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Found 13,650 Skills
The meta skill. Turn any raw feature into a properly-skilled, tested, resolvable unit of agent capability. Cross-modal eval is the recommended Phase 3 quality gate: 3 frontier models from different providers critique the output, you iterate to quality, THEN write tests that lock in the proven-good behavior.
Execute Python code in isolated rootless containers with MCP server proxying for token-efficient agent workflows
Security scanner and health check for your AI agent skills tree. Identifies dead skills, missing documentation, and unsafe shell execution paths.
Multi-AI Agent P2P Debate. Suitable for technical solution stress testing, multi-perspective collision, and design decision convergence. Use it when you want a solution to be challenged or to understand the pros and cons of different technical routes. Triggered when mentioning "debate", "agent discussion", "multi-angle analysis", or "start a team".
Multi-source research synthesis — aggregate and compare 3+ sources or any source >5KB using sub-agent dispatch and SharedState
Update or repair Luma / 拾光 / 拾光智能体 / 拾光工具 / 拾光运营套装 by updating luma-cli and syncing agent skills.
Legacy-project style inheritance skill. Use when the user types /inherit-legacy-style, or when onboarding an AI coding agent onto a hand-written legacy project and you need to prevent "style drift" (the model imposing its pretrained mainstream idioms onto the project). Language- and framework-agnostic — it aligns meta-architecture only, not syntax. Once run, it becomes a behavioral constraint on all subsequent coding tasks. Do NOT use for pure research or one-off questions unrelated to code-style alignment.
Audit an existing product surface against its own design evidence, identify verified UI problems, and write self-contained implementation plans for another agent. Strictly read-only on product source. Use when asked to review, refine, improve, or clean up an interface without replacing its identity; investigate design-system drift; or prepare a design handoff.
Gather external knowledge the spec needs and distill it into §R — the durable research log — so build grounds in facts instead of hallucinating library behavior. Each finding cites a source; unsourced claims are flagged, never written as fact. Triggers when a spec decision hinges on a library/API/best practice the agent is unsure of, when the user says "research this", "what's the best lib for…", "check current best practice", or invokes /ck:research. Defers the §R write to the spec skill.
Use when scaffolding the agent knowledge layer (ARCHITECTURE.md, QUALITY_SCORE.md, docs/) for a repo.
Orchestrate building a brand-new feature end to end — research, plan, TDD implementation, review, and gated commit — by delegating each phase to the matching ECC agent. Use when adding a capability that does not exist yet.
Helps users discover and install agentic loops (recurring, scheduled AI agents) when they ask "find a loop for X", "is there a loop that…", "install a recurring agent that does X", "schedule an agent to do X", or want a repeating job run on a timer (a daily digest, a competitor watcher, a triage sweep, an every-morning report) — even if they never say the word "loop". Use this to SEARCH the agenticloops.dev directory and INSTALL an existing loop. This is the loop-level analogue of find-skills. For AUTHORING a new loop when none fits, use the fuller `loops` skill.