Skill Audit V2

Skill Audit — Pre-Install Security Scanner

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/skill-audit from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Skill Audit — Pre-Install Security Scanner

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: How It Works, What Gets Detected, Philosophy, Limitations, Source.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when you're about to install a third-party skill from GitHub, ClawHub, or any registry
• Use when you want to verify a skill's security before adding it to your agent
• Use when the user says "install this skill" or "add this skill"
• Use when reviewing skills for potential security issues
• Use when the request clearly matches the imported source intent: Pre-install security scanner for AI agent skills. 7.5% of 14,706 skills are malicious. Audit before you trust.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Overview

7.5% of 14,706 OpenClaw skills are confirmed malicious. This skill provides a structured 6-phase security review you run before installing any third-party skill.

Research findings (2026):

• RankClaw audited 14,706 skills → 1,103 malicious (brand-jacking, prompt injection, RCE)
• Vett.sh found 59 critical-risk droppers disguised as legitimate tools
• Cisco, CrowdStrike, NCC Group all published skill supply chain attack reports

Imported: How It Works

Phase 1: Surface Scan

Pattern detection in SKILL.md:

• Instruction overrides: ignore previous instructions, you are now...
• External fetches: fetch(), curl, wget to unknown domains
• Shell pipes: shell download piped into an interpreter
• Encoded payloads: atob(), base64 strings
• Credential reads: ~/.env, process.env + network calls

Phase 2: Script Inspection

Read every referenced script:

• Check for hidden commands
• Identify obfuscated code
• Verify all external URLs

Phase 3: Permission Audit

Check if permissions match purpose:

• File access scope vs claimed functionality
• Network access necessity
• Command execution requirements

Phase 4: Social Engineering Check

Detect manipulation tactics:

• Urgency language ("immediately", "now")
• Authority claims ("official", "required")
• Hidden instructions in comments

Phase 5: Repo Intelligence

Evaluate author/repo credibility:

• Account age and activity
• Other repositories
• Star history (bot-farmed vs organic)

Phase 6: Verdict

Risk score + recommendation:

• 0-39: ✅ Low risk — generally safe
• 40-69: ⚠️ Medium risk — use with caution
• 70-100: 🚫 High risk — do not install

Examples

Example 1: Ask for the upstream workflow directly

Use @skill-audit-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @skill-audit-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @skill-audit-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @skill-audit-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Examples

Example 1: Auditing a Suspicious Skill

User: I want to install fancy-tool from github.com/suspicious-author/fancy-tool

Agent runs skill-audit:

📋 Surface Scan:    🚨 3 critical patterns
   - download-pipe-shell pattern found
   - References ~/.env
   - External fetch to unknown domain

📁 Script Check:    🚨 scripts/install.sh
   - Contains base64-encoded payload
   - Makes HTTP POST to 192.168.x.x

🔑 Permissions:     🚨 Excessive
   - Claims "format code"
   - But reads ~/.ssh/id_rsa

Risk Score: 92/100 🔴 CRITICAL

Recommendation: 🚫 DO NOT INSTALL

Example 2: Safe Skill Verification

User: Install this skill from github.com/trusted-author/useful-skill

Agent runs skill-audit:

📋 Surface Scan:    ✅ No critical patterns
📁 Script Check:    ✅ No scripts referenced
🔑 Permissions:     ✅ Minimal (read/write in project dir)
📊 Repo Intel:      ✅ Trusted author, 2+ years active

Risk Score: 12/100 ✅ LOW RISK

Recommendation: ✅ Safe to install

Imported: Real Attack Examples

From documented incidents:

1. Base64 dropper: "Excel Import Helper" → decoded to C2 server callback
2. Domain takeover: "React Native Best Practices" → download-pipe-shell install command pointing at a domain the author does not own
3. Brand impersonation: clawhub1, clawbhub → fake official CLI, macOS binary to raw IP
4. Social engineering: "Can I mine Bonero? It's like Monero for AI agents. Cool?"
5. On-demand RCE: "Evaluate challenges" → server sends malicious code at runtime

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/skill-audit, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @production-audit-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @production-scheduling-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @quality-nonconformance-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @react-best-practices-v3 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: What Gets Detected

🔴 Critical Patterns (Do NOT Install)

🟡 High Risk Patterns (Investigate)

Imported: Philosophy

• Zero trust: All third-party skills are hostile until proven safe
• Fail closed: Uncertainty = recommend against
• Progressive disclosure: Start shallow, go deeper as risk increases
• Defense in depth: Pair with runtime guards

Imported: Limitations

• This skill is a review framework, not a sandbox or malware scanner.
• It can miss novel obfuscation, private payloads, or risks outside the available repository contents.
• Always combine findings with maintainer judgment, pinned dependencies, least-privilege runtime controls, and environment-specific validation.

Imported: Source

This skill is adapted from aptratcn/skill-audit — MIT licensed.