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  3. Prism Scanner
P
Health: Not checked yetWe have not completed a health check for this listing yet.Last checked 8/11/2026, 12:24:00 AM

Prism Scanner

Enrichment pendingWe haven’t run our AI enrichment pass on this listing yet, so the overview, use cases, and FAQ below may be sparse or missing. We work through the catalog over time β€” check back soon.
View Repository

Security scanner for AI Agent skills, plugins, and MCP servers with A-F grading.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β€” or use 1-click editor setup below.

Add to CursorAdd to VS Code
Manual Client & Custom JSON ConfigExpand JSON β–Ύ

Install Config Generator

Choose your client
claude_desktop_config.json
{
  "mcpServers": {
    "prism-scanner": {
      "command": "npx",
      "args": [
        "-y",
        "prism-scanner"
      ]
    }
  }
}

πŸ’‘ Paste into ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows)

Install Directory Badge Claim listing AlternativesπŸ”’ More in Security

Documentation Overview

:large_blue_diamond: Prism Scanner

Python 3.10+ License: Apache 2.0 Tests: 40 passed Version: 0.2.2 Awesome

Security scanner for AI Agent skills, plugins, and MCP servers.

Prism analyzes code for malicious behavior before you install it β€” and checks your system for leftover threats after you uninstall.

Unlike marketplace-only trust scores, Prism gives you full lifecycle coverage with code-level transparency β€” pre-install, runtime, and post-uninstall β€” across every platform, completely open source.


:sparkles: What's new in v0.2.x

  • P10 β€” Agent psychological manipulation detection: detects gaslighting, guilt-tripping, authority impersonation, urgency pressure, and emotional coercion patterns embedded in skill descriptions, prompts, and code strings. Based on Northeastern/Harvard/MIT research on conversational manipulation of AI agents β€” to our knowledge, the first open-source automated detector for this attack class.
  • M7 β€” Publish hygiene checks: catches source maps, .env files, private keys, IDE configs, and package-manager credentials before they ship. Inspired by Anthropic's April 2026 source-map leak that exposed 512K lines of Claude Code internals via npm. Wire it into your CI:
    bash
    prism scan ./dist --fail-on high
    
  • 47–93% fewer false positives on real-world skills (S1, S4, S8, S12 engine fixes; validated against 3 representative ClawHub skills that previously flooded with false flags).
  • 41 detection rules total across S1–S14 (behavior), M1–M7 (metadata + publish hygiene), P1–P10 (patterns + manipulation), and R1–R10 (residue).
  • v0.2.2 fixes a cosmetic version-string mismatch (prism --version reported 0.1.3 after upgrading to 0.2.1); detection behavior is identical to v0.2.1.

Why Prism?

Marketplace Trust ScoresPrism Scanner
Pre-install:white_check_mark: Reputation score:white_check_mark: Deep code analysis
Post-uninstall:x::white_check_mark: Residue & persistence scan
InspectionBlack-box ratingCode-level, rule-by-rule
PlatformsSingle ecosystemClawHub, MCP, npm, pip
SourceClosedOpen (Apache 2.0)
ExecutionRequires uploadLocal-first, offline OK

Quick Start

Terminal
pip install prism-scanner

# Scan a local skill directory
prism scan ./my-skill/

# Scan a GitHub repo directly
prism scan https://github.com/user/skill-repo

# Check your system for agent residue
prism clean --scan

# Generate a cleanup plan (non-destructive)
prism clean --plan

# Execute cleanup with automatic backups
prism clean --apply

Homebrew (macOS)

bash
brew tap prismlab/tools
brew install prism-scanner

npx (no install needed)

Terminal
npx prism-scanner scan https://github.com/user/skill-repo

GitHub Action (CI/CD)

Add Prism to your CI pipeline β€” findings appear in GitHub's Security tab:

yaml
# .github/workflows/prism-scan.yml
name: Prism Security Scan
on: [push, pull_request]

permissions:
  security-events: write
  contents: read

jobs:
  scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: aidongise-cell/prism-scanner@main
        with:
          path: '.'
          fail-on: 'high'

MCP Server (Claude Desktop / Cursor / VS Code)

Prism Scanner can run as an MCP server, giving AI assistants direct access to security scanning tools.

Terminal
pip install "prism-scanner[mcp]"

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):

config.json
{
  "mcpServers": {
    "prism-scanner": {
      "command": "prism-mcp"
    }
  }
}

Or for Claude Code:

Terminal
claude mcp add prism-scanner prism-mcp

This exposes 4 tools: prism_scan, prism_grade, prism_clean_scan, prism_clean_plan.

Docker

bash
# Build the image
docker build -t prism-scanner .

# Scan a local directory
docker run -v $(pwd)/my-skill:/workspace/target:ro prism-scanner scan /workspace/target

# Scan a remote repo
docker run prism-scanner scan https://github.com/user/skill-repo

# Generate HTML report
docker run -v $(pwd):/workspace/output prism-scanner scan https://github.com/user/repo --format html -o /workspace/output/report.html

Or use the published image:

Terminal
docker run ghcr.io/prismlab/prism-scanner scan <target>

What It Detects

Prism operates across 3 analysis layers, each targeting a different phase of the agent lifecycle:

LayerFocusRulesExamples
1. Code Behavior (S1-S14)What the code does14Shell execution, data exfiltration, SSRF, persistence mechanisms, unsafe deserialization, download-and-execute
2. Metadata (M1-M6, P1-P9)What the package claims vs. contains15Hardcoded credentials, typo-squatting, install scripts, obfuscated payloads, prompt injection, suspicious domains
3. System Residue (R1-R10)What was left behind10LaunchAgents, crontab entries, shell config pollution, orphaned credentials, systemd units, login items

Total: 39 detection rules with lightweight intra-file taint analysis.


Example Output

Code
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Prism Scanner v0.1.0
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

  Target: evil_skill
  Platform: clawhub
  Duration: 42ms

[1/3] Behavior Analysis
  βœ— CRITICAL  S8    Data exfiltration: secrets sent to external endpoint  evil_skill.py:10
  βœ— CRITICAL  S10   Download and execute: fetches remote payload          evil_skill.py:24
  βœ— HIGH      S1    Shell execution with untrusted input                  evil_skill.py:14
  βœ— HIGH      S13   Persistence: writes LaunchAgent plist                 evil_skill.py:36
  ⚠ MEDIUM    S6    Dynamic code execution (eval/exec)                    evil_skill.py:21

[2/3] Metadata Analysis
  βœ— CRITICAL  P1    Hardcoded credential: AWS Access Key                  evil_skill.py:47
  ⚠ MEDIUM    P2    Base64-encoded executable content                     evil_skill.py:50
  ⚠ MEDIUM    P6    Prompt injection pattern in string literal            evil_skill.py:53

[3/3] Residue Scan
  (skipped β€” use `prism clean --scan` for system-level checks)

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  Grade: F (Critical)

  Key Risks:
    β–Έ CRITICAL: Data exfiltration to external endpoint (S8)
    β–Έ CRITICAL: Download and execute remote payload (S10)
    β–Έ CRITICAL: Hardcoded AWS credential (P1)
    β–Έ HIGH: Shell execution with user input (S1)
    β–Έ HIGH: Persistence mechanism installed (S13)

  Behavior Profile:
    exfiltrates_data, executes_shell, downloads_and_executes, installs_persistence

  Recommendation: DO NOT INSTALL β€” Critical security risks detected.

  Findings: 3 critical, 2 high, 3 medium
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Grading System

GradeLabelMeaningRecommendation
ASafeNo findings or only informationalSafe to use
BNoticeOnly LOW severity findingsLikely safe β€” minor observations
CCaution1-4 MEDIUM findingsReview before use β€” warrants manual inspection
DDanger1-2 HIGH, or 5+ MEDIUMUse in sandbox only β€” significant risks
FCriticalAny CRITICAL, or 3+ HIGHDo not install β€” critical security risks

Output Formats

bash
# Rich terminal output (default) β€” color-coded severity, grade highlighting
prism scan ./skill/

# Machine-readable JSON
prism scan ./skill/ --format json

# Standalone HTML report β€” dark-themed, self-contained, shareable
prism scan ./skill/ --format html -o report.html

# SARIF for GitHub Code Scanning
prism scan ./skill/ --format sarif -o results.sarif

HTML Report

Generate a standalone, self-contained HTML report with full findings, grade visualization, and risk breakdown β€” perfect for sharing with teammates or archiving:

bash
prism scan https://github.com/user/skill-repo --format html -o report.html

The HTML report features a dark-themed UI with color-coded severity levels, an interactive grade ring, and a complete findings table. No external dependencies β€” just open the .html file in any browser.


CI/CD Integration

Add Prism to your GitHub Actions workflow to gate deployments on security findings:

yaml
name: Security Scan
on: [push, pull_request]

jobs:
  prism-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.12"

      - name: Install Prism Scanner
        run: pip install prism-scanner

      - name: Run security scan
        run: prism scan . --format sarif -o results.sarif --fail-on high

      - name: Upload SARIF results
        if: always()
        uses: github/codeql-action/upload-sarif@v3
        with:
          sarif_file: results.sarif

The --fail-on flag exits with code 1 if any finding meets or exceeds the specified severity (critical, high, or medium), failing the CI pipeline.


Key Features

  • Taint Analysis β€” Tracks data flow from sources (env vars, user input) to sinks (shell, network) within each file
  • Multi-Platform β€” Scans ClawHub skills, MCP servers, npm packages, and pip packages with platform-aware rules
  • Zero Dependencies on Target β€” Pure static analysis; never executes scanned code
  • Residue Scanner β€” Detects persistence mechanisms, shell config pollution, and credential leaks left by uninstalled tools
  • Safe Cleanup β€” Three-tier workflow (scan -> plan -> apply) with automatic backup and --rollback
  • Suppression β€” Use .prismignore to suppress known findings by rule ID with justification
  • Offline Mode β€” Run with --offline to skip all external lookups

Adding Custom Rules

Detection rules are defined in YAML files under the rules/ directory:

Code
rules/
β”œβ”€β”€ malicious_signatures.yaml   # Known malicious code signatures and hashes
β”œβ”€β”€ permissions.yaml            # Permission baseline definitions
└── suspicious_domains.yaml     # C2 domains, dynamic DNS, disposable TLDs

The pattern engine (P1-P9) and manifest engine (M1-M6) load rules from these files at scan time. Add entries to extend detection without modifying Python code.


Project Structure

Code
src/prism/
β”œβ”€β”€ cli.py                  # CLI entry point and output formatting
β”œβ”€β”€ scanner.py              # Orchestrator β€” runs engines, collects findings
β”œβ”€β”€ models.py               # Finding, ScanResult, Severity, Layer data models
β”œβ”€β”€ scoring.py              # Letter-grade risk assessment (A-F)
β”œβ”€β”€ report.py               # HTML report generator
β”œβ”€β”€ fetcher.py              # Git clone / URL fetching with security guards
β”œβ”€β”€ cleaner.py              # System cleanup: plan, apply, rollback
β”œβ”€β”€ suppression.py          # .prismignore parsing
β”œβ”€β”€ rules_loader.py         # YAML rule loading
└── engines/
    β”œβ”€β”€ ast_engine.py       # AST-based analysis (S1-S14)
    β”œβ”€β”€ pattern_engine.py   # Regex pattern matching (P1-P9)
    β”œβ”€β”€ manifest_engine.py  # Metadata & manifest analysis (M1-M6)
    β”œβ”€β”€ residue_engine.py   # System residue scanner (R1-R10)
    └── taint.py            # Intra-file taint tracking

Contributing

Contributions are welcome. Please see CONTRIBUTING.md for guidelines on:

  • Adding new detection rules
  • Writing and running tests
  • Code style and commit conventions

License

Apache License 2.0 β€” free for commercial and personal use.


Prism Verified Badge

Show that your project has been scanned by Prism. Add a badge to your README:

Grade A (Safe):

markdown
[![Prism Grade A](https://img.shields.io/badge/Prism-Grade%20A-brightgreen?style=flat-square&logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgZmlsbD0id2hpdGUiPjxwYXRoIGQ9Ik0xMiAxTDMgNXY2YzAgNS41NSAzLjg0IDEwLjc0IDkgMTIgNS4xNi0xLjI2IDktNi40NSA5LTEyVjVsLTktNHoiLz48L3N2Zz4=)](https://github.com/aidongise-cell/prism-scanner)

All grades:

GradeBadge
A![Prism Grade A](https://img.shields.io/badge/Prism-Grade%20A-brightgreen?style=flat-square)
B![Prism Grade B](https://img.shields.io/badge/Prism-Grade%20B-green?style=flat-square)
C![Prism Grade C](https://img.shields.io/badge/Prism-Grade%20C-yellow?style=flat-square)
D![Prism Grade D](https://img.shields.io/badge/Prism-Grade%20D-orange?style=flat-square)
F![Prism Grade F](https://img.shields.io/badge/Prism-Grade%20F-red?style=flat-square)

Preview:

Prism Grade A Prism Grade B Prism Grade C Prism Grade D Prism Grade F


Support

If Prism Scanner helped you catch a security risk or gave you peace of mind, please consider giving it a ⭐ on GitHub β€” it helps others discover the project and keeps development going.

Star on GitHub


Acknowledgments

Prism Scanner is developed by Prism Lab to address a gap in the AI agent ecosystem: the lack of transparent, code-level security tooling that works across platforms and covers the full agent lifecycle. We believe developers deserve to understand exactly what a skill or plugin does before trusting it with their system.

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Frequently Asked Questions about Prism Scanner

Add the following block to your claude_desktop_config.json under mcpServers: "mcpServers": { "prism-scanner": { "command": "npx", "args": ["-y", "Prism Scanner"] } }

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Technical Specs & Signals

CategoryπŸ”’Security
More technical detailsExpand β–Ύ
TransportSTDIO
RuntimeNode.js
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27Quality signal: Emerging Β· 27/100How this signal is calculated β–Ύ
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Not scored for repo-hosted servers β€” we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership8/20
Documentation & tools11/30
Adoption & activity1/15
Community engagement0/10

A guidance signal from public completeness & health data β€” not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.

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