编码规范、框架、重构、代码审查（1.9万 个）
Inspect a codebase to identify its development services, ports, and infrastructure needs, then prepare a single tmux-based launcher with commands for starting, stopping, checking, and managing logs. It also documents how to use the launcher.
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Review agent instructions, skills, tool definitions, and linked docs in a repository. Find conflicts, outdated facts, repeated guidance, and missing context, then produce a scored report with concrete rewrite suggestions.
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Prepare a repository for agent-driven development by improving its documentation map, local development setup, end-to-end checks, verification workflow, and custom lints. The skill assesses what is missing and builds capabilities incrementally, with commit hygiene and ongoing cleanup practices.
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Port C, C++, Python, Go, JavaScript, or TypeScript code to idiomatic Rust while preserving observable behavior. Map types and errors to Rust patterns, carry over or create tests, verify the result when tools are available, and document API mappings and deliberate differences.
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Use this skill to write, review, refactor, debug, or explain Rust code. It applies guidance on ownership, types, errors, APIs, async, unsafe code, and project structure to help produce idiomatic, robust code that is easier to maintain.
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Review Rust files, modules, crates, or diffs for correctness, safety, error handling, API design, idioms, and maintainability. Get prioritized findings with cited rules, locations, concrete code fixes, and a summary of what is already working well.
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Review DataFusion Comet changes that cross the JVM/native boundary, focusing on Arrow data transfer, ownership, lifetimes, offsets, and memory release. It helps identify risks such as leaks, invalid access, or crashes and is used alongside the general Comet PR review skill.
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Review DataFusion Comet pull requests that change memory handling. Check which memory budget is affected, how reservations and pools behave, and how Spark and native code interact, then identify missing evidence or documentation updates.
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Review DataFusion Comet pull requests that change native or JVM shuffle. Check partitioning behavior, block formats, memory and spilling, writer selection, Celeborn paths, tests, and contributor documentation for compatibility risks.
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Connect an existing DataFusion or datafusion-spark function to a Spark expression in Comet. Choose a direct mapping, UDF registration, or custom serde for input handling and restrictions, then add SQL file tests. It assumes the upstream function already exists.
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Assess whether a Spark expression routed through Comet’s JVM codegen dispatcher merits a native Rust path. Weigh exact Spark compatibility against likely throughput or allocation gains, record the verdict in the expression audit log, and file a GitHub issue for the recommendation.
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Review a DataFusion Comet pull request with consistent checks for compatibility, release behavior, tests, CI, and documentation. The skill identifies which area-specific review guides apply and prepares findings and suggested comments for a human reviewer.
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Review DataFusion Comet changes to Iceberg writes, including the split write plan, native writer, eligibility checks, and iceberg-rust updates. Assess output parity with iceberg-java, fallback behavior, cleanup on failure, test evidence, and documentation needs.
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Review DataFusion Comet pull requests that add or change Spark expressions. Check the Scala serialization path, Rust implementation, return types, edge cases, tests, and performance against Spark behavior, using the general Comet PR review guide for shared review steps.
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Improve an existing native scalar expression in DataFusion Comet by benchmarking varied input shapes, preserving identical outputs, and checking for regressions. It guides changes to per-row or per-batch expression work such as casts, strings, JSON, arrays, and math.
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Implement a Spark expression in DataFusion Comet by studying Spark’s behavior, reusing an upstream datafusion-spark function when possible, and wiring the Scala serde and Rust function. Add SQL tests, then use an audit workflow to find and address coverage gaps.
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Classify open Comet issues awaiting triage as bugs or enhancements, apply the project's type, priority, and area labels, and record decisions and skipped issues in a dated summary issue for human review.
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Add integrations to an existing TanStack CLI project. Resolve valid add-on IDs, check required dependencies and options, and account for the project metadata the command needs so the additions can be configured correctly.
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Construct a deterministic `tanstack create` command for a new app by selecting its framework, build tool, template, toolchain, deployment target, and add-ons. The skill accounts for incompatible options and non-interactive defaults so the generated scaffold better matches the requested setup.
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Use this skill to choose valid TanStack CLI integrations for a new app. It maps ecosystem partners to installable add-on IDs, checks provider options, and accounts for exclusive categories and router-only compatibility limits.
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Use this skill to set up and iterate on custom TanStack add-ons. It guides initialization, compilation, development loops, and syncing watched framework files into a sandbox app, while checking project metadata and valid watch paths.
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Use TanStack CLI to discover valid library identifiers, fetch versioned documentation, search docs, and inspect available add-ons in machine-readable form. This gives an agent authoritative project context and helps validate library or integration choices before generating code.
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Combine the commits for one change into a single commit, run the project’s Gerrit review checks, and push the result to Gerrit drafts. The workflow also handles staged or unstaged tracked changes and preserves Gerrit commit-message trailers.
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Use this skill to compile one C++ source file in the Impala repository as a quick error check. It normalizes the filename, verifies the build command with a dry run, and reports the build result. It requires the configured Impala environment and its provided script.
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