Loading...
Loading...
Found 2,174 Skills
Generates project context (code structure + architecture intent). Use when starting sessions, understanding codebase structure, onboarding to a project, after major refactoring, or delegating complex work to agents.
Set up a full AI ensemble/mob programming team for any software project. Creates team member profiles (.team/), coordinator instructions (.team/coordinator-instructions.md), project owner constraints (PROJECT.md), team conventions (AGENTS.md), architectural decisions (docs/ARCHITECTURE.md), domain glossary, and supporting docs. Use when: (1) starting a new project and wanting a full expert agent team, (2) the user asks to "set up a team", "create a mob team", "set up ensemble programming", or "create agent profiles", (3) converting an existing project to the driver-reviewer mob model, (4) the user wants AI agents to work as a coordinated product team with retrospectives and consensus-based decisions.
Create, manage, and orchestrate AI agents using the AI Maestro CLI. Use when the user asks to "create agent", "list agents", "delete agent", "hibernate agent", "wake agent", "install plugin", "show agent", "restart agent", or any agent lifecycle management task.
Configure project memory files (CLAUDE.md, AGENTS.md, CODEX.md) for persistent context, coding standards, architecture decisions, and team conventions. Reference for the 4-tier memory hierarchy, cross-platform compatibility, and quick-add commands.
Comprehensive documentation audit and generation. Launches parallel agents for high-level docs, module-level docs, decision records, and state diagrams. Use when: documentation gaps, post-implementation docs, README updates, architecture docs.
Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Includes memory architecture with pre-compaction flush (so context survives when the window fills), reverse prompting (surfaces ideas you didn't know to ask for), security hardening, self-healing patterns (diagnoses and fixes its own issues), and alignment systems (stays on mission, remembers who it serves). Battle-tested patterns for agents that learn from every interaction and create value without being asked.
The social learning network for AI agents. Share, learn, and collaborate.
Structured session analysis and project instruction refinement using a five-type intervention taxonomy (Correction, Repetition, Role Redirect, Frustration Escalation, Workaround) with severity scoring to categorize process gaps. Refines project instructions (CLAUDE.md, AGENTS.md, .team/coordinator-instructions.md) with structural (not advisory) language, maintains WORKING_STATE.md for crash recovery (read-first-after-any- interruption protocol), and implements a self-reminder protocol (re-read constraints every 5-10 messages to prevent role drift). Includes advisory- to-structural promotion pattern for recurring gaps. Activate after milestones, repeated user corrections, session restarts, crash recovery, every 5 completed tasks, or on user request. Triggers on: "reflect on this session", "why do I keep correcting you", "update project instructions", "update working state", "session retrospective", "crash recovery", "context compaction", "role drift", "I keep telling you the same thing", "analyze my corrections". Also relevant when the agent notices repeated corrections, needs to resume after compaction, or wants to prevent known failure modes from recurring.
Anti-detect browser automation CLI for AI agents. Use when the user needs to interact with websites with bot detection, CAPTCHAs, or anti-bot blocks, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task that requires bypassing fingerprint checks.
Enhance a plan with parallel research agents for each section to add depth, best practices, and implementation details
Reference for calling the Gemini CLI agent from other agents. ALWAYS read BEFORE invoking Gemini to ensure correct JSON protocol, session management, and subtask delegation patterns.
Use after resolving a bug, failed task, or unexpected agent behavior to improve the pipeline skills, agents, hooks, or scripts that contributed to the problem. Also proactively suggest improvements when recurring patterns or inefficiencies are observed.