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Found 6,214 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.
Use when agent instruction files (AGENTS.md, rules/) need analysis, trimming, or restructuring. Orchestrates /imperatives → /policy-algebra → /visualize into a distillation pipeline.
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.
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.
Autonomous crypto business development patterns — multi-chain token discovery, 100-point scoring with wallet forensics, x402 micropayments, ERC-8004 on-chain identity, LLM cascade routing, and pipeline automation for CEX/DEX listing acquisition. Use when building AI agents for crypto BD, token evaluation, exchange listing outreach, or autonomous commerce with payment protocols.
Manage Zerion agent tokens and security policies — the primitives for autonomous trading and signing. Create / list / use / revoke agent tokens; create / list / show / delete policies (chain locks, allowlists, transfer/approval gates, expiry). Use whenever the user asks to set up an agent token, configure a policy, or enable autonomous trading. Required by `zerion-trading` and `zerion-sign`.
Expert in OpenClaw Studio - web dashboard for managing OpenClaw Gateway, agents, chat, approvals, and jobs
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.
Guides agents and users through migrating from Gemini API in Google AI Studio to Gemini Enterprise Agent Platform (formerly Vertex AI). Use this skill when moving applications to Google Cloud, to leverage Cloud credits, or to unify inferencing with other Cloud infrastructure (IAM, billing, telemetry).
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.
Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.