全部能力

Ingest and normalize market data into OHLCV vectors with HNSW indexing

Set up or update the agent-first engineering harness for any repository. Implements the complete scaffolding that makes AI coding agents effective: knowledge maps (AGENTS.md as a concise TOC), structured documentation, architecture boundaries, enforcement rules (.harness/*.yml specs), quality scoring, and process patterns for agent-driven development. Use this skill whenever someone wants to make a repo agent-ready, set up AGENTS.md or docs/ structure, define domain boundaries or golden principles, generate .harness/ configuration, audit agent readiness, or update an existing harness. Also trigger when a user reports problems with agent effectiveness, context management, or architectural drift — these are symptoms of a missing or stale harness. Trigger on: "harness this repo", "set up harness", "agent-first setup", "make this agent-ready", "update the harness", "assess agent readiness", "set up AGENTS.md", "organize for agents", or any discussion about structuring a codebase for AI agent workflows.

Documentation generation workflow covering API docs, architecture docs, README files, code comments, and technical writing.

Generate architecture documents using templates with diagram integration. Use for creating C4 diagrams, viewpoint documents, and technical overviews.

Spartan UI (shadcn for Angular) best practices. Covers Brain/Helm architecture, Tailwind integration, and accessible headless components. Activates when working with @spartan-ng/brain and @spartan-ng/ui. Do not use for Angular Material, PrimeNG, or other component libraries. Install alongside angular-best-practices for full coverage.

Generate publication-quality AI illustrations for academic papers using Gemini image generation. Creates architecture diagrams, method illustrations with…

Blog strategy development including topic cluster architecture with hub-and-spoke design, audience mapping, competitive landscape analysis, AI citation surface strategy across ChatGPT/Perplexity/AI Overviews, distribution channel planning (YouTube, Reddit, review platforms for GEO), content scoring targets, measurement framework, and content differentiation through original research and first-hand experience. Use when user says "blog strategy", "content strategy", "blog positioning", "what should I blog about", "blog topics", "content pillars", "blog ideation".

Route durable graph-building requests into one honest mode: assistant-native install, local Python build, incremental refresh, graph query follow-up, or a graphify-style structural fallback for markdown-heavy corpora. Use when the user wants `GRAPH_REPORT.md`, `graph.json`, `graph.html`, repo/corpus relationship tracing, mixed code+docs+asset graphing, or graph-backed architecture understanding that should persist across sessions. Route simple locate/reference work to `codebase-search`, narrative knowledge-base work to `llm-wiki`, and project-memory handoff to `opencontext`.

Autonomous design space exploration loop for computer architecture and EDA. Runs a program, analyzes results, tunes parameters, and iterates until objective is…

Build real deck artifacts when the user needs editable slides, not just prose: investor decks, roadmap/QBR decks, launch decks, architecture/demo decks, workshop/training decks, and game pitch or milestone decks. Use when the job is to choose one deck mode, one smallest useful artifact packet, and one honest handoff surface (HTML review, PPTX, PDF, Google Slides, or Figma Slides). Triggers on: presentation, slide deck, slides, pitch deck, roadmap deck, investor deck, launch deck, architecture review deck, demo deck, workshop slides, keynote, board deck, QBR deck, and game pitch deck. Route long-form docs to `technical-writing`, end-user tutorials to `user-guide-writing`, research manuscripts to `research-paper-writing`, and broad marketing planning to `marketing-automation`.

Decide when repeating a full prompt is a useful accuracy hack for non-reasoning or lightweight LLM tasks, and when the real fix is better prompt structure, retrieval, or a stronger model. Use when the user has long-context retrieval, options-first multiple choice, or position-sensitive prompts on models like Haiku, Flash, or mini variants — even if they ask in domain language like the model missed the instruction at the end, forgot the question after a long context block, or keeps failing inventory/index lookups. Do not use as a universal prompt-engineering default for reasoning-heavy, tool-heavy, or RAG architecture problems.

Expert knowledge for Azure Functions development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas,…

Implements knowledge graphs for AI-enhanced relational knowledge. Covers ontology design, graph database selection (Neo4j, Neptune, ArangoDB, TigerGraph), entity extraction, hybrid graph-vector architecture, query patterns, and AI integration. Use when implementing knowledge graphs, designing ontologies, extracting entities and relationships, selecting a graph database, or building hybrid graph-vector search. Use for knowledge graph, ontology design, entity resolution, graph RAG, hallucination detection. For architecture selection and governance, use the knowledge-base-manager skill. For document retrieval pipelines, use the rag-implementer skill.

Designs production-grade RAG pipelines with chunking optimization, retrieval evaluation, and pipeline architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, designing embedding pipelines, or evaluating RAG performance with RAGAS metrics.

Expert knowledge for Azure Container Apps development including troubleshooting, best practices, decision making, architecture & design patterns, limits &…

Search and manage Confluence pages and spaces using CQL, read/create/update pages with Markdown support. Use when working with Confluence documentation.

Clarify ambiguous or conflicting requests by researching first, then exhaustively interrogating assumptions, constraints, dependencies, trade-offs, edge cases, and failure modes before any planning or implementation. Use when prompts say "$grill-me" or "grill me", ask hard questions, request relentless interrogation, pressure-test assumptions, clarify scope or requirements, define success criteria, or request product/system-design decisions before implementation. Respond in the user's language. Stop before implementation.

Use this skill when designing help center architecture, writing support articles, or optimizing search and self-service. Triggers on knowledge base, help center, support articles, self-service, article templates, search optimization, content taxonomy, and any task requiring help documentation design or management.

Expert knowledge for Azure Monitor development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas,…

Search the code knowledge graph by function, class, or type name using FTS5 full-text search with query-aware kind boosting.