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Found 6,306 Skills
Reference skill for Zoom WebSockets. Use after routing to a low-latency event workflow when persistent connections, faster event delivery, or security constraints make WebSockets preferable to webhooks.
Give an AI agent an encrypted inbox with the masumi-agent-messenger CLI. Use when agents need to message other agents, read durable inboxes, manage threads, coordinate async multi-agent workflows, request human approval, or automate inbox operations with JSON output.
Comprehensive guide to the AgentMail Python and TypeScript SDKs. Use when building AI agents that need their own email inboxes, sending or receiving emails programmatically, managing threads and conversations, handling attachments, creating drafts for human-in-the-loop approval, setting up real-time notifications via webhooks or WebSockets, configuring custom domains, managing allow/block lists, using pods for multi-tenant isolation, or integrating email into any AI agent workflow. Covers the full AgentMail API with code examples, best practices, and production patterns.
Rigorous mathematical proof verification and fixing workflow. Reads a LaTeX proof, identifies gaps via cross-model review (Codex GPT-5.4 xhigh), fixes each gap with full derivations, re-reviews, and generates an audit report. Use when user says "检查证明", "verify proof", "proof check", "审证明", "check this proof", or wants rigorous mathematical verification of a theory paper.
Issue Workflow Stage 1 — Convert the user's problem into a reproducible, traceable {slug}-report.md through conversation. The AI only asks "what you saw, how to reproduce it, what should happen" here, and does not guess the root cause for the user (that's Stage 2's responsibility). Meanwhile, this stage is the only official decision point for choosing between the fast track and standard path: Based on the user's description, first review the relevant code; if the root cause can be identified at a glance and the required changes are minor, directly inform the user to take the fast track. Trigger scenarios: The user says "file an issue", "record this bug", "I found a problem". This is the starting point of the issue workflow with no pre-dependencies.
KUDO platform help — enterprise real-time AI speech translation and human interpretation in 200+ languages, embeddable widget for any meeting platform, 12,000+ interpreter marketplace, SOC 2 Type 2 + ISO 27001. Use when setting up KUDO for multilingual meetings or conferences, choosing between AI speech translation and human interpreters on KUDO, KUDO embeddable widget not working on a third-party event platform, comparing KUDO vs Interprefy vs Wordly vs JotMe for live interpretation, understanding KUDO Pro vs ProPlus vs ProPlatinum vs Enterprise pricing, or embedding KUDO translation into a hybrid event workflow. Do NOT use for choosing between all AI note-takers (use /sales-note-taker) or reviewing a call for coaching (use /sales-call-review).
Social media management strategy — publishing, scheduling, content calendars, engagement workflows, analytics, team collaboration, tool comparison (Sprout Social, Hootsuite, Buffer, Agorapulse, Sendible, Later, Brandwatch, Meltwater Engage, Influencity, Pallyy, Statusbrew, Sociality.io, Loomly, Planable). Use when social posts aren't getting engagement, you're spending too much time manually publishing, your content calendar is chaotic, you don't know when to post for best reach, DMs and comments are piling up unanswered, team approval workflows are slowing you down, or you can't prove social media ROI. Do NOT use for platform-specific config (use /sales-later, /sales-sproutsocial, /sales-meltwater, /sales-brandwatch, or /sales-influencity), social listening strategy (use /sales-social-listening), influencer marketing (use /sales-influencer-marketing), or paid social ads (use /sales-retargeting or /sales-b2b-advertising).
Create a workflow command that orchestrates multi-step execution through sub-agents with file-based task prompts
Implement AI Coaching best practices on AnalyticDB for PostgreSQL (ADBPG): Leverage Supabase projects (training data management) + ADBPG instances with vector optimization to build RAG-driven coaching systems that guide users through domain-specific workflows, decision-making, or skill development. Use when: User wants to create Supabase projects (spb-xxx), ADBPG instances (gp-xxx), vector knowledge bases, or RAG-driven coaching systems on ADBPG. Triggers: "Supabase", "ADBPG", "vector database", "knowledge base", "RAG", "AI coaching", "coaching system", "spb-xxx", "gp-xxx"
Comprehensive skill for the `kb` CLI and the Karpathy Knowledge Base pattern. Covers the full KB lifecycle — topic scaffolding, multi-source ingestion (URLs, files, YouTube, bookmarks, codebases), wiki article compilation, cross-article querying with file-back, lint-and-heal passes, QMD indexing, and hybrid search. Also covers codebase-specific analysis via inspect commands for complexity, coupling, blast radius, dead code, circular dependencies, symbol/file lookups, backlinks, and code smells. Use when working with kb CLI commands, knowledge base workflows, code vault generation, code graph analysis, code metrics inspection, wiki compilation, or the ingest-compile-query-lint cycle. Do not use for general code review, linting, formatting, building Go projects, or writing application code.
[production-grade] Implements autonomous testing and self-healing workflow. After code generation, automatically runs tests (unit, integration, visual, E2E), detects bugs, attempts auto-fix, and continues development. Requires: Vitest, Playwright, Applitools, LLM access.
Start a repo-local OptimizeSpec self-improvement change. Use when the user wants to create evals, optimize an agent with GEPA, define an agent self-improvement loop, or begin an ASI-first evaluation workflow.