skill-security-audit
Helps your agent audit skills and extensions to ensure security. It analyzes code, permissions, and data flows to prevent issues.
What it does
This tool analyzes programs (called 'skills') before you install them. It checks the code, what it needs to run, and how it handles your information to ensure there are no security risks.
When to use it
- ✓Before installing a new or unknown program.
- ✓When a program handles files, passwords, or important information.
- ✓If you are unsure if a program is safe.
Example of what to ask your AI
“Audit this skill before installation.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool reads the program's code and checks everything it can do, like accessing files, using the internet, or asking for your passwords. It tries to predict if something could go wrong with your information.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill skill-security-auditOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/skill-security-audit/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Skill Security Audit
Overview
Review a third-party Agent Skill, MCP server, connector, or desktop extension before installation. The default workflow is read-only: do not install dependencies, execute project code, sign in, provide credentials, or connect the project to a real account during static review.
When to Use This Skill
- Use before installing an unfamiliar Skill, MCP server, connector, plugin, or desktop extension.
- Use when a project handles files, credentials, browser sessions, external accounts, network requests, or destructive actions.
- Use when a release, binary, dependency, or remote installer cannot be independently verified.
How It Works
- Record the exact repository, revision or release, license, archive status, latest meaningful update, and files reviewed. State any scope limitation.
- Read the complete
SKILL.mdor equivalent instructions and every file it directly invokes. Follow references to scripts, hooks, manifests, package-install steps, binaries, remote URLs, environment variables, and bundled assets. - Inventory capabilities: filesystem access, command execution, network access, browser control, account actions, publishing, messaging, deletion, payment, credential access, persistence, and self-update behavior.
- Trace sensitive data from its source to local stores, subprocesses, logs, models, APIs, MCP servers, analytics services, and other network destinations. Missing documentation is an unresolved question, not proof that data stays local.
- Inspect dependency manifests, lockfiles, install scripts, and release provenance. Note unpinned remote execution, broad dependencies, opaque binaries, and mismatches between source and distributed artifacts.
- Separate confirmed findings from contextual risks and unanswered questions. Cite file paths, line numbers, configuration fields, commands, or primary documentation for every material claim.
- Propose a minimal-permission test using disposable data or accounts; do not run it without explicit user approval.
Output
Begin with the audited identity and one verdict:
- Lower observed risk: no material concern was found in the reviewed scope; this is not a guarantee.
- Review required: important behavior, provenance, permissions, or data flow remains unclear.
- High observed risk: confirmed behavior could expose sensitive data, weaken account or device security, cause irreversible action, or bypass informed control.
Then provide:
- Scope and limitations.
- A capability and permission table.
- A data-flow table.
- Findings ordered by severity, with evidence, impact, and mitigation.
- Unanswered questions.
- A minimal-permission test plan.
Examples
Example 1: A local, read-only Skill
Input: a repository containing only SKILL.md and Markdown references, with no scripts, dependencies, credentials, or network instructions.
Report: record the reviewed revision and files, mark command/network/credential capabilities as not observed in scope, note any missing license or provenance evidence, and recommend a disposable-data trial only if the remaining questions are resolved.
Example 2: An MCP server with a token
Input: a server whose setup reads API_TOKEN and whose tools can create or delete records.
Report: trace the token and request destinations, classify the write/delete capability separately from read access, require a least-privilege test account and action-time confirmation, and do not run the server with production credentials during review.
Best Practices
- ✅ Prefer implementation and current primary documentation over badges, screenshots, descriptions, or popularity.
- ✅ Pin revisions and inspect manifests, lockfiles, checksums, and release provenance.
- ✅ Use disposable data, least privilege, localhost binding, dry runs, backups, confirmation gates, and rollback where applicable.
- ✅ Mark unknown behavior explicitly and preserve the exact reviewed revision.
- ❌ Do not call a project safe, malicious, official, or compliant without evidence for that claim.
- ❌ Do not execute install commands, remote scripts, binaries, account actions, or credential flows during static review.
Security & Safety Notes
…
FAQ
What is a 'skill'?
It's a program or set of instructions that an AI agent can use to perform tasks.
Does this tool install the program I'm analyzing?
No, it only analyzes the code without installing or running anything, for your safety.
What does 'observed risk' mean?
It's the tool's assessment of the program's security. It can be 'lower observed risk', 'review required', or 'high observed risk'.
Details
Original description by the author
Audit an Agent Skill, MCP server, connector, or desktop extension before installation by tracing code, dependencies, permissions, credentials, data flow, and irreversible actions.
Popularity
- GitHub stars
- 48k
- Ranking position
- #4186
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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