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Clawscan Cli

Organization
openclaw
clawscan-cli

Use when running or explaining the ClawScan CLI, including one-off agent-skill scans, benchmark runs, scanner fixtures, judge harness commands, env var validation, and interpreting clawscan-run-v1 and clawscan-benchmark-v1 artifacts.

Overview

Publisheropenclaw
Repositoryclawscan
Skill nameclawscan-cli
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 Clawscan Cli 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/clawscan-cli
  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/clawscan-cli .claude/skills/clawscan-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Clawscan Cli 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 Clawscan Cli 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 Clawscan Cli 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.

ClawScan CLI

Overview

Use ClawScan to run one or more security scanners against agent skills, preserve raw scanner evidence, and optionally pass the evidence to an external judge harness. The public CLI is general-purpose; ClawHub-specific parity helpers belong outside cmd/clawscan. Keep secrets in environment variables, never CLI flags.

Command Surface

Run from the repo root during development:

bash
go run ./cmd/clawscan <target> --scanner <scanner-id> [flags]

Use the installed binary when available:

bash
clawscan <target> --scanner <scanner-id> [flags]

Choose the run mode:

NeedShape
Scan repo skills with one scannerclawscan --scanner skillspector from a repo with ./skills/<name>/SKILL.md
Scan repo skills with the ClawHub profileclawscan --profile clawhub from a repo with ./skills/<name>/SKILL.md
Scan one explicit target with a profileclawscan ./my-skill --profile clawhub
Capture raw scanner evidence onlyclawscan ./my-skill --scanner clawscan-static
Print raw JSON to stdoutAdd --json.
Run all profiles from a configclawscan ./my-skill --config ./security/clawscan.yml
Run one config profileclawscan ./my-skill --config ./security/clawscan.yml --profile review
Install scanner dependenciesclawscan install aig cisco skillspector
List scanner catalogclawscan scanners
Inspect one scannerclawscan scanners skillspector
List resolved profilesclawscan profiles
Print resolved profile YAMLclawscan profiles -v
List benchmark catalogclawscan benchmark list
Run SkillTrustBenchclawscan benchmark SkillTrustBench --limit 10 --scanner clawscan-static --output run.json
Run ClawHub Security Signalsclawscan benchmark clawhub-security-signals --split eval_holdout --limit 10 --scanner clawscan-static --output run.json
Use stable scanner evidenceAdd --scanner-result <id=path> for each fixture-backed scanner.
Add or override a judge harnessAdd --judge '<command with placeholders>'.
Use host-installed scanner/judge CLIsAdd --sandbox off only in an already-isolated environment.

Helpful metadata:

bash
clawscan --help
clawscan -h
clawscan --version
clawscan scanners
clawscan profiles
clawscan benchmark list

Unless --json is passed, ClawScan writes the full artifact to ./clawscan-results/artifact.json by default, preserves per-scanner JSON files in the same visible results bundle, and prints a concise key/value summary ending in full_results: ./clawscan-results/artifact.json. Use --output <path> to choose a different artifact path; explicit .json paths keep that artifact file and write scanner JSON beside it.

Targets, Profiles, And Config

If no target is passed with --scanner, --profile, or --config, ClawScan scans child skill directories under ./skills. If ./skills is missing or contains no children with SKILL.md, it fails with a target-discovery error. Plain clawscan without --scanner, --profile, or --config is invalid. Benchmark runs use clawscan benchmark <benchmark-id> and do not accept scan targets.

Built-in profiles:

ProfileScannersJudge
clawhubskillspector, clawscan-staticbundled Codex judge with ClawHub prompt/schema

Profiles are loaded from embedded built-ins. Project .clawscan.yml / .clawscan.yaml files are NOT loaded automatically: pass --config <path> to load a specific config, or --discover-config to load the nearest one found upward from the current directory. A project profile with the same name shadows the built-in whole profile. --config <path> without --profile runs every profile in that config and emits a clawscan-batch-v1 artifact. Discovery is off by default because project configs can define scanner commands that execute with the caller's environment and credentials.

Use clawscan profiles to inspect the built-in profile catalog. Use clawscan profiles -v to print it as pasteable YAML.

CLI flags override the selected profile for one run. Passing --scanner without --profile creates an ad hoc scanner-only run, so profile judges are not invoked accidentally.

Minimal config:

yaml
version: 1
sandbox:
  mode: docker
  env:
    - OPENAI_API_KEY
    - CODEX_API_KEY
profiles:
  review:
    scanners:
      - clawscan-static
    json: true
    judge:
      command: judge --out {{ output }}

sandbox.env is an allowlist of env var names to pass into the Docker runtime; store names there, not secret values. CLI equivalents are --sandbox, --sandbox-image, and repeatable --sandbox-env.

Scanners

Use clawscan scanners for the registry-backed scanner catalog and clawscan scanners <scanner-id> for one scanner's repository, description, env vars, and install guidance.

Accepted scanner IDs:

text
agentverus, aig, cisco, clawscan-static, skillspector, snyk, socket, virustotal

Credential rules:

ScannerEnv vars
aigrequired: LLM_API_KEY or OPENAI_API_KEY; optional local scanner config: DEFAULT_MODEL, DEFAULT_BASE_URL, DEFAULT_MODEL_CONTEXT_WINDOW, LOG_LEVEL
socketrequired: SOCKET_CLI_API_TOKEN
snykrequired: SNYK_TOKEN
virustotalrequired: VIRUSTOTAL_API_KEY
skillspectoroptional provider config: SKILLSPECTOR_PROVIDER, SKILLSPECTOR_MODEL, NVIDIA_INFERENCE_KEY, OPENAI_API_KEY, OPENAI_BASE_URL, ANTHROPIC_API_KEY, ANTHROPIC_PROXY_ENDPOINT_URL, ANTHROPIC_PROXY_API_KEY
ciscooptional upstream analyzers: SKILL_SCANNER_LLM_*, SKILL_SCANNER_META_LLM_*, VIRUSTOTAL_API_KEY, AI_DEFENSE_API_KEY, AI_DEFENSE_API_URL

Artifact env fields record only present or missing; they must never contain secret values. GenDigital/Gen Agent Trust Hub is not a built-in scanner because there is no local CLI for ClawScan to invoke.

Dependency setup:

bash
clawscan install aig cisco skillspector

clawscan install accepts one or more scanner IDs. It follows upstream scanner install docs where they publish an install command, including A.I.G's pip install aig-skill-scan, Cisco's uv pip install cisco-ai-skill-scanner, SkillSpector's uv tool install git+https://github.com/NVIDIA/skillspector.git, Socket's npm install -g socket, and AgentVerus' npm install --save-dev agentverus-scanner. Snyk is launcher-based, so ClawScan verifies uvx; built-in and simple API-backed scanners are skipped. The aig adapter runs aig-skill-scan --repo <target> --language en -o <result.sarif.json> and stores the SARIF 2.1.0 document as raw scanner evidence.

Starting in ClawScan v0.1.2, aig no longer uses the legacy A.I.G Docker/API service. Replace AIG_MODEL with DEFAULT_MODEL, AIG_MODEL_BASE_URL with DEFAULT_BASE_URL, and AIG_MODEL_API_KEY with LLM_API_KEY or OPENAI_API_KEY; AIG_BASE_URL and AIG_API_KEY are retired. The local scanner accepts directory targets only.

For normal runs, command-backed scanners and judges run in ghcr.io/openclaw/clawscan-runtime:latest. clawscan install is mainly for local development or --sandbox off environments.

Use --scanner-result when a test or fixture should supply stable scanner JSON:

bash
clawscan ./my-skill \
  --scanner skillspector \
  --scanner-result skillspector=./fixtures/skillspector.json \
  --json

The scanner must still be requested with --scanner or via the selected profile.

Benchmarks

Use clawscan benchmark list for the registry-backed benchmark catalog.

Supported benchmarks:

text
cuhk-zhuque/SkillTrustBench
clawhub-security-signals

Run SkillTrustBench with the canonical Hugging Face ID or the short alias SkillTrustBench:

bash
clawscan benchmark SkillTrustBench \
  --limit 10 \
  --scanner clawscan-static \
  --output /tmp/clawscan-benchmark.json

SkillTrustBench uses split benchmark. The first live run downloads and caches benchmark_full_v1.0.zip, then extracts only the requested case directories into temporary scan targets.

Use --ids <path-or-url> with SkillTrustBench to run a fixed subset from a plain text file with one ID per line or JSONL rows with an id field. The source is streamed and may contain at most 5,520 unique IDs, at most 256 bytes per trimmed ID, and at most 256 KiB (262,144 bytes) of retained ID text. These are selection limits, not a total source-size limit; JSONL sources may exceed 256 KiB. Individual lines must be smaller than the parser's 1 MiB buffer limit. --ids preserves source order, records idsSource, idsCount, and idsSha256 in the artifact, and is mutually exclusive with --limit and --offset.

ClawHub Security Signals splits: train, validation, test, eval_holdout. --limit 0 means run the full selected split. Use --offset with --limit for reproducible chunks.

For clawhub-security-signals, --output ./clawscan-benchmark.json also writes ./predictions.jsonl. Use --predictions-output <path> to choose another path. --predictions-output is only supported for clawhub-security-signals.

Benchmark artifacts use clawscan-benchmark-v1. Each case embeds the normal clawscan-run-v1 artifact, expected verdict metadata, and evaluation status. The summary includes case counts, scanner/judge statuses, and accuracy over scored cases. Inspect the JSON artifact for full evidence.

Judge Harness

--judge runs through the platform shell and must produce a JSON object on stdout or at {{ output }}.

Important placeholders:

PlaceholderMeaning
{{ workspace }}Temporary judge workspace with copied target files, scanner JSON, and metadata.
{{ prompt }} / {{ prompt:path }}Render prompt template and interpolate the rendered prompt path.
{{ output_schema }} / {{ output_schema:path }}Copy schema and interpolate the copied schema path.
{{ output }}Path where the judge should write final JSON.

Prompt files can reference requested scanner JSON:

md
```json
{{ scanners.skillspector }}
```

If a prompt references an unrequested scanner, ClawScan should fail clearly. The built-in clawhub profile uses this same judge mechanism with embedded prompt and output-schema files.

Verification

Use static scanner smokes for local proof because they do not need secrets:

bash
go run ./cmd/clawscan ./README.md --scanner clawscan-static --output /tmp/clawscan-smoke.json
go run ./cmd/clawscan benchmark SkillTrustBench --limit 1 --scanner clawscan-static --output /tmp/clawscan-benchmark-smoke.json
go run ./cmd/clawscan --help
go test -count=1 ./...
go vet ./...

Common Mistakes

  • Do not pass API keys as CLI flags.
  • Do not run plain clawscan; choose --scanner, --profile, --config, or clawscan benchmark <benchmark-id>.
  • Do not assume clawscan scans .; no target means discover ./skills.
  • Do not expect a profile judge when passing explicit --scanner flags without --profile.
  • Do not use benchmark flags such as --split, --limit, or --offset outside clawscan benchmark <benchmark-id>.
  • Do not use --config without --profile for benchmark runs; all-profile config runs are target scans only.
  • Do not assume host-installed scanner CLIs are used by default; command-backed scanners and judges use the Docker runtime unless --sandbox off is set.
  • Do not add unsupported dataset names; built-ins are SkillTrustBench and clawhub-security-signals.
  • Do not assume scanner failures are final policy verdicts; scanner output is raw evidence for comparison or judging.

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 Clawscan Cli AI skill do?

Use when running or explaining the ClawScan CLI, including one-off agent-skill scans, benchmark runs, scanner fixtures, judge harness commands, env var validation, and interpreting clawscan-run-v1 and clawscan-benchmark-v1 artifacts.

Why use Clawscan Cli on TypingMind?

Because you install it once and use it with any model. Clawscan Cli 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 Clawscan Cli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/openclaw/clawscan/tree/main/skills/clawscan-cli. 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 Clawscan Cli?

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 Clawscan Cli?

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

Is the Clawscan Cli 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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