Patterns for keeping READMEs accurate as code evolves. Focused on structural consistency, not prose style. Use when editing a README with ASCII tree diagrams, version callouts, config examples, or cross-document line ranges. Avoids generic "write clearly" advice — targets the concrete rot patterns that accumulate in docs-heavy repos.
Aggressive optimization patterns for atlas. Overrides standard Python defaults with zero-dep, startup-time, and memory-efficient alternatives. Honed for a project that runs a read-only parquet-backed RAG bundle loaded into numpy at startup and queried via MCP. Use when making any code change — these patterns take precedence over generic Python best practices.
Write and maintain Architecture Decision Records (ADRs) for the atlas project. Covers ADR format, where to store them, when to create one, and examples based on past decisions. Use when making a significant technical decision that should be recorded for future maintainers.
Add a new MCP tool to the atlas-fs or atlas-rag server. Covers tool definition, handler function, parameter schema, and register call. Use when implementing a new feature that needs to be exposed as an MCP tool.
Build the portable RAG bundle: clone docs, chunk, embed, package tarball. Use when the task involves building a bundle locally (`atlas-build`), smoke testing a bundle (`atlas-smoke`), or adding a new publication.
Manage CI bundle releases: manual dispatch, tag naming, monitoring the build-bundle workflow, promoting a release, and rolling back. Use when releasing a new bundle, debugging a CI failure, or rolling back to a previous bundle.
Evaluate RAG quality: run Precision@10 and MRR against a bundle, interpret the scores, and compare CI-built vs locally-built bundles. Use when assessing bundle quality, comparing backends, or investigating a regressed search result.
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.