{"slug":"profile-dart-code","title":"profile-dart-code","summary":"Profile Dart command-line applications using the VM Service protocol to capture CPU samples and identify performance bottlenecks. Helps agents automate CPU profiling, generate function call breakdown summaries, and export JSON profiles without a browser or DevTools.","platform":"Claude","tags":[],"authorName":"LLM Mart","authorSlug":"llm-mart","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-09-14T18:30:25.053539Z","repo":{"url":"https://github.com/kevmoo/dash_skills","stars":146,"forks":16,"license":"Apache-2.0","updatedAt":"2026-09-24T00:03:49Z"},"bodyHtml":"
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name: profile-dart-code\ndescription: |-\nProfile Dart command-line applications using the VM Service protocol to\ncapture CPU samples and identify performance bottlenecks. Helps agents\nautomate CPU profiling, generate function call breakdown summaries, and\nexport JSON profiles without a browser or DevTools.\nkey_features:

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Dart CPU Profiling

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Guidelines and automated tools for capturing CPU profiles and identifying\nbottlenecks in Dart command-line applications.

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When to use this skill

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When NOT to use (Abstention Guardrails)

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Do NOT profile using this skill when:

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Workflow

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  1. Ensure clean compilation: Make sure the target Dart script runs cleanly\n(dart run <script.dart>).
    2. \n
  2. Run Profiler Script: Use the automated profiling helper script inside\nthis skill directory to launch the target app with VM Service observability\nenabled, capture CPU samples, and output top-consuming functions.
    3. \n
  3. Analyze & Optimize: Review the self and total sample percentages reported\nby the tool to pinpoint bottlenecks (e.g., excessive object allocation,\ncostly hashing, virtual dispatch overhead).
    4. \n
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Running the Profiler Helper Script

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This repository includes a zero-dependency (using only official vm_service)\nprofiling script that launches any Dart file, connects to the VM Service, waits\nfor execution to complete (--pause-isolates-on-exit), retrieves CPU samples,\nand prints a clean summary while exporting the full JSON profile.

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Run it from any working directory:

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dart run <dash_skills_repo>/skills/profile-dart-code/scripts/bin/profile.dart --out=cpu_profile.json -- <path_to_target.dart> [target_arguments...]\n
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Script Arguments

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[!WARNING] Potential Hangs: When profiling or debugging Dart targets using\nVM services, target exceptions or connection issues can cause the process to\nhang indefinitely. Ensure your target script handles timeouts, and monitor the\nprocess output.

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Example Output

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Connecting to VM service at ws://127.0.0.1:8181/ws...\nTarget execution paused at exit. Retrieving CPU profile samples...\n\n=== Top CPU Functions (Self Samples) ===\n 1. _PuzzleSmart._shiftSlice (self: 34.2%, total: 41.0%)\n 2. _countInversions (self: 18.5%, total: 18.5%)\n 3. shortestPaths (self: 12.1%, total: 98.4%)\n\nSaved complete JSON profile to: cpu_profile.json\n
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Best Practices for Interpreting Profiles

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  1. Focus on Self % vs. Total %: High self % indicates where CPU time is\nspent directly inside a function's own body (math, loop branching, array\nindexing). High total % with low self % indicates a dispatcher or outer\norchestration loop.
    2. \n
  2. Look for Hidden Overhead: Watch out for implicit object allocations\n(_copyData, iterator wrappers, closure creation) inside tight loops.
    3. \n
  3. Verify Optimizations Empirically: Always record baseline sample counts\nand execution duration (time -v) before and after applying optimizations.
    4. \n
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