/profile-dart-code

name: profile-dart-code
description: |-
  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.
key_features:
  - Automated VM Service WebSocket connection
  - CPU sampling and top-function call summary
  - JSON trace export for further analysis

Dart CPU Profiling

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

When to use this skill

• When asked to profile, optimize, or benchmark CPU execution of a Dart script

or CLI tool.

• When investigating hot loops, heavy function calls, or unexpected execution

overhead.

When NOT to use (Abstention Guardrails)

Do NOT profile using this skill when:

• **Pure I/O-Bound Bottlenecks**: The performance bottleneck is network latency,

database queries, or disk I/O wait rather than CPU execution.

• **Flutter UI Applications**: The target is a Flutter application requiring

frame profiling, raster thread inspection, or widget rebuild tracking (use Flutter DevTools or `widget_inspector`).

• **Short-Lived Micro-Benchmarks**: Micro-benchmarks running for only a few

milliseconds where VM warmup and sampling overhead skew results (use `package:benchmark_harness` or `package:bench_press` instead).

• **Target Does Not Run Cleanly**: If the target script fails to compile or

crashes on startup, fix functional bugs before attempting CPU profiling.

Workflow

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

Running the Profiler Helper Script

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

Run it from any working directory:

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

Script Arguments

• `-o, --out=<file>`: Output file path to save the raw JSON CPU profile

(default: `cpu_profile.json`).

• `-p, --period=<micros>`: Sampling interval in microseconds (default: `1000`µs

= 1ms). Minimum `50`µs.

• `-- <target.dart> [args...]`: The Dart script to profile, followed by any

arguments passed to `main()`.

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

Example Output

Connecting to VM service at ws://127.0.0.1:8181/ws...
Target execution paused at exit. Retrieving CPU profile samples...

=== Top CPU Functions (Self Samples) ===
 1. _PuzzleSmart._shiftSlice (self: 34.2%, total: 41.0%)
 2. _countInversions (self: 18.5%, total: 18.5%)
 3. shortestPaths (self: 12.1%, total: 98.4%)

Saved complete JSON profile to: cpu_profile.json

Best Practices for Interpreting Profiles

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