Flow Parallel
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Decompose and execute large changes, migrations, or multi-issue fixes in parallel with quality gates
智能体技能是一个包含指令、脚本和资源的文件夹,智能体按需加载。与 MCP 服务器不同,它不运行自己的进程,因此在上下文上更省,也更容易审查。
第 745–768 条,共 935 条
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Decompose and execute large changes, migrations, or multi-issue fixes in parallel with quality gates
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NLSpec authoring — use when you need a structured specification from multi-AI research and consensus
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Audit codebases for quality, consistency, and broken patterns — use for pre-release or tech debt review
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Expert multi-AI code review with inline PR comments — use for thorough quality and security analysis
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Extract patterns and anatomy from URLs — use to reverse-engineer content strategies from live pages
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Present options with trade-offs for informed decision-making — use when choosing between approaches
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Generate slide deck presentations from briefs — use when you need slides, pitch decks, or visual summaries
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Thorough research across multiple sources — use for complex topics needing broad synthesis
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Persist design documents with branch tracking, revision chains, and cross-session discovery
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Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables
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Post-ship doc sync across project markdown. Use when: sync docs, update docs, document changes, release notes.
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Wrap up a branch — run tests, create PR, merge or discard — use when implementation is done
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Lock in user goals upfront and validate outputs against them — use to prevent scope drift
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Switch to Knowledge Work mode for research and writing — use when task is non-code focused
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Craft better prompts using proven optimization techniques — use when your prompt needs refinement
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Roll back to a previous checkpoint via git — use when a change went wrong and you need to revert
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Package and finalize completed work for delivery — use when a feature is done and ready to ship
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Review code in two passes: spec compliance then quality — use for thorough PR or feature reviews
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Process screenshot-based UI/UX feedback to fix visual issues — use when users share screenshots of bugs
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System architecture and API design with multi-AI consensus — use for design reviews and new subsystems
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Run a full build-and-ship pipeline from a spec — use for hands-off project generation
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How to handle code review feedback — verify before implementing, push back when wrong, never agree blindly
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Brainstorm creatively with pattern spotting and paradox hunting — use for ideation and exploration
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Evidence before claims — run verification commands before declaring work complete, fixed, or passing