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Deep file-focused code review for correctness bugs. Unlike shallow-review which runs 6 specialists in parallel across the entire patch, deep-review focuses on user-specified files with full pattern catalogs (500+ patterns), codebase investigation, and source-level context gathering. Use when: the user specifies particular files for focused review, a shallow review flagged areas that need deeper investigation, reviewing critical-path code changes, examining complex serialization/lifecycle/state-machine changes. The user instructs which files to focus on (typically a subset of files in the patch).

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Create, run, and verify TLA+ and PlusCal formal specifications. Use for modeling distributed systems, protocols, concurrent algorithms, state machines. Can compose specs from code, find divergences between spec and implementation, spot concurrency bugs and invariant violations. Use when asked to "write a TLA+ spec", "model check", "verify protocol", "find race conditions", or "formal verification".

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Multi-pass deep code review for large patches (1000+ LOC) that maximizes real bug detection. Orchestrates targeted-review, shallow-review, and deep-review in parallel across all HIGH and MEDIUM risk files and commits: understands the feature holistically, splits by file and commit, runs deep review on every HIGH/MEDIUM file, targeted review across the same scope, and per-commit shallow review, followed by cross-cut consistency checks. Use when: reviewing a large patch (feature branch, multi-commit, or single large diff, 1000+ LOC), doing a thorough pre-merge review, or when shallower reviews miss bugs due to patch size. Triggers on: "review this branch", "review these commits", "review this feature", "mega review", "thorough review", "full review of X commits", or when the user specifies a commit range or feature for review.

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Mine bug patterns from any git repository. Discovers bug-fix commits via git log heuristics, analyzes each in parallel with subagents, writes individual analysis files, and synthesizes a generalized PATTERNS.md with repo-specific details stripped. Invoke explicitly with /bug-archaeology.

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Findings-driven targeted code review. Understands the patch via patch-explainer and surrounding code via codebase-analysis, then consults a catalog of bug patterns (~11 categories under references/categories/). Picks only categories whose diff-signals match the patch and items whose "look for" hints match actual code shapes, groups them by review focus (function/file/feature), and spawns parallel subagents — each with a tight scope and selective checklist. Use for medium-to-large patches (50-1000 LOC) as a focused alternative to whole-patch ensemble or whole-file deep review. Triggers on: "review this patch/diff/change", "find bugs in this change", "scoped review", "what could go wrong here", "review using findings", or proactive PR review.

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Write minimal, reliable bug reproducers (repros) for any project. Use when: (1) a bug report or issue needs a test that reliably demonstrates the failure, (2) converting a described failure scenario into runnable code, (3) minimizing an existing complex test to its essential trigger conditions. Covers the full repro workflow: failure characterization, scope selection, writing the repro, verifying it fails for the right reason, and minimizing to the smallest possible trigger.

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Use git heatmap analysis to identify high-churn files and lines as candidates for thorough review or bug hunting. Works for PR reviews, security audits, bug hunts, or any code analysis task.

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Comprehensive guide for writing Apache Cassandra in-JVM distributed tests (dtests). Use when creating tests that simulate multi-node Cassandra clusters within a single JVM for faster integration testing. Covers cluster creation (single-node, multi-node, multi-datacenter), configuration (all cassandra.yaml parameters, features, network topology), instance lifecycle (startup/shutdown/restart), query execution, message filtering for failure scenarios, running code on instances, ClusterUtils utilities, and debugging classloader-related issues (serialization failures, same-class-different-classloader problems).

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Draft or submit Apache SeaTunnel pull requests in English using the repository's required title and body conventions. Use when Codex needs to prepare a PR from the current branch or diff, infer a compliant `[Type][Module][SubModule] Description` title, fill the required PR template, describe what changed from git history or file diffs, or open the PR after the user provides or omits the change summary.

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Write or review Apache SeaTunnel E2E (Testcontainers) and unit tests so they are stable, leak-free, and deterministic. Use when creating a new *IT/*Test class, reviewing a test diff, or refactoring a flaky or leaking test.

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Patterns and review checklist for the `datafusion-ffi` crate. Use whenever the user adds, edits, or reviews code under `datafusion/ffi/` — new `FFI_X` wrappers, `ForeignX` implementations, codec changes, or expanding an existing wrapper to cover more of a trait's surface. Also use when reviewing PRs that touch this crate.

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Audit a datafusion-spark function implementation for correctness against Apache Spark 4.2.0. Studies the Spark source across versions, reviews the Rust implementation and its signature, verifies expected values against a real PySpark, fixes divergences, and captures the rest as issues and disabled tests.

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Review Apache DataFusion pull requests following the project's PR review guide. Use whenever asked to review a DataFusion PR or PR URL, and whenever creating a PR, to check the changes against the same criteria before submitting.

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Guidelines for designing or extending DataFusion benchmarks.

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Guide on how to add and propagate new metadata fields in Apache Beam's WindowedValue, extending protos, windmill persistence, and runner interfaces to avoid metadata loss.

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Guides Java SDK development in Apache Beam, including building, testing, running examples, and understanding the project structure. Use when working with Java code in sdks/java/, runners/, or examples/java/.

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Guides YAML SDK development in Apache Beam, including environment setup, testing, and key concepts. Use when working with Beam YAML code in sdks/python/apache_beam/yaml/.

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Guides understanding and working with Apache Beam runners (Direct, Dataflow, Flink, Spark, etc.). Use when configuring pipelines for different execution environments or debugging runner-specific issues.

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Guides Python SDK development in Apache Beam, including environment setup, testing, building, and running pipelines. Use when working with Python code in sdks/python/.

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Guides the contribution workflow for Apache Beam, including creating PRs, issue management, code review process, release cycles, and rigorous evaluation rules for high-risk core component changes. Use when contributing code, creating PRs, or modifying core Beam components.

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Guides development and usage of I/O connectors in Apache Beam. Use when working with I/O connectors, creating new connectors, or debugging data source/sink issues.

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Guides understanding and working with Apache Beam's CI/CD system using GitHub Actions. Use when debugging CI failures, understanding test workflows, or modifying CI configuration.

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End-to-end guide on developing new Apache Beam I/O connectors correctly, including core IO transforms, SchemaTransforms, URN proto definitions, Managed API integration, cross-language expansion service, and testing.

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Ensures all new files include proper Apache 2.0 license headers. Use when creating any new file in the Apache Beam repository.

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