Crabpot Perf Metrics

Community

vincentkoc

crabpot-perf-metrics

Interpret Crabpot and OpenClaw performance dashboard metrics, import-loop profiles, runtime profiles, and branch-to-branch perf deltas without over-reading noisy samples.

Overview

Publisher	vincentkoc
Repository	`dotskills`
Skill name	`crabpot-perf-metrics`
Stars	112
Forks	9
Bundled files	2
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by vincentkoc on GitHub. Read the source before you install it.

Installation

Install the Crabpot Perf Metrics AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/vincentkoc/dotskills/tree/main/skills/crabpot-perf-metrics
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/vincentkoc/dotskills.git /tmp/dotskills
mkdir -p .claude/skills
cp -r /tmp/dotskills/skills/crabpot-perf-metrics .claude/skills/crabpot-perf-metrics

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Crabpot Perf Metrics in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Crabpot Perf Metrics on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Crabpot Perf Metrics is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Purpose

Turn Crabpot/OpenClaw perf artifacts into a clear read: what changed, what is real signal, what is stale/noisy, and what action is worth taking.

When to use

Comparing openclaw/crabpot branches such as main, crab-beta, or crab-development.
Reading reports/crabpot-dashboard-data.json, reports/crabpot-import-loop-profile.json, reports/crabpot-runtime-profile.json, README dashboard metrics, or GitHub Actions report artifacts.
The user asks about perf, runtime, CPU, RSS, import loop, OpenClaw import/activate, dashboard deltas, or whether a Crabpot performance change is meaningful.

Inputs

Target repo or local checkout, usually openclaw/crabpot.
Branches, refs, PRs, or artifact URLs to compare.
Optional target OpenClaw labels/SHAs if the comparison crosses openclaw@latest, beta, or openclaw/openclaw@main.

Workflow

Establish freshness before interpreting numbers.
- Resolve branch heads and report timestamps.
- Compare dashboard JSON with raw profile JSON on the same branch.
- If one branch is stale, say so first and avoid strong conclusions.
- Prefer raw JSON over README snippets when they disagree.
Separate the metric families.
- import-loop-profile: cheap cold capture loop against a tiny fixture. Good for harness/import drift, weak for production runtime claims.
- runtime-profile: macro command timings across Crabpot analysis commands. Better trend signal for real suite cost.
- OpenClaw lifecycle: import plus activate phases. If openClawLifecycleCount is 0, the branch dashboard does not include this signal.
Interpret import-loop metrics conservatively.
- Use p50WallMs, p95WallMs, p50PluginWallDeltaMs, p95PluginWallDeltaMs, maxPluginPeakRssDeltaMb, and maxPluginCpuDeltaMsEstimate.
- Treat 3-run p95 as "worst of three", not a stable tail latency estimate.
- A small wall-time bump with flat plugin RSS/CPU usually means jitter or module-resolution overhead, not a memory/CPU regression.
- Baseline-adjusted plugin deltas are more useful than raw wall/RSS/CPU values.
Interpret runtime profile metrics as the stronger trend.
- Use summary.p50WallMs, summary.p95WallMs, summary.maxPeakRssMb, summary.maxCpuMsEstimate, and summary.maxHarnessHeapDeltaMb.
- Inspect per-command medians for the source of change: fixture-inspection, compat-report-registry, contract-capture, synthetic-probe-plan, cold-import-readiness, workspace-plan, platform-probes, and import-loop-profile.
- Broadly uniform slowdowns across target-aware commands usually point at target OpenClaw surface parsing or registry work, not one plugin fixture.
- If RSS is flat and CPU moves only slightly, call it a wall-clock/runtime overhead change, not a memory leak.
Account for surface changes.
- Different fixture counts, entrypoint counts, OpenClaw labels, or commit SHAs make the comparison partly apples-to-oranges.
- New fixtures can raise issue/probe counts while leaving perf healthy.
- OpenClaw main vs npm latest can improve compatibility while adding modest analysis overhead.
Give the user a direct read.
- Start with the conclusion.
- Prefer bullets and a short summary over tables unless the user asks for a table.
- Include exact deltas and classify them: real regression, modest slowdown, noise, stale data, or missing signal.
- Recommend one next action: refresh dashboard, add a dedicated lifecycle lane, investigate a specific command, or ignore as noise.

Useful Commands

Use gh if ghx is unavailable.

Fetch current branch artifacts from GitHub:

bash
gh api /repos/openclaw/crabpot/contents/reports/crabpot-dashboard-data.json?ref=crab-development

Prefer a small Node or jq extraction over manual README scraping. Pull these files when available:

reports/crabpot-dashboard-data.json
reports/crabpot-import-loop-profile.json
reports/crabpot-runtime-profile.json
reports/crabpot-ci-summary.json

Outputs

Concise perf summary in bullets.
Freshness caveats when needed.
Important deltas with interpretation.
Clear owner/action recommendation, especially when lifecycle import+activate is missing from dashboard artifacts.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

agents/openai.yaml1 KB
assets/icon.jpg37 KB

Frequently asked questions

What does the Crabpot Perf Metrics AI skill do?

Interpret Crabpot and OpenClaw performance dashboard metrics, import-loop profiles, runtime profiles, and branch-to-branch perf deltas without over-reading noisy samples.

Why use Crabpot Perf Metrics on TypingMind?

Because you install it once and use it with any model. Crabpot Perf Metrics is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Crabpot Perf Metrics in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vincentkoc/dotskills/tree/main/skills/crabpot-perf-metrics. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Crabpot Perf Metrics?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Crabpot Perf Metrics?

As many as you like. As long as a model supports skills, you can use Crabpot Perf Metrics with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Crabpot Perf Metrics AI skill free?

Yes. It is published on GitHub by vincentkoc under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Purpose

When to use

Inputs

Workflow

Useful Commands

Outputs

Bundled files

Frequently asked questions

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