Report Clawhub Malicious Skill logo

Report Clawhub Malicious Skill

Organization
openclaw
report-clawhub-malicious-skill

Use when a researcher, maintainer, or contributor found or suspects a malicious skill on ClawHub and needs a private reporting workflow: opening a GitHub private vulnerability report, preparing a proposal-only PR, creating `proposals/<GHSA-ID>/clawscan.yml`, running ClawScan against the malicious skill, and explaining what evidence belongs in private versus public channels.

Overview

Publisheropenclaw
Repositoryclawscan
Skill namereport-clawhub-malicious-skill
Stars
150
Forks
28
Bundled files
1
LicenseMIT
Links
  • 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.

  • 1 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 Report Clawhub Malicious Skill 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/clawscan/tree/main/skills/report-clawhub-malicious-skill
  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/clawscan.git /tmp/clawscan
mkdir -p .claude/skills
cp -r /tmp/clawscan/skills/report-clawhub-malicious-skill .claude/skills/report-clawhub-malicious-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Report Clawhub Malicious Skill 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 Report Clawhub Malicious Skill 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 Report Clawhub Malicious Skill 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.

Report ClawHub Malicious Skill

Overview

Use this workflow to help someone report a malicious ClawHub skill and propose a ClawScan profile change that catches it. Keep sensitive details private and make the public PR contain only the candidate ClawScan config.

Safety Boundary

Separate private evidence from public contribution material:

  • Put live malicious skill URLs/slugs, impact, exploit details, reproduction notes, and private ClawScan artifacts in GitHub private vulnerability reporting.
  • Do not paste secrets, exploit payloads, private artifacts, or live bypass details into public issues, public PR text, README edits, or config comments.
  • Do not ask the reporter to execute the suspicious skill. ClawScan should scan a local copy as data; it should not run the skill's behavior.
  • If the reporter does not have a GHSA/private report id yet, have them open the private report first and wait for the identifier before opening the public proposal PR.

Workflow

  1. Open a GitHub private vulnerability report.

    Ask the reporter to include:

    • affected ClawHub skill URLs or slugs
    • why the current clawhub profile missed it
    • why the proposed ClawScan config catches it
    • local ClawScan artifact paths or attached artifacts
    • any private reproduction context maintainers need
  2. Create a proposal-only branch and config.

    The public PR should add only:

    text
    proposals/<GHSA-ID>/clawscan.yml

    The file must define a clawhub profile. Start from the current profile and change only what is needed to catch the reported case:

    yaml
    version: 1
    profiles:
      clawhub:
       scanners:
         - skillspector
         - clawscan-static
        judge:
          command: >
            # candidate judge command, if changed

    Do not edit the official bundled profile files in the proposal PR:

    • internal/profiles/clawhub/clawscan.yml
    • internal/profiles/clawhub/prompt.md
    • internal/profiles/clawhub/output.schema.json
  3. Prove the candidate catches the reported skill locally.

    Use a local copy of the suspicious skill:

    bash
    clawscan /path/to/suspect-skill \
      --config proposals/<GHSA-ID>/clawscan.yml \
      --profile clawhub \
      --output ./artifacts/reported-skill-candidate.json

    If useful, compare against the built-in profile:

    bash
    clawscan /path/to/suspect-skill \
      --profile clawhub \
      --output ./artifacts/reported-skill-current.json

    The public PR should not include the private artifacts unless maintainers say the contents are safe to publish. Reference them in the private report.

  4. Open the public PR.

    Keep the PR text minimal:

    • state that a private vulnerability report exists
    • point to proposals/<GHSA-ID>/clawscan.yml
    • do not include sensitive skill details
    • say maintainers should run the official SkillTrustBench Profile Gate
  5. Explain maintainer validation.

    Maintainers run the official gate against the proposal:

    bash
    clawscan benchmark SkillTrustBench \
      --ids https://huggingface.co/datasets/cuhk-zhuque/SkillTrustBench-results/resolve/main/data/evaluation_subset_10pct.jsonl \
      --config proposals/<GHSA-ID>/clawscan.yml \
      --profile clawhub \
      --output ./artifacts/skilltrustbench-candidate.json

    The proposal's clawhub profile shadows the built-in profile for that run. Maintainers review the private report, upload/preserve the benchmark artifact, add a dated baseline under benchmarks/skilltrustbench-leaderboard-10pct/, and port accepted behavior into the bundled clawhub profile. The latest YYYY-MM-DD.json baseline is the current accepted baseline.

Output Shape

When walking someone through the process, end with:

  • private report checklist
  • exact proposal file path
  • candidate config draft or edits needed
  • local ClawScan commands to run
  • public PR checklist
  • clear note that maintainers own the official SkillTrustBench gate and final built-in profile port

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 Report Clawhub Malicious Skill AI skill do?

Use when a researcher, maintainer, or contributor found or suspects a malicious skill on ClawHub and needs a private reporting workflow: opening a GitHub private vulnerability report, preparing a proposal-only PR, creating `proposals/<GHSA-ID>/clawscan.yml`, running ClawScan against the malicious skill, and explaining what evidence belongs in private versus public channels.

Why use Report Clawhub Malicious Skill on TypingMind?

Because you install it once and use it with any model. Report Clawhub Malicious Skill 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 Report Clawhub Malicious Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/clawscan/tree/main/skills/report-clawhub-malicious-skill. 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 Report Clawhub Malicious Skill?

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 Report Clawhub Malicious Skill?

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

Is the Report Clawhub Malicious Skill AI skill free?

Yes. It is published on GitHub by openclaw 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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