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Found 154 Skills
Knowledge base for designing, reviewing, and linting agentic AI infrastructure. Use when: (1) designing a new agentic system and need to choose patterns, (2) reviewing an existing agentic architecture ADR or design doc for gaps/risks, (3) applying the lint script to an ADR markdown file to get structured findings, (4) looking up a specific agentic pattern (prompt chaining, routing, parallelization, reflection, tool use, planning, multi-agent collaboration, memory management, learning/adaptation, MCP, goal setting, exception handling, HITL, RAG, A2A, resource optimization, reasoning techniques, guardrails, evaluation, prioritization, exploration/discovery). All rules and guidance are grounded in the PDF "Agentic Design Patterns" (482 pages).
Run browser automation through @playwright/mcp over UXC stdio MCP, with daemon-friendly session reuse and safe action guardrails. Use when tasks need deterministic page navigation, DOM snapshots, and scripted browser interaction from CLI.
Verify and validate AI output before it reaches users. Use when you need guardrails, output validation, safety checks, content filtering, fact-checking AI responses, catching hallucinations, preventing bad outputs, quality gates, or ensuring AI responses meet your standards before shipping them. Covers DSPy assertions, verification patterns, and generate-then-filter pipelines.
Use curated Korean trending-slang candidates plus best-effort Namu Wiki lookups to write witty Korean text with up-to-date slang, with conservative safety and freshness guardrails.
Production-grade AI agent patterns with MCP integration, agentic RAG, handoff orchestration, multi-layer guardrails, observability, token economics, ROI frameworks, and build-vs-not decision guidance (modern best practices)
Safety guardrails for destructive commands. Warns before rm -rf, DROP TABLE, force-push, git reset --hard, kubectl delete, and similar destructive operations. User can override each warning. Use when touching prod, debugging live systems, or working in a shared environment. Use when asked to "be careful", "safety mode", "prod mode", or "careful mode".
Build a Solana wallet monitoring bot (inflows/outflows, threshold alerts) with safe rate limits and privacy guardrails. Use for treasury monitoring, whale tracking, or security alerts.
MUST use this skill when installed and users ask to query, inspect, or run SELECT statements against SQLite or Postgres databases. Always route database reads through Unleak when a project contains an unleak/ folder, or when users ask to list database connections, inspect schemas, propose or validate access policies, activate policies, or query approved database data with leakage guardrails. This skill prevents direct credential, policy, schema, and raw database CLI access.
Use when clarifying fuzzy boundaries, defining quality criteria, teaching by counterexample, preventing common mistakes, setting design guardrails, disambiguating similar concepts, refining requirements through anti-patterns, creating clear decision criteria, or when user mentions near-miss examples, anti-goals, what not to do, negative examples, counterexamples, or boundary clarification.
CrewAI task design and configuration. Use when creating, configuring, or debugging crewAI tasks — writing descriptions and expected_output, setting up task dependencies with context, configuring output formats (output_pydantic, output_json, output_file), using guardrails for validation, enabling human_input, async execution, markdown formatting, or debugging task execution issues.
Use this skill when authoring PolicyRuleDefinition and PolicyRuleDefinitionSet metadata XML for the Salesforce Enforce-O-Matic MDAPI (Data Cloud governance policies), or when editing *.policyRuleDefinition / *.policyRuleDefinitionSet files. Covers the category decision tree, full schema for all policy variants (ACCESS, GOVERNANCE, RECORD, TRANSFORM), UI-compatibility rules for the Data Governance Policy Builder, and validation guardrails. Do NOT use this skill for UserAccessPolicy, AccessPolicy, SharingRules, PermissionSet, or any non-Enforce-O-Matic access-control metadata — those have their own types and live outside the PolicyRuleDefinition schema.
Summarizes WeChat group chat highlights into a structured digest using the local wx-cli binary (https://github.com/jackwener/wx-cli). Generates a normal digest by default; a roast (毒舌) version is opt-in. Maintains per-group history (history.json + history-digests.jsonl) and per-user profiles across runs, with privacy guardrails baked in. Use when the user asks to "总结群聊", "群聊精华", "群聊摘要", "summarize group chat", "group chat digest", mentions a WeChat group name with a time range, says "帮我看看 XX 群最近聊了什么", "XX 群有什么值得看的", or asks to "回溯画像" / "初始化画像" / "backfill profiles". Adds the roast version when the user says "毒舌版", "roast 版", "再来个毒舌的", or similar.