编码规范、框架、重构、代码审查（1.6万 个）
Review open pull requests whose latest commit has not been checked, or select one by number. Apply repository review rules, inspect surrounding code, and preview findings before optionally posting inline comments on GitHub.
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Turn accumulated code maintenance problems into a prioritized ledger. Findings cite files and lines, include severity and estimated effort, and distinguish quick wins from larger fixes. Repeat audits track resolved, new, and continuing issues against the previous ledger.
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Turn a feature request or bug fix into a structured implementation plan with goals, exclusions, risks, acceptance criteria, and verification steps. Break work into checkable actions and prepare a handoff after the user accepts the plan.
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Prepare a debugging plan for native program crashes and memory corruption. Organize competing explanations, identify observations that could disprove each one, and specify debugger steps for an executor to collect evidence.
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Turn an app idea into a staged delivery plan, from product decisions to implementation handoff, verification, release, and monitoring. Define acceptance conditions and track which steps are prepared, observed, blocked, or still missing.
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Ask Jev to assess a failed run using its command, error excerpt, and retry history. Get a suggested next step—retry, fix dependencies, request access, or change approach—alongside the rule behind that decision.
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Organize agent sessions, MCP connection settings, and Git worktrees into a shared inventory. Compare metadata across tools, distinguish observed information from missing or stale entries, and identify configuration differences before assigning more work.
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Turn a public chat report into a GitHub issue package. Classify the problem, ask focused questions, check for duplicates, and confirm scope before handing one issue creation request to an authorized connector, then verify the result.
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Plan repairs for Git conflicts, broken commits, and branch history. Identify which changes are already shared, define recovery points before rewriting history, and specify checks for finding the commit that introduced a bug.
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Draft commit messages and pull request descriptions using the repository’s existing conventions. Explain why the change matters and separate commands with observed execution records from planned or skipped checks, so validation claims match the available evidence.
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Plan a behavior-preserving refactor of oversized UI components, addressing state management before hooks, component extraction, and naming. Preview changes with safety reasons, then hand off approved implementation with tests that check behavior before and after.
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Turn a repository's actual code structure into one readable PlantUML diagram and rendered PNG. Show packages or modules, public elements imported elsewhere, and dependency links, with a visible legend explaining which details were omitted to keep the picture readable.
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Explore an unfamiliar code repository and build a practical guide to its structure, reading order, terminology, risks, and unknowns. Use observed files to suggest low-risk starter tasks and the commands needed to verify them.
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Analyze failed builds and CI checks, including automated tests, using fresh logs. Group failures by likely cause, distinguish newly introduced issues from existing ones, and prepare a minimal repair handoff with ordered verification steps.
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Review pull requests, code changes, or documented claims for bugs using concrete evidence. Receive prioritized findings, a separate assessment of whether requirements are met, and clear lists of checked areas, unanswered questions, and testing gaps.
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Prepare updates to a local code index, a map used to navigate a repository. Summarize structure and task-relevant context for handoff, while distinguishing planned commands from verified outputs and generated files.
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Compare Codex, Claude Code, Hermes coding, and oh-my runtimes before assigning development work. Identify missing tools or credentials, clarify how work will be handed over, and distinguish preparation from confirmed execution.
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Investigate incorrect application behavior by recording a reproduction, comparing competing explanations, and choosing low-cost observations that distinguish them. Prepare a fix only after evidence identifies the cause, carrying the reproduction forward as a regression test.
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Create or update repository instructions for AI agents, documenting knowledge the code cannot explain. It preserves handwritten sections, labels commands by verification status, and records how generated files are rebuilt and checked.
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Guide cleanup of redundant, unused, or low-quality code while preserving observable behavior. Classify problems, remove them in small passes, and compare before-and-after checks, with a separate review of drafted cleanup changes.
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Plan server, API, and data changes before implementation. Define who can access each operation, consistent response formats, error handling, and database migration steps, with compatibility plans for existing consumers and rollback points for storage changes.
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Examine a technical proposal through independent perspectives, challenge their findings, and resolve objections. The result is a structured set of constraints, decisions, risks, and open questions for a separate planning workflow.
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Test one uncertain technical choice through a small, isolated experiment before planning. Record what actually happened with an API, interaction, performance question, or integration, then prepare a decision record and handoff for the next planning step.
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Find disk space and Git clutter left by AI coding work, including old worktrees, merged branches, build artifacts, caches, archives, and session logs. Review a scan plan, confirm selected items, then remove only items that remain eligible when checked again.
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