编码规范、框架、重构、代码审查（1.6万 个）
Address repeated instructions or recurring corrections by turning rules into automated checks, metadata flags, or scripts. When a rule requires human judgment, make the instruction more prominent and add an example showing the failure to avoid.
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Collect review feedback and discussion comments from the pull request for the current branch. Get a grouped summary, a prioritized action list, and unresolved questions that need clarification before you proceed.
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Revisit an existing design as though a new requirement had been present from the start. Examine affected files, plan the overall redesign, and carry changes through types, documentation, examples, and design rationale in incremental steps.
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Reviews the current conversation from three perspectives to find reusable lessons and propose concrete skill improvements. It separates findings into accepted, rejected, and backlog items, with user approval required before applying accepted edits.
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Review code comments in selected files or changes with a dedicated agent, remove accepted findings, and fix underlying problems. It also proposes ways to enforce documented constraints through types, runtime checks, tests, or automated checks.
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Generate several candidate solutions in parallel, compare them against concrete criteria, and choose a base. Combine useful parts from other candidates, then verify the final artifact and document why each major choice was made.
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Use repeated failed fixes to question their shared assumption. This principle asks agents to state that assumption, test it with a repeatable check, and follow the evidence before attempting another fix based on it.
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Configure which AI models pstack uses for each role, along with reasoning effort, the level of reasoning applied. Detects available models, writes settings for the current environment, and controls the session startup hook where supported.
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Explore a codebase with SocratiCode to locate features, functions, and types. Search by meaning, inspect how files depend on each other, and find relevant database schemas or API specifications before reading selected files.
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Set up and maintain SocratiCode indexes, which prepare project files for search. Manage file watching, refresh changed content, rebuild dependency graphs, and check infrastructure health when indexing or search results need attention.
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Turn a completed workflow or conversation into a reusable agent skill. Clarify inputs, outputs, dependencies, and error handling, then produce an approved design, written instructions or helper scripts, and validation based on the original process.
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Prepare the Python package manager uv for skills that depend on it. This skill checks for an existing installation, installs uv if needed, and configures the command search path so uv commands are available.
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Check maintainer documentation locally before automated CI checks run. Find missing metadata, outdated review dates, nonexistent paths in the architecture manifest, and completed plans that still contain unfinished tasks, with guidance for correcting common failures.
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Fill missing interface translations in a viewer JSON file using English and Chinese source strings. Preview missing entries, translate into six languages through OpenRouter, and validate the updated file with JSON checks and tests.
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Check whether a pull request is ready to merge by combining local lint, formatting, and tests with GitHub Actions status and PR description requirements. Returns a readiness verdict with specific reasons for any failed checks.
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Process GitHub issues from selection through code fixes and pull requests, which propose changes for review. Parallel agents implement and test fixes, while review monitoring handles follow-up comments. Filters, fork support, and recurring checks help organize the work.
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Compare project documentation with code to find outdated descriptions, missing details, and inconsistent references. Review recent changes, audit all documentation, or focus on one area, then apply approved fixes while preserving the existing language and style.
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Delegate feature development, code refactoring, and pull request reviews to Codex, Claude Code, Pi, or OpenCode. It guides background execution, progress monitoring, and parallel work in separate working directories.
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Use the ClawHub command-line tool to find and install agent skills, update them to the latest or a specified version, list installed skills, and publish skill folders with version numbers and change notes.
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Collect, list, and count actionable comments from CodeRabbit and Codex on GitHub pull requests. Separate comments attached to code from review summaries, check Codex status signals, and manage CodeRabbit review conversations.
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Extract skill ideas from recent Claude Code session logs using repeated actions, errors, and automation requests as signals. Remove duplicates, score novelty, feasibility, and trading value, then maintain a prioritized backlog for skill generation.
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Generate a Claude CLI prompt from a structured JSON skill idea. The prompt guides creation of a complete skill directory, including SKILL.md, reference documents, helper scripts, and test scaffolding that follows repository conventions.
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Implement work described in a specification or task tickets, using test-driven development where possible. Check types and individual tests during development, run the full test suite at the end, review the changes, and commit to the current branch.
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Turn a plan, specification, or conversation into small development tickets that each deliver a verifiable feature. Identify prerequisite tickets, review the breakdown with the user, and publish approved tickets as local files or tracker issues.
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