编码规范、框架、重构、代码审查（1.6万 个）
Turn records from a completed project phase into a structured LEARNINGS.md file. Extract decisions, lessons, recurring patterns, and unexpected findings from planning documents, implementation summaries, verification records, and acceptance test results.
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Execute a development phase by grouping plans into waves based on dependencies and assigning them to subagents. Collect results and handle checkpoints, with options to run one wave, focus on fixes, or work sequentially with user input.
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Fix issues recorded in a phase’s REVIEW.md, commit each fix separately, and produce a summary. Choose which severity levels to include, or repeat fixes and reviews for up to three iterations.
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Handle repository change requests from another Claude session. Guides user approval, clarification of missing API details, negotiation of proposed changes, and precise replies listing changed files, agreed behavior, verification results, and deviations from the request.
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Audit registered projects, turn outstanding issues into tasks with clear dependencies, and assign work to parallel agents. Review their changes, check automated tests, and move pull requests through merging and deployment verification.
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Review issues from Sentry, Linear, and GitHub Issues against code and deployment evidence. Close confirmed fixes after approval, organize remaining issues by priority, and dispatch agents to prepare fixes and pull requests.
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Run remaining project phases through discussion, planning, and execution. Track progress and phase documents, then audit and close the milestone. Select a phase range or use interactive discussion when decisions need your input.
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Review open GitHub pull requests across repositories, assign agents to fix automated checks and review feedback, and merge after confirmation. Also supports conflict handling, previewing planned actions, and optional synchronization between dev and main branches.
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Audit a development milestone before archiving it by comparing completed work with its original goals. Review requirement coverage, connections between phases, and complete user flows, while collecting technical debt and deferred gaps.
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Analyze which development phases must precede others by checking shared files, API usage, and data flow. Suggest dependency entries for ROADMAP.md and apply confirmed changes to clarify the order of work.
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Coordinate changes with a Claude session working in another repository. Specify API inputs, outputs, authentication, and errors, then review the reply and verify the implementation before building work that depends on it.
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Query and update Supabase data, inspect table structures, and generate TypeScript types through Rube MCP. Review project settings and service health, and inspect storage bucket lists and Edge Functions, code hosted by Supabase.
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Plan system architecture and APIs, compare design patterns, and record decisions with their context and rationale. Check scalability, reliability, monitoring, and security to choose a simple structure that meets the project's requirements.
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Turn a codebase into Markdown guides for operation, deployment, architecture, and maintenance, plus an index.json for AI agents. Operational claims include source references and confidence labels, while unverified deployment details remain documented gaps.
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Create structured Claude skill packages, validate their files and metadata, and bundle them as ZIP archives. Share the new skill’s description and file links in a Slack channel through Rube so teammates can discover it.
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Build MCP servers, which expose external services as tools for AI agents. Follow a structured process covering API research, tool design, Python or TypeScript implementation, code review, and realistic evaluations of how agents use the tools.
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Understand a TypeScript codebase using Ontoly's Software Graph, a map of code elements and their relationships. Explain architecture, trace request flows, inspect dependencies, and assess change impact while distinguishing reported facts from inference.
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Create or update Claude skills that package specialized knowledge, repeatable workflows, and tool instructions. Work from concrete usage examples to organized instructions and reusable resources, then validate the skill and package it as a distributable ZIP.
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Investigate past agent runs using recorded tool calls, exit codes, and file changes. Replay failures, distinguish observed evidence from inferred causes, and compare models from the same saved point when requested.
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Analyze recent Claude Code conversations to identify coding habits, strengths, and specific learning gaps. Receive prioritized improvement suggestions and relevant HackerNews reading recommendations in a personalized report delivered to Slack direct messages.
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Delegate reviews, research, and other tasks from Claude Code to Codex or Antigravity CLI. Claude evaluates the responses, explains agreements and disagreements, and preserves separate conversations for follow-up questions on each topic.
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Prepare the current project for uc-taskmanager by creating its works/ directory and, with approval, adding file access and Bash permissions to local Claude settings. Preserve existing permission entries and skip duplicates.
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Start a structured development workflow by adding a square-bracket tag to a request. The skill routes requirements through specialized agents for implementation, verification, and commits, with approval checkpoints determined by the selected execution mode.
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Turn implementation requests into bounded tasks for coding agents, with clear file ownership, acceptance criteria, and validation. Review changes for bugs and scope drift, and organize recovery when an agent’s work becomes difficult to trust.
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