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Openclaw Test Heap Leaks

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openclaw
openclaw-test-heap-leaks

Investigate OpenClaw pnpm test memory growth, Vitest OOMs, RSS spikes, and heap snapshot deltas.

Overview

Publisheropenclaw
Repositoryopenclaw
Skill nameopenclaw-test-heap-leaks
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Bundled files
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  • 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 openclaw on GitHub. Read the source before you install it.

Installation

Install the Openclaw Test Heap Leaks AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/openclaw/openclaw/tree/main/.agents/skills/openclaw-test-heap-leaks
  4. 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.
2

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/openclaw/openclaw.git /tmp/openclaw
mkdir -p .claude/skills
cp -r /tmp/openclaw/.agents/skills/openclaw-test-heap-leaks .claude/skills/openclaw-test-heap-leaks
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openclaw Test Heap Leaks 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 Openclaw Test Heap Leaks 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 Openclaw Test Heap Leaks 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.

OpenClaw Test Heap Leaks

Use this skill for test-memory investigations. Do not guess from RSS alone when heap snapshots are available. Treat snapshot-name deltas as triage evidence, not proof, until retainers or dominators support the call.

Read ../openclaw-test-performance/SKILL.md first for the current test-performance commands and proof routing.

For runtime fixes (e.g., closure leaks in long-running services like the gateway), see Validating runtime fixes below — that uses a dedicated harness rather than the unit-test profiling workflow.

Workflow

  1. Reproduce the failing shape first.

    • Match the real entrypoint and worker budget. For a broad unit-fast baseline with per-config max RSS and top-file timing, start with:

      bash
      pnpm test:perf:groups \
        --config test/vitest/vitest.unit-fast.config.ts \
        --allow-failures \
        --output .artifacts/test-perf/unit-fast-memory.json
    • For a suspected file, rerun that file with one worker and collect wall/RSS evidence: /usr/bin/time -l pnpm test <file> --maxWorkers=1 --reporter=verbose.

    • Current pnpm test execution is planned by scripts/test-projects.mts. Record the printed Vitest config or shard and preserve that shape when the report is configuration- or worker-budget-specific.

  2. Collect the strongest available heap evidence.

    • Run pnpm test:perf:profile:runner -- --output-dir .artifacts/test-perf/vitest-runner-profile -- <file> for a CPU profile plus a sampling heap profile of the unit runner. Open the heap profile in DevTools and inspect the largest allocation families.
    • Sampling .heapprofile output is not a .heapsnapshot; do not pass it to the snapshot delta helper.
    • When a dedicated harness or another known producer emits repeated .heapsnapshot files, compare snapshots from the same PID with .agents/skills/openclaw-test-heap-leaks/scripts/heapsnapshot-delta.mjs.
    • If no snapshot or heap profile is available, RSS growth is useful triage evidence, but keep the leak classification inconclusive.
  3. Classify the growth before choosing a fix.

    • If growth is dominated by Vite/Vitest transformed source strings, Module, system / Context, bytecode, descriptor arrays, or property maps, treat it as likely retained module graph growth in long-lived workers.
    • If growth is dominated by app objects, caches, buffers, server handles, timers, mock state, sqlite state, or similar runtime objects, treat it as a likely cleanup or lifecycle leak.
    • If the names are ambiguous, stop short of a confident label and inspect retainers/dominators in DevTools for the top deltas.
  4. Fix the right layer.

    • For likely retained transformed-module growth in shared workers:
    • Inspect the owning Vitest config and the process chunking in scripts/test-projects.test-support.mjs. Fix process lifetime or project ownership there only when the same-shape evidence shows that shared-worker retention is the cause.
    • test/vitest/vitest.unit-fast-isolated.config.ts is for audited stateful tests that need a fresh module graph. Do not use it as a generic memory-hotspot list.
    • For real leaks:
    • Patch the implicated test or runtime cleanup path.
    • Look for missing afterEach/afterAll, module-reset gaps, retained global state, unreleased DB handles, or listeners/timers that survive the file.
  5. Verify with the most direct proof.

    • Re-run the same grouped or scoped command and confirm the max-RSS trend or OOM is reduced.
    • When a dedicated producer emits heap profiles or snapshots, repeat the same producer and compare equivalent artifacts.
    • For routing or config changes, verify the expected Vitest config or shard starts and the affected tests complete.

Heuristics

  • Do not call everything a leak. Growth in a non-isolated shared Vitest project can be a worker-lifetime problem rather than an application object leak.
  • scripts/test-projects.mts, scripts/test-group-report.mts, and scripts/run-vitest-profile.mts are the current execution, grouped-RSS, and profile entrypoints.
  • The [test] starting ... lines identify the Vitest config or shard to reproduce.
  • .artifacts/vitest-shard-timings.json stores config/shard durations for scheduling. It is not a file-level memory-hotspot or behavior manifest.
  • When the same retained object families grow across multiple intervals in the same worker PID, trust the snapshots over intuition, then confirm ambiguous calls with retainer evidence.

Snapshot Comparison

  • Direct comparison:
    • node .agents/skills/openclaw-test-heap-leaks/scripts/heapsnapshot-delta.mjs before.heapsnapshot after.heapsnapshot
  • Auto-select earliest/latest snapshots per PID within one lane:
    • node .agents/skills/openclaw-test-heap-leaks/scripts/heapsnapshot-delta.mjs --lane-dir <snapshot-directory>
  • Useful flags:
    • --top 40
    • --min-kb 32
    • --pid 16133

Read the top positive deltas first. Large positive growth in module-transform artifacts points to shared-process lifetime or project ownership; large positive growth in runtime objects suggests a real leak. If the names alone do not settle it, open the same snapshot pair in DevTools and inspect retainers/dominators for the top rows before declaring root cause.

Validating runtime fixes (not test-memory)

The workflow above is for diagnosing Vitest worker memory growth. For validating that a runtime/closure fix actually releases captured state, use the dedicated harness:

  • pnpm leak:embedded-run — runs scripts/embedded-run-abort-leak.ts. Loops N aborted runs in a function-shaped scope mimicking runEmbeddedAttempt, writes heap snapshots, and reports a PASS/FAIL verdict on retention growth using FinalizationRegistry for tracked-instance counting plus RSS delta.

Modes:

  • closure-extracted (default) — production fix shape (helper at module scope).
  • closure-inline — pre-fix shape (closure inside the runner scope). Use as a sensitivity check: if it passes you've broken the harness, not fixed a bug.
  • synthetic-leak — deliberately retains via a module-level bucket. Use to confirm the harness can detect leaks before trusting a PASS on a real fix.

Snapshots land in .tmp/embedded-run-abort-leak/. Diff with the same script as above:

node .agents/skills/openclaw-test-heap-leaks/scripts/heapsnapshot-delta.mjs \
  .tmp/embedded-run-abort-leak/baseline-*.heapsnapshot \
  .tmp/embedded-run-abort-leak/batch-N-*.heapsnapshot --top 30

When fixing a different runtime leak, add a new harness alongside this one rather than retrofitting it. The fixture function should mimic the lexical scope of the function where the leak lives, not be a generic abort-loop.

Output Expectations

When using this skill, report:

  • The exact reproduce command.
  • Which Vitest config or target was reproduced, and which PID was compared when snapshots were available.
  • The dominant retained object families from the heap profile or snapshot delta, when available.
  • Whether the issue is a likely real leak or likely shared-worker retained module growth, plus whether retainers/dominators confirmed it.
  • The concrete fix or impact-reduction patch.
  • What you verified, and what snapshot overhead prevented you from verifying.

Bundled files

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

Frequently asked questions

What does the Openclaw Test Heap Leaks AI skill do?

Investigate OpenClaw pnpm test memory growth, Vitest OOMs, RSS spikes, and heap snapshot deltas.

Why use Openclaw Test Heap Leaks on TypingMind?

Because you install it once and use it with any model. Openclaw Test Heap Leaks 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 Openclaw Test Heap Leaks in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/openclaw/tree/main/.agents/skills/openclaw-test-heap-leaks. 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 Openclaw Test Heap Leaks?

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 Openclaw Test Heap Leaks?

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

Is the Openclaw Test Heap Leaks AI skill free?

It is published on GitHub by openclaw. Check the repository for licensing terms. 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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