全部能力

This skill should be used when user asks to "search for papers", "find research papers", "search arXiv", "search PubMed", "find academic papers", "search…

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering,…

Investigate and answer repository questions in read-only mode. Use when asked for research-backed answers that require codebase and documentation investigation…

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.

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and…

Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures,…

Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with…

PostgreSQL-based semantic and hybrid search with pgvector and ParadeDB. Use when implementing vector search, semantic search, hybrid search, or full-text search in PostgreSQL. Covers pgvector setup, indexing (HNSW, IVFFlat), hybrid search (FTS + BM25 + RRF), ParadeDB as Elasticsearch alternative, and re-ranking with Cohere/cross-encoders. Supports vector(1536) and halfvec(3072) types for OpenAI embeddings. Triggers: pgvector, vector search, semantic search, hybrid search, embedding search, PostgreSQL RAG, BM25, RRF, HNSW index, similarity search, ParadeDB, pg_search, reranking, Cohere rerank, Voyage rerank, graceful fallback, iterative_scan, filtered HNSW, websearch_to_tsquery, unaccent, multilingual FTS, pg_trgm, trigram, fuzzy search, LIKE, ILIKE, autocomplete, typo tolerance, fuzzystrmatch, evaluation, benchmarking, Hit@K, MRR, contextual embeddings, halfvec cast

Retrieve sector P/E ratios using Octagon MCP. Use when comparing company valuations to sector benchmarks, analyzing sector valuations across exchanges, and…

Compile a LaTeX project and run basic QA (missing refs, bib errors, broken citations), producing `latex/main.pdf` and a build report. **Trigger**: latex compile, build PDF, LaTeX errors, missing refs, 编译PDF, 引用错误. **Use when**: 已有 `latex/main.tex`（通常来自 `latex-scaffold`），需要确认可编译并输出失败原因报告。 **Skip if**: 还没有 LaTeX scaffold（先跑 `latex-scaffold`）。 **Network**: none. **Guardrail**: 编译失败也要落盘 `output/LATEX_BUILD_REPORT.md`；不做“内容改写”，只做编译/QA。

This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection,…

Real-time Korean law lookup skill using Beopmang API v4 (api.beopmang.org). Searches statutes, articles, court cases, and revision history. Converts everyday Korean to legal keywords via keyword-map, verifies legal claims, and falls back to web search for the latest or unsupported data. No API key required. Triggers on Korean law names (민법, 근로기준법, 형법, etc.), legal questions (합법이야, 해도 돼, 처벌, 권리), and statute citations.