Total 38,159 skills
Showing 12 of 38159 skills
Expert blueprint for First-Person Shooters (Doom, Quake, Battlefield, Overwatch) focusing on physics-based movement, acceleration/friction, camera sway, weapon bobbing, and high-precision hit registration. Use when building tight, responsive FPS combat with advanced camera mechanics. Keywords FPS, movement physics, weapon bobbing, camera sway, hitscan, ground detection, air control.
Pipeline orchestrator that classifies incoming coding tasks and routes them through the correct combination of skills in the right order at the right depth. Auto-activates on any coding task. Centralizes the decision logic for which skills to use, how deep each goes, and how artifacts pass between them. Handles three pipeline variants: standard (plan-interview, intent-framed-agent, context-surfing, simplify-and-harden, self-improvement), team-based (agent-teams-simplify-and-harden), and CI (simplify-and-harden-ci, self-improvement-ci). Use this skill whenever starting any coding work — it determines the appropriate pipeline depth and variant automatically. Does not replace individual skills; dispatches to them.
AI-powered user research through natural language. Use the Cookiy CLI and hosted API for study creation, AI interviews, discussion guide editing, participant recruitment, report generation, and optional quantitative questionnaires.
AI-assisted Ansible authoring toolkit for Claude Code. Scaffolds, reviews, and updates playbooks, roles, collections, and ansible.cfg files following production best practices. Sub-commands: new-playbook, review-playbook, update-playbook, new-role, review-role, update-role, new-collection, review-collection, update-collection, new-conf, review-conf, update-conf. Requires bash_tool. Runs discovery (CLAUDE.md to ansible.cfg to README to filesystem) at the start of every command.
Use after the final approved execution scope is complete, or when the user asks whether a feature is done, ready to ship, safe to merge, or needs a quality check. Runs the post-execution quality gate: specialist review, artifact verification, and human UAT against locked decisions and the final exit state. Use for prompts like "review this feature", "is this done?", "can we ship this?", "double-check the implementation", or "run UAT".
Use when an approved current phase has 3 or more independent ready tasks and parallel execution will materially reduce cycle time. Orchestrates bounded workers, monitors blockers and file conflicts, coordinates rescues, and hands off to planning or reviewing when the current execution scope is complete. Use for prompts about swarming, parallel workers, launching multiple agents, coordinating a worker pool, or running approved current-phase work at scale.
Use whenever a beo session is starting, resuming, recovering from interruption, checking status, deciding what to do next, or when the correct beo skill is not obvious. This is the default bootstrap and routing entry point for the beo pipeline. Use first for prompts like "continue", "resume", "what's next?", "status?", "pick this back up", "where are we?", or any new feature request where the current phase is unclear.
Local reminder system with natural language scheduling
Use before any non-instant feature work, refactor, behavior change, or requirements-shaping conversation where user intent is not yet locked. Extracts and confirms the decisions that planning will depend on, especially when the user knows what they want but has not fully thought through edge cases, scope boundaries, or expected behavior. Output is CONTEXT.md.
Use when creating a new beo skill, editing an existing beo skill, or pressure-testing a beo skill before deployment. This skill should win whenever the task is to make a beo skill robust against rationalization, misuse, or failure under pressure. Do not use it for project-specific AGENTS.md conventions, one-off solutions, or ordinary feature planning.
Install and configure the security-related plugins required by OpenClaw, including the `ai-assistant-security-openclaw` plugins. Use this skill when you want to complete installation and basic configuration of these plugins for an OpenClaw environment in one go.
Use when beo learnings need a manual consolidation pass across multiple completed features, especially when learnings have gone stale, repeated patterns are accumulating, or the user asks to consolidate, clean up, merge, or promote existing learnings. This is the periodic learnings-sweep skill, not the per-feature compounding step. Use for prompts like "run dream", "consolidate learnings", "merge repeated learnings", or "do a learnings pass".