Coding

This skill covers when and how to ask an LLM for machine-readable payloads: define a JSON Schema (or the vendor's equivalent), enable the structured-output feature your provider documents, validate responses in application code, and handle refusals or validation errors explicitly. It applies to tool-calling agents, extraction pipelines, configuration emitters, and any workflow where brittle text parsing creates production risk.

Follow Cursor's official Rules documentation when you want persistent Agent guidance tied to a repository. Project rules encode architecture expectations, risky-folder guardrails, or repeatable workflows; Cursor applies them via Always Apply, intelligent relevance, glob-scoped attachments, or manual @mentions. Use .mdc frontmatter for finer control and reference templates with @file instead of pasting large snippets.

Builds and maintains semantic indexes of a codebase so AI coding assistants can retrieve relevant context—file relationships, symbol usage, historical decisions—without re-parsing the entire codebase on every query. Codebase indexing is essential for large codebases where context window limits prevent feeding the entire codebase to the model.

Builds retrieval-augmented generation pipelines that ground model responses in your own documents rather than generic training knowledge. A RAG implementation covers document ingestion, semantic chunking, embedding, vector storage, hybrid search, reranking, and answer synthesis—so assistants answer from your data with cited sources.

Locks API expectations between services using consumer-driven contracts so that when one team changes their implementation, it fails in CI rather than during a coordinated production deployment. Contract testing prevents the common integration failure pattern where both sides of an API appear to work in isolation but break when connected in production.

Executes refactoring changes in small, test-backed steps so behavior is preserved while structure improves. Each refactoring operation—rename, extract, inline, move—is validated by the test suite before proceeding to the next, preventing the common pattern of refactoring into subtle behavioral regressions that are only caught in production.

Shapes REST or RPC API surfaces with consistent resource modeling, predictable error responses, paginated list endpoints, and an explicit deprecation policy before implementation locks you into contracts that are costly to change. Good API design prevents client breakage, reduces support burden, and makes feature additions less disruptive.

Executes a pre-written implementation plan in disciplined order, stopping at defined checkpoints to verify assumptions before moving forward. This skill prevents the common pattern of diverging from the plan silently when reality proves it wrong, and it creates natural opportunities to course-correct before small errors compound into large rework.

Drives development through red-green-refactor cycles where you write a failing test that names the desired behavior before writing any implementation code. TDD produces tests that document intent, catches regressions immediately, and forces small, verifiable increments—making it especially valuable for complex features, bug fixes with known failure cases, and any code that needs a long-term safety net.