Native macOS/iOS app performance profiling via xctrace/Time Profiler and CLI-only analysis of Instruments traces. Use when asked to profile, attach, record, or analyze Instruments .trace files, find hotspots, or optimize native app performance without opening Instruments UI.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Goal: record Time Profiler via xctrace, extract samples, symbolicate, and propose hotspots without opening Instruments.
# Start app yourself, then attach
xcrun xctrace record --template 'Time Profiler' --time-limit 90s --output /tmp/App.trace --attach <pid>
xcrun xctrace record --template 'Time Profiler' --time-limit 90s --output /tmp/App.trace --launch -- /path/App.app/Contents/MacOS/App
scripts/extract_time_samples.py --trace /tmp/App.trace --output /tmp/time-sample.xml
# While app is running
vmmap <pid> | rg -m1 "__TEXT" -n
scripts/top_hotspots.py --samples /tmp/time-sample.xml \
--binary /path/App.app/Contents/MacOS/App \
--load-address 0x100000000 --top 30
--launch.xcrun xctrace help record and xcrun xctrace help export show correct flags.scripts/record_time_profiler.sh: record via attach or launch.scripts/extract_time_samples.py: export time-sample XML from a trace.scripts/top_hotspots.py: symbolicate and rank top app frames.__TEXT load address from vmmap.--binary path; symbols must match the trace.atos.还没有评价。欢迎分享你的使用感受。
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Use when evaluating, designing, or pressure-testing the business model of an AI agent product. Triggers on "agent business model", "agent economics", "agent canvas", "evaluate agent product", "agent pricing", "agent unit economics", "agentic business", "AI agent company", "agent cost structure".
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Create user-facing App Store release notes by collecting and summarizing all user-impacting changes since the last git tag (or a specified ref). Use when asked to generate a comprehensive release changelog, App Store "What's New" text, or release notes based on git history or tags.
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Use when someone wants to launch a content brand from scratch on any platform. Triggers on "launch a channel", "start a YouTube channel", "build a podcast", "start a newsletter", "content brand", "creator launchpad", "build my audience", "start creating content", "grow on TikTok", "start a blog".
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Use when you have an existing pipeline DOT file that needs review, fixing, or validation. Use when validation produces warnings or errors, when a DOT file was hand-written or generated by another tool, or when you want to verify a pipeline covers 100% of a spec before running it.
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Use when you want to build a project but don't have a spec yet and need to brainstorm the idea into a design doc structured for pipeline DOT generation. Use when starting from a vague idea, project concept, or feature request that needs to become a headless autonomous build pipeline.
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Use when you have a spec, design doc, structured plan, or prompt_plan and need to generate a pipeline Graphviz DOT file for headless autonomous building. The agent reads the spec, extracts build pipeline phases, determines tech stack, asks about human gates and model preferences, generates a validated DOT digraph with rich self-contained prompts, and saves it to the project root. Handles any language or framework.
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