- name
- skill-profiler
- description
- Profile offline OpenClaw skill run samples to detect latency, CPU, and memory bottlenecks and compare sessions.
- homepage
- https://github.com/mike007jd/openclaw-skills/tree/main/skill-profiler
- metadata
- {"openclaw":{"emoji":"📈","requires":{"bins":["node"]}}}
Skill Profiler
Analyze offline sample sets and surface the slowest or heaviest skills before they become production regressions.
When to use
- You have JSON sample logs with
latencyMs,cpuMs, andmemoryMb. - You want threshold-based hotspot detection for local review or CI.
- You need a shareable JSON or HTML report, or a before/after comparison between two runs.
Commands
node {baseDir}/bin/skill-profiler.js run --input ./samples.json
node {baseDir}/bin/skill-profiler.js report --input ./samples.json --out ./report.html
node {baseDir}/bin/skill-profiler.js compare --left ./v1.json --right ./v2.jsonSample input
[
{
"sessionId": "s1",
"skill": "clawshield",
"latencyMs": 1320,
"cpuMs": 910,
"memoryMb": 240
}
]What it reports
- Average latency and p95 latency per skill
- Average CPU and peak memory per skill
- Bottlenecks using configurable latency, CPU, and memory thresholds
- Session diffs showing added, removed, and changed skills
Boundaries
- Skill Profiler is built for offline sample analysis, not live tracing.
- It depends on the quality of the input samples and does not capture traces by itself.