Use when improving, evolving, benchmarking, or maintaining agent skills over time. Treats skills as long-lived structured artifacts: execute tasks, diagnose trajectory evidence, accumulate textual momentum, patch at the correct abstraction layer, test per model and harness, canary changes, and roll back regressions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when designing retryable or crash-resilient agent workflows, especially when tools can send, charge, publish, provision, or otherwise create duplicate side effects.
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Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate or edit images via OpenRouter with the Gemini 3 Pro Image model. Use for prompt-only image generation, image edits, and multi-image compositing; supports 1K/2K/4K output.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Production-grade Stripe integrations: subscriptions with trials and proration, one-time payments, usage-based billing, checkout sessions, idempotent webhook handlers, customer portal, and invoicing. Covers Next.js, Express, and Django patterns. Use when integrating Stripe for the first time, debugging webhook reliability issues, migrating from a different payment provider, or adding usage-based billing to an existing subscription product.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Improve AI application with evaluation-driven development. Define eval criteria, instrument the application, build golden datasets, observe and evaluate application runs, analyze results, and produce a concrete action plan for improvements. ALWAYS USE THIS SKILL when the user asks to set up QA, add tests, add evals, evaluate, benchmark, fix wrong behaviors, improve quality, or do quality assurance for any Python project that calls an LLM model.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when designing evaluation suites for agentic systems, choosing agent benchmarks, measuring multi-agent performance, or critiquing single-score evaluation of agents. Based on arXiv:2605.26112v1. Trigger phrases: "evaluate my agent", "benchmark agent", "measure agent reliability", "pass^k evaluation", "agent metrics beyond success rate", "trajectory quality", "memory hygiene metric", "longitudinal agent eval", "why my agent scores well on benchmarks but fails in production".
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Set up and optimize a complete local AI infrastructure — Ollama, Open WebUI, local RAG pipelines, and private model serving. Use when running models locally for privacy, compliance, offline use, or cost, or setting up GPU-accelerated local inference and RAG.
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Use when evaluating MCP-enabled agents, tool-using LLM systems, multi-server workflows, tool-selection quality, trajectory quality, task completion, or MCP security regressions.
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Merges mature lessons from a domain memory file into its instruction file. Syntax: `/memory-merger >domain [scope]` where scope is `global` (default), `user`, `workspace`, or `ws`. Use when a domain memory file has accumulated lessons that should become permanent instructions.
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Use when comparing basic, enhanced, GraphRAG, or agentic RAG designs, evaluating domain-specific RAG quality, tuning retrieval components, or deciding whether agentic RAG is worth its cost.
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Use when the user asks to work on a Python FastAPI project. Triggers on keywords Python, FastAPI, Pydantic, SQLAlchemy.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.