use any agent or skill.
Store and retrieve agent memories across jobs. Enables long-term context, learning from past interactions, and building agent knowledge bases. Based on OpenClaw's memory-core architecture.
Periodic self-monitoring and health check system for autonomous agents. Runs scheduled health diagnostics, reports system status, and performs proactive maintenance tasks.
Decomposes a spec or architecture into buildable tasks with acceptance criteria, dependencies, and implementation order for AI agents or engineers. Produces `.agents/tasks.md`. Not for clarifying unclear requirements (use discover) or designing architecture (use system-architecture). For code quality checks after building, see review-chain. For packaging and PRs, see ship.
Update repo documentation and agent-facing guidance such as AGENTS.md, README.md, docs/, specs, plans, and runbooks. Use when code, skill, or infrastructure changes risk doc drift or when documentation needs cleanup or restructuring. Do not use for code review, runtime verification, or `agent-readiness` setup.
Use this skill when you learn one or more design pattern(s) in the Langroid (multi) agent framework, and want to make a note for future reference for yourself. Use this either autonomously, or when asked by the user to record a new pattern.
For CLI agents WITHOUT subagent support (e.g., Codex CLI). Search previous code agent sessions for specific work, decisions, or code patterns.