nrminor

Conflict microscope for Jujutsu repos: use when preemptively resolving future rebase conflicts, moving a commit under an active stack, making a later rebase clean, or deciding how to jj absorb/squash conflict resolutions into semantic owners without rebasing the whole stack.

Use when the vocabulary of a codebase is getting too muddy, too difficult to teach, or too difficult to update and is in need of refactoring.

Efficient codebase search using a tiered strategy — ast-grep (structural) and ripgrep (textual fallback) — with VCS-aware tooling. Teaches agents to find information in codebases with minimal token overhead by choosing the right level of search abstraction rather than defaulting to text grep.

Write production logging that actually helps you debug. Teaches the wide event / canonical log line pattern — one rich, structured event per request per service instead of scattered log lines. Covers business context, deployment context, single logger setup, middleware enrichment, tail sampling, and idioms for Rust, Python, Go, and TypeScript. Use when adding logging, instrumentation, or observability to any codebase.

Use when proposing, reviewing, or comparing API designs, refactors, architecture changes, or implementation plans with code sketches. Requires sketches to cover internals, callsites, docs/teaching cost, and library-vs-application API tradeoffs instead of ad hoc snippets.

Jujutsu (jj) version control for the user's preferred workflow — graph-aware, describe-first, split-oriented, with stacked PRs. Use when working with jj repositories, manipulating the commit graph, creating PRs, or any version control operation in a jj repo.

This skill is simply the TigerStyle document from the TigerBeetle project. It is self-describing and should be treated with reverence.

Write TypeScript with the Effect V4 library. Covers the core Effect type, generator syntax (Effect.gen/Effect.fn), typed errors, services and dependency injection via Context/Layer, resource management, streams, HTTP client/server, CLI, child processes, testing, observability, AI modules, and cluster. Use when writing or reviewing Effect code, migrating vanilla TypeScript to Effect, or when you need to understand Effect's service/layer/error model.

High-throughput transient container for read-like genomic records. Use when writing Rust code that needs fast temporary disk persistence for sequencing data, including external sorting, spill/reload pipelines, partitioning, bucketing, or any out-of-core genomics workflow. Also use when the user mentions dryice, temporary FASTQ storage, sequence sorting, record keys, or genomic spill files.