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Found 13,396 Skills
Use when a Luma / 拾光 / 拾光智能体 / 拾光工具 agent needs to inspect local material libraries, describe material groups, upload or understand materials, search candidates, or prepare PIP matching inputs.
Transform vague ideas into implementation-ready specifications through structured interviewing. Use when user describes a new feature/product idea, has a problem to solve, or needs to document requirements. Produces intent.md (technical spec for code agents) and overview.md (human-friendly summary).
PeachSolution 신규 모듈 개발을 조율하는 통합 팀 스킬. 준비된 DB 스키마와 Spec, ui-proto 기반 표준 모드 + Spec만 모드 + 자연어 prompt 모드를 지원. "팀으로 만들어줘", "풀스택 개발", "팀 개발", "백엔드+UI 전체 생성", "버그 수정해줘", "이 화면에 X 추가해줘", "API와 화면 같이 만들어줘", "백엔드만 만들어줘", "API만 만들어줘", "UI만 추가" 키워드로 트리거. mode=backend(API+Store) | ui(UI만) | fullstack(전체) 지원하며, mode/proto 없이 자연어 입력만으로도 즉흥적 버그 수정·기능 추가 가능. 대규모 작업은 기능 큐와 Contract Gate로 1차 완성도를 높이는 방향을 따른다. peach-team-e2e와 함께 하나의 개발-검증 납품 흐름을 이루되, E2E 검증 독립성은 유지한다. 팀 실행 방식은 요청 범위와 런타임 도구 가용성을 분석해 single-agent / role-queue / agent-team 중 선택한다. 기존 팀 개발 스킬의 개발 조율 역할을 대체하며, DB 생성은 peach-gen-db 선행 단계로 분리한다.
Discover feature areas in the current repository that are not yet documented under the agent docs `features/` tree (scaffolded by `setup-agentic-repository` — `agents-docs/features/` by default, or wherever `--docs-dir` put it), then create populated feature docs from the canonical template. Use whenever the user wants to find undocumented features, fill out `features/`, catch up on missing feature documentation, document feature X/Y/Z, or mentions "find features". This is the natural follow-up to `setup-agentic-repository`, which scaffolds the empty `features/` tree this skill populates.
Use when running Claude Fable on codebase-heavy or token-heavy work and the user wants Fable to orchestrate research, coding, and testing while cheaper subagents do bounded heavy lifting.
Manage OpenCode's permission rules in opencode.jsonc — add, remove, or list auto-approval rules for Bash commands and tool invocations so the agent stops asking for confirmation on every single command. Use whenever the user wants to auto-approve, deny, or require confirmation for a shell command, even if they don't mention "permission" or "opencode.jsonc" directly. Triggers on "允许 kubectl get *", "拒绝 rm -rf", "auto-approve npm run build", "总是执行 git status", "add permission rule", "list my permissions", "查看权限", "添加权限", "移除权限", "把 X 加到允许列表", "skip confirmation for", and similar — even if the user doesn't explicitly mention OpenCode's config.
Using the Pi terminal agent — workspace setup, sessions, /commands, compaction, settings.json/AGENTS.md, skill discovery, providers/models, plus theme/keybinding/prompt customization (SYSTEM.md, APPEND_SYSTEM.md, settings.json, keybindings.json). Use for any "how do I configure/run Pi" question.
Use for "automate me", "create/update/refresh my -mode skill", "turn/capture my preferences or working style into a skill", or wanting agents to follow how the user works. Drafts or revises a personal -mode skill via create-skill + unslop, optionally pulling fresh evidence from recent transcripts.
Manages agent isolation levels and resource boundaries. Configures strict, moderate, and permissive isolation profiles. Activate on 'isolation level', 'agent isolation', 'resource boundaries', 'sandboxing', 'agent containment'. NOT for permission validation (use dag-permission-validator) or runtime enforcement (use dag-scope-enforcer).
Local-first, security-first control center for OpenClaw agents — visibility dashboard with readonly defaults, token attribution, collaboration tracing, and safe write operations.
Generate setup scripts/configs for AI agent worktrees and isolated environments across Cursor, Codex, and Conductor. Use when wiring up a project so AI agents start with the same dependencies, env files, and tool configs as the main repo.
Use when the user asks to "create a metric", "write a metric", "design a metric", "build a metric for", "evaluate agent performance", "measure call quality", "track a KPI", "add a workflow metric", "improve my metric", "fix a metric", "debug metric results", "set up quality scoring", or "what metrics do I need". Also relevant when discussing LLM judge prompts, custom code metrics, evaluation triggers, VALID_SKIP patterns, section extraction, or metric best practices for Cekura voice AI agents. Covers both creating new metrics and reviewing, iterating on, or troubleshooting existing ones.