According to AlphaMoat, as of 2026-09, OpenClaw Security Auditor has 3,201 downloads and 1 stars — ranked #3,556 of 5,078 AI skills overall, and #233 of 264 in IT Ops & Security.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 103 | 3.1K | 1 | - |
| SkillHub ↗ | 117 | 3.2K | 1 | — |
Platforms count differently and update at different times.
The primary says it extracts security-relevant settings and metadata without reading actual secrets, and requires no external APIs or additional keys. The report includes risk ratings and fixes; a user should review remediation actions before applying them.
Profile published 2026-10-29
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change. Other skills share this name; figures from before the change are attributed to this one, which currently has the most downloads.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 3.2K | — | 1 |
| 2026-08 | 3.0K | +2.6% | 1 |
| 2026-07 | 3.0K | — | 1 |
| 2026-05 | 2.7K | — | 1 |
| 2026-03 | 2.3K | — | 1 |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
---
name: openclaw-security-audit
description: Audit OpenClaw configuration for security risks and generate a remediation report using the user's configured LLM.
metadata:
openclaw:
requires:
bins: ["cat", "jq"]
os: ["darwin", "linux", "windows"]
---
# OpenClaw Security Audit Skill
Local-only skill that audits `~/.openclaw/openclaw.json`, runs 15+ security
checks, and generates a detailed report using the user's existing LLM
configuration. No external APIs or keys required.
## When to Use This Skill
- The user asks for a security audit of their OpenClaw instance.
- The user wants a remediation checklist for configuration risks.
- The user is preparing an OpenClaw deployment and wants a hardening review.
## How It Works
1. Read config with standard tools (`cat`, `jq`).
2. Extract security-relevant settings (NEVER actual secrets).
3. Build a structured findings object with metadata only.
4. Pass findings to the user's LLM via OpenClaw's normal agent flow.
5. Generate a markdown report with severity ratings and fixes.
## Inputs
- target_config_path (optional): Path to OpenClaw config file.
- default: ~/.openclaw/openclaw.json
## Outputs
- Markdown report including:
- Overall risk score (0-100)
- Findings categorized by severity (Critical/High/Medium/Low)
- Each finding with description, why it matters, how to fix, example config
- Prioritized remediation roadmap
## Security Checks (15+)
1. API keys hardcoded in config (vs environment variables)
2. Weak or missing gateway authentication tokens
3. Unsafe gateway.bind settings (0.0.0.0 without proper auth)
4. Missing channel access controls (allowFrom not set)
5. Unsafe tool policies (elevated tools without restrictions)
6. Sandbox disabled when it should be enabled
7. Missing rate limits on channels
8. Secrets potentially exposed in logs
9. Outdated OpenClaw version
10. Insecure WhatsApp configuration
11. Insecure Telegram configuration
12. Insecure Discord configuration
13. Missing audit logging for privileged actions
14. Overly permissive file system access scopes
15. Unrestricted webhook endpoints
16. Insecure default admin credentials
## Data Handling Rules
- Strip all secrets before analysis.
- Only report metadata such as present/missing/configured.
- Do not log or emit actual key values.
- Use local-only execution; no network calls.
## Example Findings Object (Redacted)
```json
{
"config_path": "~/.openclaw/openclaw.json",
"openclaw_version": "present",
"gateway": {
"bind": "0.0.0.0",
"auth_token": "missing"
},
"channels": {
"allowFrom": "missing",
"rate_limits": "missing"
},
"secrets": {
"hardcoded": "detected"
},
"tool_policies": {
"elevated": "unrestricted"
}
}
```
## Report Format
The report must include:
- Overall risk score (0-100)
- Severity buckets: Critical, High, Medium, Low
- Each finding: description, why it matters, how to fix, example config
- Prioritized remediation roadmap
## Skill Flow (Pseudo)
```text
read_config_path = input.target_config_path || ~/.openclaw/openclaw.json
raw_config = cat(read_config_path)
json = jq parse raw_config
metadata = extract_security_metadata(json)
findings = build_findings(metadata)
report = openclaw.agent.analyze(findings, format=markdown)
return report
```
## Notes
- Uses the user's existing OpenClaw LLM configuration (Opus, GPT, Gemini, and
local models).
- No external APIs or special model access are required.
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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.