- Skills
- 413
- 仓库
- 74
- 职业覆盖
- 8
- 场景覆盖
- 7
- Stars
- ★ 30w
The Apache Software Foundation 的 Skills 413
targeted-review
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.
shallow-review
Shallow (quick) ensemble bug-finding review using 6 specialist agents in parallel. Each specialist reviews the same patch through a different lens: Logic & Types, Boundaries & I/O, Concurrency & State, Resources & Serialization, Absence Analysis, and API Completeness. Findings are merged and deduplicated. Best for: quick first-pass review of patches, triage of diffs, broad surface-level bug scan. For deeper file-focused review with full pattern catalogs and codebase investigation, use the deep-review skill instead.
patch-explainer
Deep code analysis with ASCII visualizations showing structure, flow, and state transitions. Use when analyzing patches/diffs, explaining classes or subsystems, understanding code architecture, reviewing changes for inconsistencies, or when asked to visualize how code works. Provides before/after diagrams, data/control flow, concurrency analysis, assumptions, and failure modes. Triggers on explain this patch/code/class, how does X work, show me the flow, visualize this change, code review requests, or proactive analysis during PR reviews.
mega-review
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.
heatmap
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.
deep-review
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).
cassandra-injvm-dtest
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).
bug-archaeology
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.
seatunnel-test-suite
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.
seatunnel-pr-submit
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.
pr_review
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.
datafusion-ffi
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.
audit-datafusion-spark-expression
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.
add-benchmarks
Guidelines for designing or extending DataFusion benchmarks.
yaml-development
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/.
runners
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.
python-development
Guides Python SDK development in Apache Beam, including environment setup, testing, building, and running pipelines. Use when working with Python code in sdks/python/.
license-compliance
Ensures all new files include proper Apache 2.0 license headers. Use when creating any new file in the Apache Beam repository.