Найдите подходящую возможность для вашего агента.
Build interactive AI chat widgets with buttons, forms, and bidirectional actions. Use when creating agentic UIs with clickable widgets, entity tagging (@mentions), composer tools, or server-handled widget actions. Covers full widget lifecycle. NOT when building simple text-only chat without interactive elements.
Recognize, diagnose, and mitigate patterns of context degradation in agent systems. Use when context grows large, agent performance degrades unexpectedly, or debugging agent failures.
Build persistent memory systems for AI agents using Mem0, claude-mem, or custom implementations. Use when adding conversation memory, user preferences, or contextual recall to agents. Covers memory architecture patterns, retrieval strategies, and privacy controls. NOT for RAG systems (use building-rag-systems).
Use when building AI agents with Google's Agent Development Kit (ADK). Triggers include creating ADK agents, Gemini-powered agents, multi-agent workflows, MCP integration with ADK, agent evaluation, workflow agents (Sequential/Parallel/Loop), Vertex AI deployment, or comparing OpenAI SDK with ADK. NOT when using other frameworks (OpenAI SDK, LangChain, CrewAI) or general LLM API calls.
Designs deliberate practice exercises applying evidence-based learning strategies like retrieval practice, spaced repetition, and interleaving. Activate when educators need varied exercise types (fill-in-blank, debug-this, build-from-scratch, extend-code, AI-collaborative) targeting learning objectives with appropriate difficulty progression. Creates exercise sets that apply cognitive science principles to maximize retention and skill development. Use when designing practice activities for Python concepts, creating homework assignments, generating problem sets, or evaluating exercise quality.
Use when testing AI agent code with pytest. Covers TDD for agent APIs, mocking LLM calls (NOT evaluating LLM outputs), pytest-asyncio patterns, FastAPI testing with httpx, SQLModel testing, and agent tool testing. NOT for evaluating LLM reasoning quality (use evals skill).
Design co-learning experiences using the Three Roles Framework (AI as Teacher/Student/Co-Worker). Use when teaching AI-driven development workflows, spec-first collaboration, or balancing AI assistance with foundational learning. NOT for curriculum without AI integration.
Use when building async database layers with SQLModel and PostgreSQL. Triggers include async session management, create_async_engine, SQLModel relationships, CRUD operations with async/await, N+1 prevention with selectinload, JSONB columns, self-referential models, or Alembic async migrations. NOT when using sync SQLAlchemy (use sync patterns) or raw SQL (use SQLModel ORM).
Generate production-ready Dockerfiles with multi-stage builds, security best practices, and optimization. Use when containerizing Python applications for production deployment.
Deploy and test dspy-cli on Fly.io using local changes via temp git branch. Full integration testing with guaranteed cleanup. (project)
Nx monorepo management skill for AI-native development. This skill should be used when working with Nx workspaces, project graphs, affected detection, code generation, and caching. Use when analyzing dependencies, running affected commands, generating code, configuring Nx Cloud, or optimizing build performance. Invoke nx-mcp tools for documentation queries.
Build production traffic engineering for Kubernetes with Envoy Gateway, Gateway API, KEDA autoscaling, and Envoy AI Gateway. Use when implementing ingress, rate limiting, traffic routing, TLS, autoscaling, or LLM traffic management.
Research GitHub, GitLab, and Bitbucket repositories using DeepWiki MCP server. Use when exploring unfamiliar codebases, understanding project architecture, or asking questions about how a specific open-source project works. Provides AI-powered repo analysis and RAG-based Q&A about source code. NOT for fetching library API docs (use fetching-library-docs instead) or local files.
This skill should be used when evaluating the quality of book chapters, lessons, or educational content. It provides a systematic 6-category rubric with weighted scoring (Technical Accuracy 30%, Pedagogical Effectiveness 25%, Writing Quality 20%, Structure & Organization 15%, AI-First Teaching 10%, Constitution Compliance Pass/Fail) and multi-tier assessment (Excellent/Good/Needs Work/Insufficient). Use this during iterative drafting, after content completion, on-demand review requests, or before validation phases.
Tracks and analyzes keyword ranking positions over time for both traditional search results and AI-generated responses. Monitors ranking changes, identifies trends, and alerts on significant movements.
Validates skills against production-level criteria with 9-category scoring. This skill should be used when reviewing, auditing, or improving skills to ensure quality standards. Evaluates structure, content, user interaction, documentation, domain standards, technical robustness, maintainability, zero-shot implementation, and reusability. Returns actionable validation report with scores and improvement recommendations.
Systematic methodology for debugging bugs, test failures, and unexpected behavior. Use when encountering any technical issue before proposing fixes. Covers root cause investigation, pattern analysis, hypothesis testing, and fix implementation. Use ESPECIALLY when under time pressure, just one quick fix seems obvious, or you've already tried multiple fixes. NOT for exploratory code reading.
Use Fabric CLI for Power BI operations — semantic models, reports, DAX queries, refresh, gateways. Activate when users work with Power BI items, need to refresh datasets, execute DAX, manage reports, or troubleshoot refresh failures.
AI驱动的SVG文章配图生成工具，支持动态SVG、静态SVG和PNG导出三种输出模式。当用户需要为文章生成配图、创建SVG插图、将SVG转换为PNG，或提到"为文章配图"、"生成插图"时使用此技能。
