NVIDIA/SkillSpector

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

AI agent skills (used by Claude Code, Codex CLI, Gemini CLI, etc.) execute with implicit trust and minimal vetting. In the 31,132-skill analyzed subset of the research dataset, 26.1% of skills contain vulnerabilities and 5.2% show likely malicious intent.

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

SkillSpector is part of the NVIDIA Verified Skills pipeline, which scans, evaluates, and signs agent skills before publication. Skills that pass are published to the NVIDIA skills catalog.

• Scan agent skills before installation — Hosted guide: when to scan, how to read a report, and how to gate installs.
• Development guide — Architecture, package layout, and how to extend the analyzer pipeline.
• Analysis resource bounds — Fail-closed bundle, parser, nested-artifact, ledger, and finding ceilings.
• Pi extension — Install SkillSpector as a Pi tool for scanning skills from inside agent sessions.
• OpenCode extension — Install SkillSpector as an OpenCode tool and /skillspector command for scanning skills from inside agent sessions.

• Multi-format input: Scan Git repos, URLs, zip files, directories, or single files
• 71 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)

Open-source software notice: This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.

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 (CLI-only):

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

If you plan to run skillspector mcp, install the MCP extra at install time:

uv tool install 'skillspector[mcp] @ git+https://github.com/NVIDIA/skillspector.git'

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

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

# 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

SkillSpector enforces two independent caps on remote and archive inputs to bound the impact of oversized downloads and zip bombs:

• Per-ingest cap: INGEST_MAX_BYTES (100 MiB) — applied to streamed URL downloads, total uncompressed size of zip archives, and post-clone disk usage of Git repos.
• Zip member cap: INGEST_MAX_ZIP_MEMBERS (10,000) — caps the number of entries in a single zip.

Note that the per-file 1 MB analysis cap (MAX_FILE_BYTES) is a separate, downstream limit: it bounds what individual analyzers will read out of an already-ingested directory. The ingest caps above bound how much content can land on disk in the first place. A breach of either ingest cap fails closed with an IngestLimitExceededError.

# 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

Scan entire directories of skills in parallel from contrib/batch_scan/:

python -m contrib.batch_scan.batch_scan ./my-skills/ --no-llm
python -m contrib.batch_scan.batch_scan ./my-skills/ --workers 20 -f json -o report.json
python -m contrib.batch_scan.batch_scan ./tests/fixtures/ -f terminal --workers 20

Supports multilingual detection (zh/ja/ko) and terminal/JSON/Markdown output.

For LLM scans with higher concurrency, configure multiple API keys following .env.example — the pool improves throughput and resilience, provided the keys don't share an account-level rate limit.

See the contrib guide for details.

Note on LLM support: The default configuration targets DeepSeek as the cheapest public option. DeepSeek-Chat is expected to sunset, and the contributor does not have hardware to test against local models. The batch scanner was originally tested with OpenAI-compatible endpoints — DeepSeek's lack of structured-output support required manual JSON-parsing patches. If you can contribute a more universal backend (Ollama, vLLM, or a different provider), PRs are very welcome.

Start with raw registry payload captures (registry-before.json and registry-after.json). Save a scan report as previous.json, then compare a later scan against that generated local report:

skillspector scan registry-before.json --mcp-registry --format json --output previous.json
skillspector scan registry-after.json --mcp-registry --format json \
  --mcp-registry-compare previous.json --output compared.json

The optional comparison object lists added and removed server identities, changed normalized fields with their previous and current values, and an unchanged_count. Identity is the server name and version, so a new version appears as an addition and the old version as a removal if it is absent from the new scan. Acquisition source and scan timestamp are excluded from comparison. unmodeled_changes lists same-identity records whose raw-record hash changed while normalized fields match; these are not counted as unchanged. This can indicate a change to fields outside the snapshot model, or array reordering in the raw record. Package and remote ordering alone is not a normalized field change. Entries in changed may also contain changes outside the snapshot model; inspect the raw record to see those changes. Compare reports with the same selection scope: a server absent from the current input is reported as removed, which does not prove it was removed from a registry.

The comparison file must be a local report produced by this registry mode with valid normalized snapshots. Malformed, oversized or duplicate server identities are rejected. This option requires --mcp-registry and is separate from the finding-suppression --baseline: it never suppresses findings or changes risk scores, and it does not fetch or execute listed server endpoints.

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. Exact fingerprint baselines are evidence-bound: changing the scanned source or SkillSpector version keeps the finding active until it is reviewed again. When a selected baseline or baseline output is stored inside the skill directory, SkillSpector excludes that exact file from content analysis so its suppression text cannot create findings or enter regenerated fingerprints; sibling files remain in normal scan scope.

For a local directory containing one SKILL.md or skill.md, repeat --exclude to omit selected files before content analysis:

skillspector scan ./my-skill --exclude 'tests/*' --exclude 'fixtures/*.json'

Patterns are case-sensitive globs matched against the entire relative POSIX path; * also matches /. Quote patterns so your shell does not expand them. This is an explicit caller option, not an author-controlled ignore file. Patterns matching either manifest name (SKILL.md or skill.md) are rejected, as are recursive, multi-skill, transitive, registry, and non-directory inputs. No-match patterns are still recorded in the report.

Files already outside the scan inventory, such as policy-excluded dependencies, retain their existing policy handling and are not counted as caller exclusions. Explicitly excluded inventory files remain in the coverage denominator as entirely uninspected. The report lists the applied patterns, excluded count and individual skipped paths; any matched exclusion makes coverage partial and prevents a SAFE recommendation. Use --fail-on-incomplete when partial scope should fail CI. These exclusions do not suppress findings in included files or override existing safety limits.

For the best results, configure an OpenAI-compatible LLM endpoint for semantic analysis. Pick a provider with SKILLSPECTOR_PROVIDER; hosted providers ship bundled default models, while CLI providers fall back to the local runtime's default model unless SKILLSPECTOR_MODEL is set. SkillSpector also works against local OpenAI-compatible servers (Ollama, vLLM, llama.cpp) and managed inference gateways.

gpt-6.1-sol, claude-opus-5-5, and claude-sonnet-5-5 remain opt-in through SKILLSPECTOR_MODEL or per-slot overrides. Promoting them to defaults requires live scanner acceptance covering benign input, credential disclosure, prompt injection, refusals, and incomplete responses. Record findings, completion status, latency, and usage, and compare against previous defaults where accessible.

For NVIDIA Build's z-ai/glm-5.3, SkillSpector requests high reasoning effort. Set SKILLSPECTOR_REASONING_EFFORT to override it with low, high, or max. The bundled 128,000-token context and 32,000-token output budgets are conservative application limits; they do not claim the hosted endpoint's maximum capacity.

Structured output is requested through LangChain's with_structured_output. The default is client-specific: ChatOpenAI uses json_schema, while other clients may use tool calling. Some models reject a forced tool call with HTTP 400 (tool_choice: type "tool" and "any" are not supported for this model). The anthropic and anthropic_proxy providers route those models (claude-opus-5-5, claude-sonnet-5-5, claude-fable-5-1, claude-mythos-5-1, or any registry entry with structured_output: json_schema) to the native JSON-schema response format. Bedrock has no JSON-schema output for them, so the bedrock provider leaves toolChoice at auto, asks for the tool call in the prompt, and retries a prose answer; it recognises the model from the model ID, a geo/global inference-profile ID, or a foundation-model / inference-profile ARN. An application-inference-profile ARN hides the model, so add that ARN to the registry (SKILLSPECTOR_MODEL_REGISTRY) with tool_choice: auto. The openai_compatible provider honours the same tool_choice: auto entry for endpoints that ignore both response_format and a forced tool_choice and answer in prose (for example iFlytek's spark-x2.5, which is bundled). The anthropic, anthropic_proxy, openai, and openai_compatible providers also recognise those Claude models inside gateway model IDs such as anthropic/claude-opus-5-5 or aws/anthropic/bedrock-claude-opus-5-5. Some OpenAI-compatible gateways turn a json_schema response format for Claude into a forced tool call, so the openai and openai_compatible providers bind the schema as a tool with tool_choice left at auto and retry a prose answer. SKILLSPECTOR_STRUCTURED_OUTPUT_METHOD=json_schema|function_calling overrides the method for any provider. The method precedence is SKILLSPECTOR_STRUCTURED_OUTPUT_METHOD, the provider's method hint, an analyzer preference, then the client default. TP4 prefers function_calling only for provider openai and the exact model label azure/anthropic/claude-opus-5, with reasoning/thinking controls unset; dated or suffixed labels and other providers keep their existing behavior. An explicit environment override or provider hint still wins when reasoning is configured.

# 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/

# AWS Bedrock (Claude via SigV4)
export SKILLSPECTOR_PROVIDER=bedrock
# Optional: select an AWS named profile. When unset, the standard
# boto3 credential chain (env vars, instance metadata, SSO, etc.) resolves.
# export AWS_PROFILE=my-profile
export AWS_REGION=us-west-2  # default if unset
# Default model: us.anthropic.claude-sonnet-4-6
# Override with any Bedrock model ID, cross-region inference-profile
# ID, or your own application-inference-profile ARN. Claude 5.5 is opt-in:
# us./eu. profiles keep requests in that geography, global. routes worldwide.
# export SKILLSPECTOR_MODEL=us.anthropic.claude-sonnet-5-5
skillspector scan ./my-skill/

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

# Gemini on Google Cloud (Application Default Credentials / Workload Identity)
# Prerequisites:
#   1. Google Cloud project with billing enabled.
#   2. Enable Gemini Enterprise Agent Platform / Vertex AI API: `aiplatform.googleapis.com`.
#   3. IAM permission: grant `roles/aiplatform.user` (or at minimum `aiplatform.endpoints.predict`)
#      to your user account or Kubernetes service account.
#   4. Local authentication: run `gcloud auth application-default login`.
#      Configure a quota project if needed: `gcloud auth application-default set-quota-project PROJECT_ID`.
#   5. Kubernetes / GKE: configure Workload Identity and leave GOOGLE_APPLICATION_CREDENTIALS unset
#      rather than exporting service account keys.
# Note on Data Residency:
#   The default `global` endpoint does not support data-residency requirements. While you can target
#   `us`, `eu`, or regional endpoints (e.g. `us-central1`), endpoint selection alone does not guarantee
#   data residency or in-region processing without appropriate organizational policies. Always verify
#   that your selected model is supported in your target location.
# Optional credentials:
#   GOOGLE_APPLICATION_CREDENTIALS is optional and can reference Workload or Workforce Identity Federation
#   configuration files; exporting long-lived service account keys is discouraged.
export SKILLSPECTOR_PROVIDER=gemini
export GOOGLE_CLOUD_PROJECT=my-project-id
# export GOOGLE_CLOUD_LOCATION=global  # default is global; or us, eu, or specific region (e.g. us-central1)
# Default model: gemini-3.8-flash
# export SKILLSPECTOR_MODEL=gemini-3.7-flash
skillspector scan ./my-skill/

# Local Claude CLI — no API key; uses your existing `claude auth login` session
# Requires: claude CLI installed and authenticated (claude auth login)
export SKILLSPECTOR_PROVIDER=claude_cli
# Uses the local Claude CLI runtime fallback unless SKILLSPECTOR_MODEL is set.
# export SKILLSPECTOR_MODEL=claude-sonnet-4-6
skillspector scan ./my-skill/

# Gemini (via OpenAI compatibility layer)
export SKILLSPECTOR_PROVIDER=openai
export OPENAI_API_KEY="YOUR_GEMINI_API_KEY"
export OPENAI_BASE_URL="https://generativelanguage.googleapis.com/v1beta/openai/"
export SKILLSPECTOR_MODEL=gemini-3.5-flash
skillspector scan ./my-skill/

# Local Ollama — no API key
export SKILLSPECTOR_PROVIDER=ollama
# export OLLAMA_BASE_URL=http://localhost:11434/v1  # shown default
export SKILLSPECTOR_MODEL=llama3.1:8b
skillspector scan ./my-skill/

# Azure OpenAI
export SKILLSPECTOR_PROVIDER=azure_openai
export AZURE_OPENAI_API_KEY=...
export AZURE_OPENAI_ENDPOINT=https://example.openai.azure.com/
export AZURE_OPENAI_DEPLOYMENT=my-deployment
skillspector scan ./my-skill/

# Any other OpenAI-compatible endpoint
export SKILLSPECTOR_PROVIDER=openai_compatible
export SKILLSPECTOR_COMPAT_API_KEY=...
export SKILLSPECTOR_COMPAT_BASE_URL=https://api.groq.com/openai/v1
export SKILLSPECTOR_MODEL=llama-3.1-70b-versatile
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

Run SkillSpector as a Model Context Protocol server so local MCP-capable agents (Claude Code, Codex CLI, Gemini CLI) 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.

skillspector mcp requires skillspector[mcp].

# Install, or reinstall if you already used the CLI-only path
uv tool install --force 'skillspector[mcp] @ git+https://github.com/NVIDIA/skillspector.git'

# FastMCP stdio transport for local CLI agents
skillspector mcp

# Streamable HTTP transport on a local loopback interface
skillspector mcp --transport http --host 127.0.0.1 --port 8000

The stdio transport is the current FastMCP path for local CLI agents, and the initialize hang reported in issue #199 still applies there.

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

Security — HTTP transport trust model

The HTTP transport ships without authentication. Any caller that can reach the port can invoke scan_skill. HTTP bindings are restricted to loopback IPs (127.0.0.1 or ::1); localhost binds to 127.0.0.1 without DNS resolution. Wildcard, routable and other hostname bindings are rejected.

• For remote access, put an authenticating reverse proxy (e.g. nginx + mTLS) in front of the loopback listener.
• Local paths and file:// URLs are automatically rejected over HTTP to prevent unauthenticated callers from reading arbitrary host files. Only remote Git and .zip URLs are accepted.

HTTP scans accept only credential-free https:// targets. Git scans never use the server's Git login or Git config. If a proxy or custom CA is needed, set HTTPS_PROXY, NO_PROXY or GIT_SSL_CAINFO in the server environment. Use stdio or the CLI for private repositories. Git redirects are rejected; use the current repository URL after a rename or transfer.

SkillSpector detects 71 vulnerability patterns across 17 categories:

All detected patterns are listed in the tables above.

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

 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.

Disabled provider: codex_cli remains registered but cannot run LLM analysis because its read-only sandbox allows host-file reads. Existing users should select an HTTP API provider or another supported CLI provider.

CLI providers (claude_cli, gemini_cli, opencode_cli): No API key is needed. Authentication is managed entirely by the agent CLI's own login session. SkillSpector never reads or forwards API keys when these providers are active. The subprocess is run with capabilities restricted, and untrusted skill content is delivered only via stdin.

opencode_cli currently fails closed unless the installed OpenCode version is exactly 1.18.33, the version whose configuration precedence and deny-all semantics are verified by this release.

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]

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.

skillspector scan exits with:

By default, the exit code collapses SAFE and CAUTION into 0. Use --fail-on-findings to gate on any active finding, --fail-on-incomplete to gate on incomplete coverage, --min-coverage PERCENT to gate on a coverage floor, or read the JSON recommendation field for custom policy.

--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,
    "inference_usage": [
      {
        "node": "semantic_security_discovery",
        "request_kind": "structured_output",
        "provider": "nv_inference",
        "model": "azure/anthropic/claude-opus-4-6",
        "model_source": "provider_response",
        "usage_source": "provider_response",
        "prompt_tokens": 1000,
        "completion_tokens": 100,
        "cached_tokens": 400,
        "cache_write_tokens": 50,
        "total_tokens": 1100
      }
    ]
  }
}

• 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.
• Archive-member components retain their virtual path and include outer_path, nested_path, and container provenance. Virtual paths identify in-memory members, not on-disk files; see JSON component locations.
• AE1 findings use Incomplete referenced artifact analysis. Their source location identifies the reference; evidence identifies the affected target, analyzer reasons, and available bounds. Review the target's completeness ledger when reasons_truncated is true. See referenced-artifact diagnostics and Perl help text for interpretation and corrective actions.
• metadata.inference_usage contains one sanitized record per LLM response when the provider exposes token counters. It is an empty list when usage is unavailable; SkillSpector never estimates missing tokens. Prompt totals are inclusive of cache reads and writes so downstream pricing can separate those partitions safely. model_source distinguishes an independently identified provider model from the exact requested model used when response identity is absent or ambiguous. SkillSpector does not currently send Anthropic prompt-cache controls, so its scan requests cannot select the separate 5-minute or 1-hour cache-write tiers; TTL-specific response fields are normalized defensively into the aggregate cache-write counter.
• See Inference usage telemetry for the complete provenance, cache-accounting, privacy, fail-closed ingestion, and downstream pricing contract.
• 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. Each result carries its security severity; rule descriptors carry a score only when all of their results share that severity. Mixed-severity rules remain unannotated, so consumers that read only rule-level severity cannot distinguish their result severities. Recursive scans chain each percent-encoded skill-directory URI through an absolute scan-root file URI. External dependency locations retain their source provenance instead of being rebased into the local skill directory.

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

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.

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

SkillSpector uses a two-stage detection pipeline:

• 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 analyzer-eligible files in the skill
• High recall (catches most issues)
• Moderate precision (some false positives)

OpenSSF Model Signing / DSSE bundles remain in content analysis, regardless of filename. Static and LLM analyzers inspect decoded JSON payloads, readable DER certificate/signature content, and all unknown wrapper fields. Strictly parsed binary fields are projected rather than treated as executable base64; canonical bytes remain available to YARA and fingerprints. Unsupported encodings (including environments without the optional cryptography dependency) retain raw fields and produce an explicit incomplete-analysis warning. Findings in decoded content point to the original bundle and label their projection in evidence. None of this verifies signatures, certificate chains, transparency logs, or signer identity.

• Evaluates context and intent
• Confirmed findings may gain an explanation and higher confidence, never lower
• Every deterministic finding stays in the report whether the model confirms it, disputes it, or does not address it
• Findings the model reviews but does not confirm (disputed, low-confidence, or unaddressed) are tagged llm-unconfirmed in JSON and SARIF output
• Findings whose review fails are kept without that tag; evidence.llm_review_outcome distinguishes confirmed, disagreed, low-confidence, missing, and failed in JSON and SARIF output
• Results depend on the configured model and prompt context; Stage 2 does not replace deterministic findings or guarantee a particular precision rate

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

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.

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 analyzer-eligible file contents to the configured provider. When LLM analysis is enabled (the default), file contents are sent to the active SKILLSPECTOR_PROVIDER endpoint. DSSE payloads and readable signing metadata are included. 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.

• 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

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; 31,132 were analyzed for the following rates
• Vulnerable: 26.1% of the analyzed subset contain at least one vulnerability
• High-severity: 5.2% of the analyzed subset show likely malicious intent
• Key finding: Skills with executable scripts are 2.12x more likely to be vulnerable

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']}")

Apache License 2.0 - see LICENSE for details.

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

• Issues: GitHub Issues