SkillSpector

Security scanner for AI agent skills. Detect vulnerabilities, malicious patterns, and security risks before installing agent skills.

Overview

AI agent skills (used by Claude Code, Codex CLI, Gemini CLI, etc.) execute with implicit trust and minimal vetting. Research shows that 26.1% of skills contain vulnerabilities and 5.2% show likely malicious intent.

SkillSpector helps you answer: "Is this skill safe to install?"

Documentation

Development guide — Architecture, package layout, and how to extend the analyzer pipeline.
Pi extension — Install SkillSpector as a Pi tool for scanning skills from inside agent sessions.

Features

Multi-format input: Scan Git repos, URLs, zip files, directories, or single files
68 vulnerability patterns across 17 categories: prompt injection, data exfiltration, privilege escalation, supply chain, excessive agency, output handling, system prompt leakage, memory poisoning, tool misuse, rogue agent, anti-refusal, trigger abuse, dangerous code (AST), taint tracking, YARA signatures, MCP least privilege, and MCP tool poisoning
Two-stage analysis: Fast static analysis + optional LLM semantic evaluation
Live vulnerability lookups: SC4 queries OSV.dev for real-time CVE data with automatic offline fallback
Multiple output formats: Terminal, JSON, Markdown, and SARIF reports
Risk scoring: 0-100 score with severity labels and clear recommendations
Baseline / false-positive suppression: Accept known findings via a glob-rule or fingerprint baseline so re-scans surface only new issues (docs)

Quick Start

Installation

Create and activate a virtual environment first (all make targets assume the venv is active). Use uv or pip; the Makefile uses uv if available, otherwise pip.

Quick install with uv (no clone required):

uv tool install git+https://github.com/NVIDIA/skillspector.git
# Update later: uv tool update skillspector

From source:

# Clone the repository
git clone https://github.com/NVIDIA/skillspector.git
cd skillspector

# Create and activate virtual environment
uv venv .venv && source .venv/bin/activate
# or: python3 -m venv .venv && source .venv/bin/activate

# Install for production use
make install

# Or install with development dependencies
make install-dev

Docker (no Python required)

Run SkillSpector without installing Python by building it locally from the included Dockerfile. The image is based on the Docker Official Python 3.12-slim-bookworm image.

Build the image:

make docker-build
# or: docker build -t skillspector .

Scan a local directory by mounting your current directory into /scan, the container's working directory:

docker run --rm -v "$PWD:/scan" skillspector scan ./my-skill/ --no-llm

Scan with LLM analysis by passing credentials with a local .env file:

cat > .env <<'EOF'
SKILLSPECTOR_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-...
EOF

docker run --rm \
  -v "$PWD:/scan" \
  --env-file .env \
  skillspector scan ./my-skill/

Or pass credentials directly from your shell environment:

docker run --rm \
  -v "$PWD:/scan" \
  -e SKILLSPECTOR_PROVIDER=anthropic \
  -e ANTHROPIC_API_KEY="$ANTHROPIC_API_KEY" \
  skillspector scan ./my-skill/

Write a report to the host filesystem by writing to the mounted directory:

docker run --rm \
  -v "$PWD:/scan" \
  skillspector scan ./my-skill/ --no-llm --format json --output report.json

Optional alias for repeated static scans:

alias skillspector-docker='docker run --rm -v "$PWD:/scan" skillspector'
skillspector-docker scan ./my-skill/ --no-llm

Basic Usage

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

# Scan a single SKILL.md file
skillspector scan ./SKILL.md

# Scan a Git repository
skillspector scan https://github.com/user/my-skill

# Scan a zip file
skillspector scan ./my-skill.zip

Output Formats

# Terminal output (default) - pretty formatted
skillspector scan ./my-skill/

# JSON output - machine readable
skillspector scan ./my-skill/ --format json --output report.json

# Markdown output - for documentation
skillspector scan ./my-skill/ --format markdown --output report.md

# SARIF output - for CI/CD integration and IDE tooling
skillspector scan ./my-skill/ --format sarif --output report.sarif

Suppressing False Positives (baseline)

Suppress known/accepted findings so the risk score reflects only un-triaged issues and re-scans surface only new findings. See the suppression guide for the full reference.

# Accept all current findings into a baseline (run once), then commit it.
skillspector baseline ./my-skill/ -o .skillspector-baseline.yaml

# Scan against the baseline — only NEW findings are reported and scored.
skillspector scan ./my-skill/ --baseline .skillspector-baseline.yaml

# Review what was suppressed (still excluded from the score).
skillspector scan ./my-skill/ --baseline .skillspector-baseline.yaml --show-suppressed

A baseline can also use drift-tolerant glob rules (by rule id, file path, or message) — see .skillspector-baseline.example.yaml.

LLM Analysis

For the best results, configure an OpenAI-compatible LLM endpoint for semantic analysis. Pick a provider with SKILLSPECTOR_PROVIDER; each ships its own bundled default model. SkillSpector also works against local OpenAI-compatible servers (Ollama, vLLM, llama.cpp) and managed inference gateways.

Provider (`SKILLSPECTOR_PROVIDER`)	Credential env var	Endpoint	Default model
`openai`	`OPENAI_API_KEY` (+ optional `OPENAI_BASE_URL`)	api.openai.com (or any OpenAI-compatible URL)	`gpt-5.4`
`anthropic`	`ANTHROPIC_API_KEY`	api.anthropic.com	`claude-opus-4-6`
`anthropic_proxy`	`ANTHROPIC_PROXY_API_KEY` + `ANTHROPIC_PROXY_ENDPOINT_URL`	Any Vertex-style raw-predict proxy	`claude-sonnet-4-6`
`nv_build`	`NVIDIA_INFERENCE_KEY`	build.nvidia.com	`deepseek-ai/deepseek-v4-flash`

# Stock OpenAI
export SKILLSPECTOR_PROVIDER=openai
export OPENAI_API_KEY=sk-...
skillspector scan ./my-skill/

# Anthropic
export SKILLSPECTOR_PROVIDER=anthropic
export ANTHROPIC_API_KEY=sk-ant-...
skillspector scan ./my-skill/

# Anthropic via Vertex-style proxy (corporate gateways, GCP Vertex AI)
export SKILLSPECTOR_PROVIDER=anthropic_proxy
export ANTHROPIC_PROXY_ENDPOINT_URL=https://my-gateway.example.com/models/claude-sonnet-4-6:streamRawPredict
export ANTHROPIC_PROXY_API_KEY=your-bearer-token
export SKILLSPECTOR_MODEL=claude-sonnet-4-6
skillspector scan ./my-skill/

# NVIDIA build.nvidia.com
export SKILLSPECTOR_PROVIDER=nv_build
export NVIDIA_INFERENCE_KEY=nvapi-...
skillspector scan ./my-skill/

# Local Ollama or any OpenAI-compatible endpoint
export SKILLSPECTOR_PROVIDER=openai
export OPENAI_API_KEY=ollama
export OPENAI_BASE_URL=http://localhost:11434/v1
export SKILLSPECTOR_MODEL=llama3.1:8b
skillspector scan ./my-skill/

# Override the provider's default model
export SKILLSPECTOR_MODEL=gpt-5.2
skillspector scan ./my-skill/

# Skip LLM analysis (faster, static analysis only)
skillspector scan ./my-skill/ --no-llm

MCP Server

Run SkillSpector as a Model Context Protocol server so any MCP-capable agent (Claude Code, Codex CLI, Gemini CLI) or remote runtime can call scanning as a tool and gate skill/MCP installs on the result — turning SkillSpector into a runtime guardrail instead of an out-of-band audit step.

# Install the optional MCP dependency
pip install "skillspector[mcp]"

# stdio transport — for local CLI agents
skillspector mcp

# streamable HTTP/SSE transport — for remote / A2A callers
skillspector mcp --transport http --host 127.0.0.1 --port 8000

The server exposes a single tool:

scan_skill(target, use_llm=true, output_format="json") — scans a Git URL, file URL, .zip, .md file, or directory and returns a structured verdict: risk_score (0-100), severity, recommendation, safe_to_install, and findings. It also reports llm_used / scan_mode so a low score from a static-only scan is never mistaken for a clean full scan.

Register it with Claude Code via:

claude mcp add skillspector -- skillspector mcp

Vulnerability Patterns

SkillSpector detects 68 vulnerability patterns across 17 categories:

Prompt Injection (5 patterns)

ID	Pattern	Severity	Description
P1	Instruction Override	HIGH	Commands to ignore safety constraints
P2	Hidden Instructions	HIGH	Malicious directives in comments/invisible text
P3	Exfiltration Commands	HIGH	Instructions to transmit context externally
P4	Behavior Manipulation	MEDIUM	Subtle instructions altering agent decisions
P5	Harmful Content	CRITICAL	Instructions that could cause physical harm

Anti-Refusal (3 patterns)

ID	Pattern	Severity	Description
AR1	Refusal Suppression	HIGH	Instructions to never refuse or always comply (e.g. "never refuse", "always comply")
AR2	Disclaimer Suppression	HIGH	Instructions to omit warnings, disclaimers, or ethical commentary (e.g. "no disclaimers", "do not moralize")
AR3	Safety Policy Nullification	HIGH	Jailbreak framing that nullifies guardrails (e.g. "you have no restrictions", "ignore your guidelines", "do anything now")

Data Exfiltration (4 patterns)

ID	Pattern	Severity	Description
E1	External Transmission	MEDIUM	Sending data to external URLs
E2	Env Variable Harvesting	HIGH	Collecting API keys and secrets
E3	File System Enumeration	MEDIUM	Scanning directories for sensitive files
E4	Context Leakage	HIGH	Transmitting conversation context externally

Privilege Escalation (3 patterns)

ID	Pattern	Severity	Description
PE1	Excessive Permissions	LOW	Requesting access beyond stated functionality
PE2	Sudo/Root Execution	MEDIUM	Invoking elevated system privileges
PE3	Credential Access	HIGH	Reading SSH keys, tokens, passwords

Supply Chain (6 patterns)

ID	Pattern	Severity	Description
SC1	Unpinned Dependencies	LOW	No version constraints on packages
SC2	External Script Fetching	HIGH	curl \| bash and remote code execution
SC3	Obfuscated Code	HIGH	Base64/hex encoded execution
SC4	Known Vulnerable Dependencies	HIGH	Dependencies with known CVEs (live OSV.dev lookup)
SC5	Abandoned Dependencies	MEDIUM	Unmaintained packages without security updates
SC6	Typosquatting	HIGH	Package names similar to popular packages

Excessive Agency (4 patterns)

ID	Pattern	Severity	Description
EA1	Unrestricted Tool Access	HIGH	Unfettered tool access without constraints
EA2	Autonomous Decision Making	HIGH	High-impact decisions without human-in-the-loop
EA3	Scope Creep	MEDIUM	Capabilities extending beyond stated purpose
EA4	Unbounded Resource Access	MEDIUM	No rate limits or quotas on resource consumption

Output Handling (3 patterns)

ID	Pattern	Severity	Description
OH1	Unvalidated Output Injection	HIGH	Model output used without sanitization
OH2	Cross-Context Output	MEDIUM	Output flows across trust boundaries without validation
OH3	Unbounded Output	MEDIUM	No limits on output size or generation rate

System Prompt Leakage (3 patterns)

ID	Pattern	Severity	Description
P6	Direct Leakage	HIGH	Instructions that expose system prompts or internal rules
P7	Indirect Extraction	MEDIUM	Extraction via rephrasing, translation, or side-channels
P8	Tool-Based Exfiltration	HIGH	System prompts exfiltrated via file writes or network requests

Memory Poisoning (3 patterns)

ID	Pattern	Severity	Description
MP1	Persistent Context Injection	HIGH	Content designed to persist across interactions
MP2	Context Window Stuffing	MEDIUM	Filler content displacing safety constraints
MP3	Memory Manipulation	HIGH	Tampering with agent memory or stored state

Tool Misuse (3 patterns)

ID	Pattern	Severity	Description
TM1	Tool Parameter Abuse	HIGH	Crafted parameters for unintended behavior (shell=True, --force)
TM2	Chaining Abuse	HIGH	Tool chains that bypass individual safety checks
TM3	Unsafe Defaults	MEDIUM	Overly permissive defaults (disabled TLS, no auth)

Rogue Agent (2 patterns)

ID	Pattern	Severity	Description
RA1	Self-Modification	CRITICAL	Modifying own code or configuration at runtime
RA2	Session Persistence	HIGH	Unauthorized persistence via cron jobs or startup scripts

Trigger Abuse (3 patterns)

ID	Pattern	Severity	Description
TR1	Overly Broad Trigger	MEDIUM	Trigger patterns matching common words
TR2	Shadow Command Trigger	HIGH	Triggers that shadow built-in commands or other skills
TR3	Keyword Baiting Trigger	MEDIUM	Generic triggers designed to maximize activation

Behavioral AST (9 patterns)

ID	Pattern	Severity	Description
AST1	exec() Call	CRITICAL	Direct exec() enabling arbitrary code execution
AST2	eval() Call	HIGH	Direct eval() evaluating arbitrary expressions
AST3	Dynamic Import	HIGH	__import__() loading arbitrary modules at runtime
AST4	subprocess Call	HIGH	External command execution via subprocess
AST5	os.system / exec-family	HIGH	Shell commands via os module
AST6	compile() Call	MEDIUM	Code object creation from strings
AST7	Dynamic getattr()	MEDIUM	Arbitrary attribute access with non-literal names
AST8	Dangerous Execution Chain	CRITICAL	exec/eval combined with dynamic source (network, encoded data)
AST9	Reflective getattr() Sink	HIGH	Reflective exec via `getattr(os,'system')` / `getattr(builtins,'exec')` that evades AST1/AST5

Taint Tracking (5 patterns)

ID	Pattern	Severity	Description
TT1	Direct Taint Flow	HIGH	Data flows directly from a source to a sink without sanitization
TT2	Variable-Mediated Taint Flow	MEDIUM	Data flows from source to sink through intermediate variables
TT3	Credential Exfiltration Chain	CRITICAL	Credentials (env vars, secrets) flow to network output sinks
TT4	File Read to Network Exfiltration	HIGH	File contents flow to network output sinks
TT5	External Input to Code Execution	CRITICAL	Network or user input flows to exec/eval/subprocess sinks

YARA Signatures (4 patterns)

ID	Pattern	Severity	Description
YR1	Malware Match	CRITICAL	YARA rule match for known malware signatures
YR2	Webshell Match	CRITICAL	YARA rule match for webshell patterns
YR3	Cryptominer Match	HIGH	YARA rule match for crypto mining indicators
YR4	Hack Tool / Exploit Match	HIGH	YARA rule match for hack tools or exploit code

MCP Least Privilege (4 patterns)

ID	Pattern	Severity	Description
LP1	Underdeclared Capability	HIGH	Code uses capabilities not listed in declared permissions
LP2	Wildcard Permission	MEDIUM	Permission list contains wildcards (*, all, full, any)
LP3	Missing Permission Declaration	MEDIUM	No permissions field but code has detectable capabilities
LP4	Overdeclared Permission	LOW	Permission declared but no corresponding code capability found

MCP Tool Poisoning (4 patterns)

ID	Pattern	Severity	Description
TP1	Hidden Instructions	HIGH	Hidden directives in metadata (HTML comments, zero-width chars, base64, data URIs)
TP2	Unicode Deception	HIGH	Homoglyphs, RTL overrides, mixed-script identifiers in tool metadata
TP3	Parameter Description Injection	MEDIUM	Injection patterns in parameter definitions (overrides, system tokens, malicious defaults)
TP4	Description-Behavior Mismatch	MEDIUM	Declared tool description does not match actual code behavior (LLM-powered)

All detected patterns are listed in the tables above.

Risk Scoring

Score Calculation

CRITICAL issues: +50 points
HIGH issues: +25 points
MEDIUM issues: +10 points
LOW issues: +5 points
Executable scripts: 1.3x multiplier

Severity Levels

Score	Severity	Recommendation
0-20	LOW	SAFE
21-50	MEDIUM	CAUTION
51-80	HIGH	DO NOT INSTALL
81-100	CRITICAL	DO NOT INSTALL

Example Output

Terminal Output

 SkillSpector Security Report  v2.0.0

Skill: suspicious-skill
Source: ./suspicious-skill/
Scanned: 2026-01-29 10:30:00 UTC

        Risk Assessment
 Metric          Value
 Score           78/100
 Severity        HIGH
 Recommendation  DO NOT INSTALL

        Components (3)
 File              Type      Lines  Executable
 SKILL.md          markdown    142  No
 scripts/sync.py   python       87  Yes
 requirements.txt  text          3  No

Issues (2)

  HIGH: Env Variable Harvesting (E2)
    Location: scripts/sync.py:23
    Finding: for key, val in os.environ.items():...
    Confidence: 94%
    Explanation: This code collects environment variables containing
    API keys and secrets, then sends them to an external server.

  HIGH: External Transmission (E1)
    Location: scripts/sync.py:45
    Finding: requests.post("https://api.skill.io/env"...
    Confidence: 89%
    Explanation: Data is being sent to an external server. Combined
    with env harvesting above, this indicates credential exfiltration.

Configuration

Environment Variables

Variable	Description	Required
`SKILLSPECTOR_PROVIDER`	Active LLM provider: `openai`, `anthropic`, or `nv_build`. Each provider has its own bundled `model_registry.yaml` and default model (see the LLM Analysis table above). Defaults to `nv_build`.	Optional
`NVIDIA_INFERENCE_KEY`	Credential for the `nv_build` provider (build.nvidia.com).	Required for LLM analysis when `SKILLSPECTOR_PROVIDER=nv_build`
`OPENAI_API_KEY`	Credential for the OpenAI provider (`SKILLSPECTOR_PROVIDER=openai`). Also serves as the tier-2 fallback in the credential waterfall when the active provider returns no credentials.	Required for LLM analysis when `SKILLSPECTOR_PROVIDER=openai`
`OPENAI_BASE_URL`	Override the OpenAI endpoint (e.g. point at Ollama).	Optional
`ANTHROPIC_API_KEY`	Credential for the Anthropic provider (`SKILLSPECTOR_PROVIDER=anthropic`).	Required for LLM analysis when `SKILLSPECTOR_PROVIDER=anthropic`
`ANTHROPIC_PROXY_ENDPOINT_URL`	Full endpoint URL for the Anthropic proxy provider (Vertex-style raw-predict).	Required when `SKILLSPECTOR_PROVIDER=anthropic_proxy`
`ANTHROPIC_PROXY_API_KEY`	Bearer token for the Anthropic proxy provider.	Required when `SKILLSPECTOR_PROVIDER=anthropic_proxy`
`ANTHROPIC_PROXY_API_VERSION`	`anthropic_version` value sent in the request body (default: `vertex-2023-10-16`).	Optional
`SKILLSPECTOR_MODEL`	Override the active provider's default model. See the LLM Analysis table for each provider's default.	Optional
`SKILLSPECTOR_MODEL_REGISTRY`	Override the bundled per-provider YAML registry (`src/skillspector/providers/<provider>/model_registry.yaml`) with a custom path.	Optional
`SKILLSPECTOR_LOG_LEVEL`	Log level: `DEBUG`, `INFO`, `WARNING`, `ERROR` (default: `WARNING`).	Optional

CLI Options

skillspector scan --help

Options:
  -f, --format [terminal|json|markdown|sarif]  Output format [default: terminal]
  -o, --output PATH                            Output file path
  --no-llm                                     Skip LLM analysis (static only)
  --yara-rules-dir PATH                        Extra YARA rules directory
  -b, --baseline PATH                          Suppress findings listed in a baseline
  --show-suppressed                            List baseline-suppressed findings
  -V, --verbose                                Show detailed progress
  --help                                       Show this message and exit

# Generate a baseline of all current findings (see docs/SUPPRESSION.md)
skillspector baseline <path> [-o FILE] [--no-llm] [--reason TEXT]

Integrating SkillSpector

SkillSpector is built to be driven by other tools (CI pipelines, install gates, editor integrations). Its exit code and JSON output are a stable contract.

Exit codes

skillspector scan exits with:

Code	Meaning
`0`	Scan completed, `risk_score` ≤ 50 (recommendation `SAFE` or `CAUTION`)
`1`	Scan completed, `risk_score` > 50 (recommendation `DO_NOT_INSTALL`)
`2`	Error (bad input, unreadable source, internal failure)

The exit code collapses SAFE and CAUTION into 0. To act differently on them (e.g. warn on CAUTION but block on DO_NOT_INSTALL), read the recommendation field from the JSON output rather than relying on the exit code.

Machine-readable output

--format json produces a JSON report; with no --output/-o it is written to stdout:

skillspector scan ./my-skill/ --format json

The top-level shape is (this example shows a full LLM-backed scan; with --no-llm, metadata.llm_requested is false):

{
  "skill": { "name": "...", "source": "...", "scanned_at": "<ISO 8601>" },
  "risk_assessment": { "score": 0, "severity": "LOW", "recommendation": "SAFE" },
  "components": [ { "path": "...", "type": "...", "lines": 0, "executable": false, "size_bytes": 0 } ],
  "issues": [ { "id": "...", "category": "...", "severity": "...", "confidence": 0.0, "location": { "file": "...", "start_line": 0 } } ],
  "metadata": { "has_executable_scripts": false, "skillspector_version": "...", "llm_requested": true, "llm_available": true }
}

risk_assessment.severity ∈ LOW | MEDIUM | HIGH | CRITICAL.
risk_assessment.recommendation ∈ SAFE | CAUTION | DO_NOT_INSTALL, mapped from severity: LOW → SAFE, MEDIUM → CAUTION, HIGH/CRITICAL → DO_NOT_INSTALL.
metadata.llm_error appears only when LLM analysis was requested but unavailable.
The full per-issue shape is defined by Finding.to_dict() in models.py; rely on the fields above and treat any additional fields as best-effort.

For CI/IDE tooling, --format sarif emits SARIF 2.1.0.

Recommended gate mapping

When using SkillSpector as an install gate, map the recommendation to an action:

`recommendation`	Suggested action
`SAFE`	allow
`CAUTION`	prompt / warn the user
`DO_NOT_INSTALL`	block

SkillSpector computes the score band and recommendation; how strict the gate is (e.g. whether CAUTION blocks in CI) is a policy decision for the integrating tool.

Development

Setup

All make targets assume a virtual environment is already created and activated. The Makefile uses uv if available, else pip.

# Clone, create venv, activate, install dev dependencies
git clone https://github.com/NVIDIA/skillspector.git
cd skillspector
uv venv .venv && source .venv/bin/activate
# or: python3 -m venv .venv && source .venv/bin/activate
make install-dev

# Run tests
make test

# Run tests with coverage
make test-cov

# Run linting
make lint

# Format code
make format

How It Works

SkillSpector uses a two-stage detection pipeline:

Stage 1: Static Analysis

Fast regex-based pattern matching across 11 static analyzers
AST-based behavioral analysis detecting dangerous calls (exec, eval, subprocess, etc.)
Live vulnerability lookups via OSV.dev for known CVEs in dependencies
Scans all files in the skill
High recall (catches most issues)
Moderate precision (some false positives)

Stage 2: LLM Semantic Analysis (Optional)

Evaluates context and intent
Filters false positives
Provides human-readable explanations
Improves precision to ~87%

The LLM prompt includes anti-jailbreak protections to prevent malicious skills from manipulating the analysis.

Live Vulnerability Lookups (SC4)

SC4 uses the OSV.dev API to check dependencies against the full Open Source Vulnerabilities database — covering tens of thousands of advisories across PyPI and npm.

No API key required — OSV.dev is free and unauthenticated.
Batch queries — all dependencies are checked in a single HTTP call.
Automatic fallback — if OSV.dev is unreachable (air-gapped/offline), a small built-in fallback list is used.
Caching — results are cached in-memory for 1 hour to avoid redundant API calls during a session.

The tool requires outbound HTTPS access to api.osv.dev for live vulnerability data. When that is not available, findings are limited to the static fallback list.

Trust model and data egress

SkillSpector is defense-in-depth, not a sandbox. Know what it does and does not do before relying on it:

It never executes the scanned skill. All analysis is static (regex, Python AST, YARA) plus optional LLM evaluation of file contents — the skill's code is never run.
LLM analysis sends file contents to the configured provider. When LLM analysis is enabled (the default), file contents are sent to the active SKILLSPECTOR_PROVIDER endpoint. Use --no-llm to keep contents local (static analysis only).
SC4 sends dependency names to OSV.dev. The supply-chain check queries OSV.dev with the package names and versions the skill declares, to look up known CVEs. This is fundamental to the check and runs even with --no-llm. It sends dependency coordinates (not file contents), requires no API key, and falls back to a bundled list when OSV.dev is unreachable.
It does not sandbox the host. SkillSpector flags risky patterns before you install a skill; it does not contain or isolate a skill you choose to install anyway.

Limitations

Non-English content: May miss patterns in other languages
Image-based attacks: Cannot analyze text in images
Encrypted/binary code: Cannot analyze compiled or encrypted content
Runtime behavior: Static analysis only, no dynamic execution
Offline SC4: Without network access to api.osv.dev, SC4 uses a small static fallback list

Research Background

Based on research from "Agent Skills in the Wild: An Empirical Study of Security Vulnerabilities at Scale" (Liu et al., 2026):

Dataset: 42,447 skills from major marketplaces
Vulnerable: 26.1% contain at least one vulnerability
High-severity: 5.2% show likely malicious intent
Key finding: Skills with executable scripts are 2.12x more likely to be vulnerable

Python API Integration

from skillspector import graph

# Invoke the LangGraph workflow
result = graph.invoke({
    "input_path": "/path/to/skill",
    "output_format": "json",   # terminal, json, markdown, or sarif
    "use_llm": True,           # False for static-only analysis
})

# Access results
print(f"Risk Score: {result['risk_score']}/100")
print(f"Severity: {result['risk_severity']}")
print(f"Recommendation: {result['risk_recommendation']}")

for finding in result["filtered_findings"]:
    print(f"[{finding['severity']}] {finding['rule_id']}: {finding['message']}")

License

Apache License 2.0 - see LICENSE for details.

Contributing

Contributions are welcome! Please read our contributing guidelines and submit pull requests.

Support

Issues: GitHub Issues

Name		Name	Last commit message	Last commit date
Latest commit History 156 Commits
.github/workflows		.github/workflows
docs		docs
extensions		extensions
src/skillspector		src/skillspector
tests		tests
.dockerignore		.dockerignore
.env.example		.env.example
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
.skillspector-baseline.example.yaml		.skillspector-baseline.example.yaml
CONTRIBUTING.md		CONTRIBUTING.md
Dockerfile		Dockerfile
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
SECURITY.md		SECURITY.md
THIRD_PARTY_NOTICES.md		THIRD_PARTY_NOTICES.md
langgraph.json		langgraph.json
model_registry.yaml		model_registry.yaml
package.json		package.json
pyproject.toml		pyproject.toml
uv.lock		uv.lock

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SkillSpector

Overview

Documentation

Features

Quick Start

Installation

Docker (no Python required)

Basic Usage

Output Formats

Suppressing False Positives (baseline)

LLM Analysis

MCP Server

Vulnerability Patterns

Prompt Injection (5 patterns)

Anti-Refusal (3 patterns)

Data Exfiltration (4 patterns)

Privilege Escalation (3 patterns)

Supply Chain (6 patterns)

Excessive Agency (4 patterns)

Output Handling (3 patterns)

System Prompt Leakage (3 patterns)

Memory Poisoning (3 patterns)

Tool Misuse (3 patterns)

Rogue Agent (2 patterns)

Trigger Abuse (3 patterns)

Behavioral AST (9 patterns)

Taint Tracking (5 patterns)

YARA Signatures (4 patterns)

MCP Least Privilege (4 patterns)

MCP Tool Poisoning (4 patterns)

Risk Scoring

Score Calculation

Severity Levels

Example Output

Terminal Output

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Integrating SkillSpector

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Recommended gate mapping

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How It Works

Stage 1: Static Analysis

Stage 2: LLM Semantic Analysis (Optional)

Live Vulnerability Lookups (SC4)

Trust model and data egress

Limitations

Research Background

Python API Integration

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