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Found 2,175 Skills
Automated, project-wide code coverage and CRAP (Change Risk Anti-Patterns) score analysis for .NET projects with existing unit tests. Auto-detects solution structure, runs coverage collection via `dotnet test` (supports both Microsoft.Testing.Extensions.CodeCoverage and Coverlet), generates reports via ReportGenerator, calculates CRAP scores per method, and surfaces risk hotspots — complex code with low test coverage that is dangerous to modify. Use when the user wants project-wide coverage analysis with risk prioritization, coverage gap identification, CRAP score computation across an entire solution, or to diagnose why coverage is stuck or plateaued and identify what methods are blocking improvement. DO NOT USE FOR: targeted single-method CRAP analysis (use crap-score skill), writing tests, running tests without coverage collection, applying test filters, producing TRX reports, or troubleshooting test execution (use run-tests for all of these).
Estimates storage requirements for CockroachDB online schema change backfills using SHOW RANGES WITH DETAILS, KEYS, INDEXES. Use before CREATE INDEX, ADD COLUMN with INDEX/UNIQUE, ALTER PRIMARY KEY, CREATE MATERIALIZED VIEW, CREATE TABLE AS, REFRESH, or SET LOCALITY on tables with large per-index footprints, to avoid mid-backfill disk exhaustion.
Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (formerly neo4j-genai). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever), retrieval_query Cypher fragments, query_params, pipeline wiring (GraphRAG + LLM), embedder setup, index creation, and LangChain/LlamaIndex integration. Does NOT handle KG construction from documents — use neo4j-document-import-skill. Does NOT handle plain vector search — use neo4j-vector-index-skill. Does NOT handle GDS analytics — use neo4j-gds-skill. Does NOT handle agent memory — use neo4j-agent-memory-skill.
Turn vague "what did I do?" into evidence-backed impact statements for performance reviews, self-reviews, promotion packets, and weekly updates. Uniquely mines Copilot CLI session logs to reconstruct forgotten work, plus git commits and GitHub PRs. Enforces a 3-part impact contract (action → result → evidence). Works standalone with zero dependencies. Trigger for: "brag", "log work", "what did I do", "backfill my work history", "performance review", "self-review", "self assessment", "write impact statement", "review prep", "promo packet", "promotion case", "weekly update", "status report", "accomplishments", "what did I ship", "I forgot to log my work", "summarize my work", "track my wins", "what should I highlight", "end of half", "career growth", "work journal", or any request to document, summarize, or organize work accomplishments.
Evaluate test coverage and fill real gaps with high-value tests.
Implement GraphRAG patterns combining knowledge graphs with retrieval for complex reasoning. Use this skill when building RAG over interconnected data or needing relationship-aware retrieval. Activate when: GraphRAG, knowledge graph, graph retrieval, entity relationships, Neo4j RAG, graph database, connected data.
This skill should be used when the user asks to "build a RAG pipeline", "create retrieval augmented generation", "use ColBERTv2 in DSPy", "set up a retriever in DSPy", mentions "RAG with DSPy", "context retrieval", "multi-hop RAG", or needs to build a DSPy system that retrieves external knowledge to answer questions with grounded, factual responses.
Generate a visual spec-to-code coverage map showing which code files are covered by which specifications. Creates ASCII diagrams, reverse indexes, and coverage statistics. Use after implementation or during cleanup to validate spec coverage.
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
Build RAG systems - embeddings, vector stores, chunking, and retrieval optimization
RAG, embedding, vector search를 통해 사내/최신 데이터를 LLM 응답에 연결하는 방법과 선택 기준을 다루는 모듈.
AI-first security scanning with Medusa. 3,000+ detection patterns covering AI/ML, agents, MCP, RAG, prompt injection, and traditional SAST vulnerabilities. Wraps Medusa CLI with SARIF/JSON parsing, structured finding output, OWASP mapping, and remediation guidance.