125 skills (repositories by apache) · sorted by popularity
Analyze Git history to find frequently and recently changed files and lines, then rank review targets. Use the results to focus pull request reviews, bug investigations, or security audits on specific code regions and risks.
Turn bug reports into small, runnable tests or scripts with clear failure checks. Verify that they expose the reported problem, then reduce inputs and setup while preserving the same failure.
Analyze bug-fix commits in a Git repository to uncover recurring failure patterns. Produce individual bug analyses and a reusable pattern guide that removes project-specific details, helping inform future code reviews.
Run a quick first review of code changes with six specialist agents working in parallel. They check logic, input and output, shared state, resources, missing behavior, and API completeness, then combine duplicate findings into a report ranked by confidence.
Review selected files for bugs by reading full source code, callers, and related implementations. Apply detailed checklists and investigate suspected issues, then report evidence, confidence levels, and suggested fixes.
Model distributed systems and concurrent algorithms in TLA+ or PlusCal, then check possible states with TLC. Use the results to investigate deadlocks, broken rules, and differences between specifications and implementation code.
Write Apache Cassandra distributed tests that simulate multiple nodes within one JVM, the Java runtime. The guide covers cluster setup, queries, node restarts, message dropping for failure scenarios, and troubleshooting problems caused by isolated class loading.
Explain code structure and behavior with text diagrams, including before-and-after views of changes. Trace data flow and state changes, clarify design choices, and examine assumptions, possible failures, and interactions between tasks running at the same time.
Review changes of 1,000 lines or more by dividing work across files, focused areas, and commits. Combine several review methods, check consistency across subsystems, verify bug fixes, and report validated findings alongside a checklist showing review coverage.
Write and review Apache SeaTunnel unit tests and E2E tests that check complete jobs using Testcontainers. It provides concrete rules for container setup, waiting, resource cleanup, and assertions to address intermittent failures and resource leaks.
Prepare English pull requests for Apache SeaTunnel from a change summary, branch history, and file differences. It builds titles and bodies that follow project conventions, records actual testing, and can submit the pull request when requested.
Guide changes and reviews in datafusion-ffi, which connects separately compiled libraries. Check binary compatibility, memory ownership, and method coverage, and choose unit and cross-library tests for new wrappers, trait extensions, or codec changes.
Design or extend DataFusion benchmarks using SQL where practical. Select representative workload variations, keep unrelated processing lightweight, and integrate runs with bench.sh so measurements reflect useful execution paths and remain practical to run.
Review Apache DataFusion pull requests using the project's review guide. Gather the proposed changes, discussion, and automated check results, or apply the same review criteria to your own changes before submitting a pull request.
Audit one datafusion-spark function against Apache Spark 4.2.0. Compare source code, accepted types, and edge cases, verify expected results with PySpark, then fix differences and record remaining gaps as issues and disabled tests.
Guides adding metadata, extra information attached to data elements, to Apache Beam's WindowedValue. Maps the changes needed across serialization, storage, processing, and runner interfaces so new fields remain available during pipeline execution.
Develop and troubleshoot Apache Beam's YAML SDK. Understand its source files, run data processing pipelines locally or on Dataflow, test changes, and investigate provider issues or mismatched record fields.
Configure and troubleshoot Apache Beam runners, the engines that execute data processing pipelines. Covers local testing, Dataflow, Flink, and Spark, with Java and Python examples, runner validation tests, and guidance on logs and execution settings.
Develop and verify changes to Apache Beam's Python SDK. Set up a virtual environment, run tests, build installable packages or containers, and run data processing pipelines with the modified SDK.
Helps add Apache 2.0 license headers to new Apache Beam files using the correct comment format and placement. Covers source files, configuration, documentation, and local checks for missing license notices.
Guides Java SDK development in Apache Beam, from finding relevant modules to building code, running focused tests, and trying examples. Also explains how to publish modified modules locally and build SDK containers.
Work with Apache Beam I/O connectors, components that read and write external data. Get guidance on connector usage, unit and integration tests, troubleshooting data connections, and the basic structure of new connectors.
Build Apache Beam connectors that read and write external data. This guide covers Java implementation, schema support for structured records, access from Python and Go, Managed API integration, and testing.
Build and test Apache Beam with Gradle, the system that manages its project tasks. Find relevant configuration files, inspect library dependencies, run formatting checks, publish locally, and investigate build failures.