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Found 73 Skills
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.
MUST be used whenever creating an AtlasTool (client-side tool) for an Atlas agent. Do NOT manually write AtlasTool definitions or wire them into useAtlasChat — this skill handles the TypeBox schema, execute function, and hook wiring. This includes tools that fetch data, render UI, call APIs, show charts, query local state, or perform any browser-side action. Triggers: AtlasTool, client tool, add tool, create tool, new tool, tool definition, agent tool.
Convert websites, Electron apps, and local tools into standardized CLIs for AI agents and humans using OpenCLI's browser automation and adapter framework.
Universal CLI client for Model Context Protocol (MCP) with persistent sessions, OAuth, tasks, and JSON output for shell scripting
Use when handling files, images, attachments, or binary data in n8n, OR when an AI agent needs to take a user-uploaded file as tool input or return a generated file. For Data Tables (schemas, dedup, persistent state), see the separate n8n-data-tables-official skill. Triggers on "file", "image", "PDF", "attachment", "binary", "upload", "download", chat trigger with files, agent tool that needs a file, vision/multimodal, or any handling of non-JSON file data.
Project scaffolding CLI with 30+ integrations, custom templates, and MCP server for AI agents.
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.
Slack automation CLI for AI agents. Use when: - Reading a Slack message or thread (given a URL or channel+ts) - Browsing recent channel messages / channel history - Getting all unread messages across channels - Searching Slack messages or files - Sending, editing, or deleting a message; adding/removing reactions - Listing channels/conversations; creating channels and inviting users - Fetching a Slack canvas as markdown - Looking up Slack users - Marking channels/DMs as read - Opening DM or group DM channels Triggers: "slack message", "slack thread", "slack URL", "slack link", "read slack", "reply on slack", "search slack", "channel history", "recent messages", "channel messages", "latest messages", "mark as read", "mark read", "unread messages", "unread", "what did I miss"
Conventional Commits 1.0.0 + 베스트 프랙티스 워크플로 (diff → staging → type 결정 → secrets blocklist → 사전 체크리스트) + 5 founding principle (atomic / leaves-repo-green / why-over-what / imperative / searchable) + project dialect scaffolding. 커밋을 4 reader (`git log` 스캐너 / `git blame` 추적자 / `git bisect` 사냥꾼 / AI agent — `/clear` 컨텍스트 복원 / PR 리뷰 / changelog 생성 / NL 질의)에게 동시에 도움되는 영구 history로 다룸. 본 파일은 한국어 prose 변형. 룰 자체 (영문 default body, lowercase summary, imperative mood, atomic / why-over-what 등 §0 전 원칙)는 영문 SKILL.md와 동일 — 변형 무관. ALWAYS trigger 조건은 영문 SKILL.md frontmatter §ALWAYS와 동일. Triggers (multi-lingual): EN: commit, git commit, stage, commit message, breaking change, conventional commits, revert, fixup, amend, cherry-pick, changelog KO: 커밋, 깃 커밋, 스테이지, 커밋 메시지, 커밋 룰, 컨벤셔널 커밋, 리버트, 되돌리기, 어맨드, 커밋 컨벤션, 커밋 메시지 검토 JA: コミット, git コミット, ステージ, コミットメッセージ, ブレーキング チェンジ, リバート, アメンド ZH: 提交, git 提交, 暂存, 提交信息, 提交消息, 重大变更, 回滚, 修订 Audience: 한국어를 모국어로 쓰는 개발자. §0 founding principle을 한국어로 먼저 잡고 싶은 사용자에게 적합. §1-§14 룰 자체는 영문 SKILL.md를 정본으로 참조 — 본 변형이 룰을 새로 정의하지 않음.
Expert in integrating OpenClaw AI with Chinese IM platforms (DingTalk, Feishu, QQ, WeChat Work, WeChat Official Account)
Use when the user wants to configure Lore commit format in their project or globally — writes Lore rules to the agent's instruction file (AGENTS.md, CLAUDE.md, QWEN.md, or global agent config) so all agents automatically use structured git trailers in commit messages
Provides tool and function calling patterns with LangChain4j. Handles defining tools, function calls, and LLM agent integration. Use when building agentic applications that interact with tools.