How to Uninstall Claude Code Skills — Remove & Manage

Run a structured code review on your recent changes without waiting for a teammate. This skill checks for security vulnerabilities (SQL injection, XSS, authentication bypasses), logic errors, edge cases, performance issues, and style violations.Findings are organized by severity: Critical, Warning, and Suggestion. Each finding includes the file, line number, a description of the issue, and a concrete fix. Use it as a first pass before peer review, or as your only reviewer on solo projects.

Stop writing vague commit messages. This skill reads your actual staged diff and generates precise, informative commit messages following the Conventional Commits specification. It detects the commit type (feat, fix, refactor, docs, chore, etc.), identifies the scope from the changed files, flags breaking changes, and suggests splitting commits when multiple logical changes are staged. Works with any git repository.`

What This Skill DoesMost RAG systems fail silently — not because the model is weak, but because the retrieval architecture assumes every query is a lookup. This skill teaches you to design hybrid context layers that match the retrieval strategy to the query type, so your agent gets the right kind of context every time.Problems It SolvesThe RAG-for-everything trap — routing relational and temporal queries through vector search causes silent structural failure, expensive reranking, and answers that get worse as you add more context.Multi-hop blindness — "Which teams own services that depend on the deprecated API?" is an entity-traversal query, not a lookup. Vector RAG cannot answer it accurately.Missing organizational causation — questions like "What decisions led to this incident?" require a temporal event graph, not a document chunk.Context-reasoning mismatch — good context routed to a weak reasoning tier, or long context with no causal structure, produces hallucinations at scale.What You GetThe skill defines a three-layer context model:Layer 1 — Factual Store (Vector RAG): Single-fact, single-document point queries — the only case where RAG is structurally correct.Layer 2 — Relational Store (Knowledge Graph): Entity relationships, dependency chains, and multi-hop queries that require traversal across linked nodes.Layer 3 — Temporal/Episodic Store (Timeline Index): Event sequences, causal decision chains, and "how did we get here" queries that require timestamped structure.You also get a query router decision tree — a concrete classification step that routes every incoming query to the correct layer before any retrieval begins, plus a phased implementation roadmap for teams migrating from RAG-only systems.Who Should Use ThisTeams building AI agents over enterprise knowledge bases, architecture decision records, incident histories, or any organizational system where the agent must answer relational or causal questions — not just fact lookups.