Skill Scanner

SentryOfficialPublished by Sentry

Skill
Review and audit
Security
Apache-2.0
Medium risk
Has scripts

From the author

Scan agent skills for security issues. Use when asked to "scan a skill", "audit a skill", "review skill security", "check skill for injection", "validate SKILL.md", or assess whether an agent skill is safe to install. Checks for prompt injection, malicious scripts, excessive permissions, secret exposure, and supply chain risks.

— the description by Sentry

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

skill-scanner is a security auditing tool designed to evaluate the safety of agent skills before they are adopted. It performs a multi-phase analysis, including static code scanning, frontmatter validation, prompt injection detection, and behavioral assessment. The tool helps users identify potential risks such as secret exposure, supply chain vulnerabilities, and unauthorized configuration modifications, providing a structured report with severity levels and remediation advice.

What it does

Performs static analysis on skill scripts to detect deterministic security patterns.
Validates SKILL.md frontmatter for required fields and tool consistency.
Analyzes instructions for potential prompt injection and behavioral risks.
Evaluates tool permissions against a least-privilege framework.
Assesses supply chain risks by reviewing external URLs and dependencies.
Generates a structured security report with risk levels and remediation steps.

When to use it

Before installing or adopting a new agent skill.
When auditing existing skills for potential security vulnerabilities.
When assessing whether a skill's tool permissions are justified.
When investigating suspicious behavior or potential prompt injection in a skill.

Advantages

Provides a systematic, multi-layered approach to security auditing.
Reduces the risk of installing malicious or overly permissive skills.
Helps distinguish between legitimate security documentation and actual threats.
Standardizes the security review process for agent skills.
Offers clear risk-level determination and actionable remediation advice.

Usage examples

“scan the skill located at .agents/skills/my-new-skill”: The agent will perform a full security audit of the specified directory, outputting a report on findings, risk levels, and safety assessment.
“audit the skill named 'data-fetcher'”: The agent will locate the skill, run the static scanner, validate its permissions, and provide a security summary.
“scan all skills in this repository”: The agent will discover all SKILL.md files in the repository and generate a comprehensive security report for each.
“check if the skill in ./skills/web-tool is safe to install”: The agent will perform a behavioral and permission analysis to determine if the skill is safe or requires caution.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/getsentry/skill-scanner/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip skill-scanner.zip -d .agents/skills

Documentation by Sentry

Skill Security Scanner

Scan agent skills for security issues before adoption. Detects prompt injection, malicious code, excessive permissions, secret exposure, and supply chain risks.

Requires: The uv CLI for python package management, install guide at https://docs.astral.sh/uv/getting-started/installation/

Important: Run all scripts from the repository root. Script paths like scripts/scan_skill.py are relative to this skill's root directory (the directory containing this SKILL.md), not relative to the target repository.

Bundled Script

`scripts/scan_skill.py`

Static analysis scanner that detects deterministic patterns. Outputs structured JSON.

uv run scripts/scan_skill.py <skill-directory>

Returns JSON with findings, URLs, structure info, and severity counts. The script catches patterns mechanically — your job is to evaluate intent and filter false positives.

Workflow

Phase 1: Input & Discovery

Determine the scan target:

If the user provides a skill directory path, use it directly
If the user names a skill, look for it under .agents/skills/<name>/ first, then other established layouts such as skills/<name>/ when the repo uses a canonical root skill tree, .claude/skills/<name>/, plugins/*/skills/<name>/, or another repo-managed skill root with clear prior art
If the user says "scan all skills", discover all */SKILL.md files and scan each

Validate the target contains a SKILL.md file. List the skill structure:

ls -la <skill-directory>/
ls <skill-directory>/references/ 2>/dev/null
ls <skill-directory>/scripts/ 2>/dev/null

Phase 2: Automated Static Scan

Run the bundled scanner:

uv run scripts/scan_skill.py <skill-directory>

Parse the JSON output. The script produces findings with severity levels, URL analysis, and structure information. Use these as leads for deeper analysis.

Fallback: If the script fails, proceed with manual analysis using Grep patterns from the reference files.

Phase 3: Frontmatter Validation

Read the SKILL.md and check:

Required fields: name and description must be present
Name consistency: name field should match the directory name
Tool assessment: Review allowed-tools — is Bash justified? Are tools unrestricted (*)?
Model override: Is a specific model forced? Why?
Description quality: Does the description accurately represent what the skill does?

Phase 4: Prompt Injection Analysis

Load references/prompt-injection-patterns.md for context.

Review scanner findings in the "Prompt Injection" category. For each finding:

Read the surrounding context in the file
Determine if the pattern is performing injection (malicious) or discussing/detecting injection (legitimate)
Skills about security, testing, or education commonly reference injection patterns — this is expected

Critical distinction: A security review skill that lists injection patterns in its references is documenting threats, not attacking. Only flag patterns that would execute against the agent running the skill.

Phase 5: Behavioral Analysis

This phase is agent-only — no pattern matching. Read the full SKILL.md instructions and evaluate:

Description vs. instructions alignment:

Does the description match what the instructions actually tell the agent to do?
A skill described as "code formatter" that instructs the agent to read ~/.ssh is misaligned

Config/memory poisoning:

Instructions to modify CLAUDE.md, MEMORY.md, settings.json, .mcp.json, or hook configurations
Instructions to add itself to allowlists or auto-approve permissions
Writing to ~/.claude/, ~/.agents/, or any agent configuration directory
Scripts that append to global config files — the poisoned instructions persist after skill removal

Scope creep:

Instructions that exceed the skill's stated purpose
Unnecessary data gathering (reading files unrelated to the skill's function)
Instructions to install other skills, plugins, or dependencies not mentioned in the description

Information gathering:

Reading environment variables beyond what's needed
Listing directory contents outside the skill's scope
Accessing git history, credentials, or user data unnecessarily

Structural attacks (check scanner output for these):

Symlinks: Files that resolve outside the skill directory — can disguise reads of ~/.ssh/id_rsa, ~/.aws/credentials, etc. as "example" files
Frontmatter hooks: PostToolUse/PreToolUse hooks in YAML — execute shell commands automatically, the model cannot prevent it
!command`` syntax: Runs shell commands at skill load time during template expansion, before the model sees the prompt
Test files: conftest.py, test_*.py, *.test.js — test runners auto-discover and execute these as side effects of pytest or npm test
npm lifecycle hooks: postinstall scripts in bundled package.json — run automatically on npm install
Image metadata: PNG files with text in metadata chunks (tEXt/iTXt) — multimodal LLMs can read hidden instructions from image metadata

Phase 6: Script Analysis

If the skill has a scripts/ directory:

Load references/dangerous-code-patterns.md for context
Read each script file fully (do not skip any)
Check scanner findings in the "Malicious Code" category
For each finding, evaluate:
- Data exfiltration: Does the script send data to external URLs? What data?
- Reverse shells: Socket connections with redirected I/O
- Credential theft: Reading SSH keys, .env files, tokens from environment
- Dangerous execution: eval/exec with dynamic input, shell=True with interpolation
- Config modification: Writing to agent settings, shell configs, git hooks
Check PEP 723 dependencies — are they legitimate, well-known packages?
Verify the script's behavior matches the SKILL.md description of what it does

Legitimate patterns: gh CLI calls, git commands, reading project files, JSON output to stdout are normal for skill scripts.

Phase 7: Supply Chain Assessment

Review URLs from the scanner output and any additional URLs found in scripts:

Trusted domains: GitHub, PyPI, official docs — normal
Untrusted domains: Unknown domains, personal sites, URL shorteners — flag for review
Remote instruction loading: Any URL that fetches content to be executed or interpreted as instructions is high risk
Dependency downloads: Scripts that download and execute binaries or code at runtime
Unverifiable sources: References to packages or tools not on standard registries

Phase 8: Permission Analysis

Load references/permission-analysis.md for the tool risk matrix.

Evaluate:

Least privilege: Are all granted tools actually used in the skill instructions?
Tool justification: Does the skill body reference operations that require each tool?
Risk level: Rate the overall permission profile using the tier system from the reference

Example assessments:

Read Grep Glob — Low risk, read-only analysis skill
Read Grep Glob Bash — Medium risk, needs Bash justification (e.g., running bundled scripts)
Read Grep Glob Bash Write Edit WebFetch Task — High risk, near-full access

Confidence Levels

Level	Criteria	Action
HIGH	Pattern confirmed + malicious intent evident	Report with severity
MEDIUM	Suspicious pattern, intent unclear	Note as "Needs verification"
LOW	Theoretical, best practice only	Do not report

False positive awareness is critical. The biggest risk is flagging legitimate security skills as malicious because they reference attack patterns. Always evaluate intent before reporting.

Output Format

## Skill Security Scan: [Skill Name]

### Summary
- **Findings**: X (Y Critical, Z High, ...)
- **Risk Level**: Critical / High / Medium / Low / Clean
- **Skill Structure**: SKILL.md only / +references / +scripts / full

### Findings

#### [SKILL-SEC-001] [Finding Type] (Severity)
- **Location**: `SKILL.md:42` or `scripts/tool.py:15`
- **Confidence**: High
- **Category**: Prompt Injection / Malicious Code / Excessive Permissions / Secret Exposure / Supply Chain / Validation
- **Issue**: [What was found]
- **Evidence**: [code snippet]
- **Risk**: [What could happen]
- **Remediation**: [How to fix]

### Needs Verification
[Medium-confidence items needing human review]

### Assessment
[Safe to install / Install with caution / Do not install]
[Brief justification for the assessment]

Risk level determination:

Critical: Any high-confidence critical finding (prompt injection, credential theft, data exfiltration)
High: High-confidence high-severity findings or multiple medium findings
Medium: Medium-confidence findings or minor permission concerns
Low: Only best-practice suggestions
Clean: No findings after thorough analysis

Reference Files

File	Purpose
`references/prompt-injection-patterns.md`	Injection patterns, jailbreaks, obfuscation techniques, false positive guide
`references/dangerous-code-patterns.md`	Script security patterns: exfiltration, shells, credential theft, eval/exec
`references/permission-analysis.md`	Tool risk tiers, least privilege methodology, common skill permission profiles

Files

ATTRIBUTION.mdAdded by AutoGineer850 B
LICENSELicence file added by AutoGineer10.5 KiB
SKILL.md9.7 KiB
references/dangerous-code-patterns.md6.3 KiB
references/permission-analysis.md4.2 KiB
references/prompt-injection-patterns.md6.4 KiB
scripts/scan_skill.pyScript28.9 KiB

LICENSE

                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to the Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   Copyright 2025 Functional Software, Inc. dba Sentry

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

Requirements and compatibility

Written for: Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Names Claude Code tools, Needs Python, Needs Node.js

Security

What our scanner noticed· Medium risk

References to credential files
Encoded commands that are executed
Scripts that your agent may run
Zero-width Unicode characters

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by

Sentry

Publisher

Sentry (Official)

Source

getsentry/skills · skills/skill-scanner · commit e94f222

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Sentry.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download skill-scanner.zip