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Found 758 Skills
Route to the appropriate Frappe skill based on task type. Use as the entry point when working on Frappe projects to determine which specialized skill to apply.
Universal fallback for executing actions across 1,000+ apps when no other skill is available. Use this skill ONLY when user requests an action on an app/service not covered by other skills (e.g., Notion, Asana, Trello, HubSpot, Airtable, Linear, Monday, Zendesk, Intercom, Stripe, Shopify, QuickBooks, Zoom, Microsoft 365, Dropbox, Box, Figma, Jira, Confluence, etc.). Do NOT use if another skill already handles the service. Triggers on requests to connect to external apps, execute actions on third-party services, or when user asks "can you actually do X" for an unsupported service.
Report-only QA testing. Systematically tests a web application and produces a structured report with health score, screenshots, and repro steps — but never fixes anything. Use when asked to "just report bugs", "qa report only", or "test but don't fix". For the full test-fix-verify loop, use /qa instead.
Plans.mdのタスクを実装。スコープを聞いて自動判断、1タスクから全タスクまで。Use when user mentions '/work', execute plan, implement tasks, build features, work on tasks, 'do everything', 'implement', '実装して', '全部やって', 'ここだけ'. Do NOT load for: planning, reviews, setup, deployment, or breezing (team execution).
Eng manager-mode plan review. Lock in the execution plan — architecture, data flow, diagrams, edge cases, test coverage, performance. Walks through issues interactively with opinionated recommendations.
Manage DingTalk product capabilities (AI Table/Calendar/Contacts/Documents/Robots/Todo/Email/Meeting Minutes/AI Applications/Approvals/Work Reports/Drive, etc.). Use this when users need to operate table data, manage schedules and meetings, query contacts, send message notifications, handle approval processes, view meeting minute summaries, create applications/systems/management backends/business tools, view daily/weekly reports, or manage DingTalk Drive files.
STUB — installed at ~/openclaw/skills/healthcheck/SKILL.md
Code quality dashboard. Wraps existing project tools (type checker, linter, test runner, dead code detector, shell linter), computes a weighted composite 0-10 score, and tracks trends over time. Use when: "health check", "code quality", "how healthy is the codebase", "run all checks", "quality score". (gstack)
Codify the most recent successful /scrape flow into a permanent browser-skill on disk. Future /scrape calls with the same intent run the codified script in ~200ms instead of re-driving the page. Walks back through the conversation, synthesizes script.ts + script.test.ts + fixture, runs the test in a temp dir, and asks before committing. Use when asked to "skillify", "codify", "save this scrape", or "make this permanent". (gstack)
Turn any markdown file into a publication-quality PDF. Proper 1in margins, intelligent page breaks, page numbers, cover pages, running headers, curly quotes and em dashes, clickable TOC, diagonal DRAFT watermark. Not a draft artifact — a finished artifact. Use when asked to "make a PDF", "export to PDF", "turn this markdown into a PDF", or "generate a document". (gstack) Voice triggers (speech-to-text aliases): "make this a pdf", "make it a pdf", "export to pdf", "turn this into a pdf", "turn this markdown into a pdf", "generate a pdf", "make a pdf from", "pdf this markdown".
Cross-model benchmark for gstack skills. Runs the same prompt through Claude, GPT (via Codex CLI), and Gemini side-by-side — compares latency, tokens, cost, and optionally quality via LLM judge. Answers "which model is actually best for this skill?" with data instead of vibes. Separate from /benchmark, which measures web page performance. Use when: "benchmark models", "compare models", "which model is best for X", "cross-model comparison", "model shootout". (gstack) Voice triggers (speech-to-text aliases): "compare models", "model shootout", "which model is best".
Manage project learnings. Review, search, prune, and export what gstack has learned across sessions. Use when asked to "what have we learned", "show learnings", "prune stale learnings", or "export learnings". Proactively suggest when the user asks about past patterns or wonders "didn't we fix this before?"