426 API Endpoints Detail 1dc02130

API Endpoints Detail

Full request/response documentation for every Skill Scanner API endpoint. For a high-level overview, see the API Server page.

[!TIP] Quick reference For a compact table of all endpoints and Pydantic models, see the auto-generated API Endpoint Reference.

Root

GET /

Returns service metadata and links:

{
  "service": "Skill Scanner API",
  "version": "<installed-package-version>",
  "docs": "/docs",
  "health": "/health"
}

Health Check

GET /health

Returns server status and available analyzers.

Response:

{
  "status": "healthy",
  "version": "<installed-package-version>",
  "analyzers_available": [
    "static_analyzer",
    "bytecode_analyzer",
    "pipeline_analyzer",
    "behavioral_analyzer",
    "llm_analyzer",
    "virustotal_analyzer",
    "trigger_analyzer",
    "meta_analyzer",
    "aidefense_analyzer"
  ]
}

[!NOTE] Analyzer naming The /health and /analyzers endpoints use suffixed names (bytecode_analyzer, pipeline_analyzer) from a static list in the API router. The core analyzers report shorter names (bytecode, pipeline) in analyzers_used within scan results. Both refer to the same analyzers.

For LLM/meta auth, Bedrock models can use AWS credentials/IAM when configured with a bedrock/... model.

Scan Single Skill

POST /scan
Content-Type: application/json

{
  "skill_directory": "/path/to/skill",
  "policy": "balanced",
  "custom_rules": null,
  "use_behavioral": false,
  "use_llm": false,
  "llm_provider": "anthropic",
  "use_virustotal": false,
  "vt_upload_files": false,
  "use_trigger": false,
  "enable_meta": false,
  "llm_consensus_runs": 1,
  "use_aidefense": false,
  "aidefense_api_url": null
}

Request Headers:

Request Body Parameters:

For Bedrock, Vertex, Azure, Gemini, and other LiteLLM backends, configure SKILL_SCANNER_LLM_MODEL/provider environment variables instead of relying on the llm_provider shortcut.

Response:

{
  "scan_id": "uuid",
  "skill_name": "calculator",
  "is_safe": true,
  "max_severity": "SAFE",
  "findings_count": 0,
  "scan_duration_seconds": 0.15,
  "timestamp": "2025-01-01T12:00:00",
  "findings": []
}

Upload and Scan Skill

Primary use case: Upload a skill package as a ZIP file for scanning. This is the main workflow for CI/CD and web interfaces.

POST /scan-upload
Content-Type: multipart/form-data

file: skill.zip
policy: balanced
use_llm: false
llm_provider: anthropic

Uploads a ZIP file containing a skill package and scans it. The ZIP file is extracted to a temporary directory, scanned, and then cleaned up.

/scan-upload accepts the same optional scan flags as /scan, but as multipart form fields (not query params).

Form Fields:

API keys (X-VirusTotal-Key, X-AIDefense-Key) are passed as request headers, same as /scan.

Response: Same as /scan

Batch Scan (Async)

POST /scan-batch
Content-Type: application/json

{
  "skills_directory": "/path/to/skills",
  "policy": "balanced",
  "custom_rules": null,
  "recursive": false,
  "check_overlap": false,
  "use_behavioral": false,
  "use_llm": false,
  "llm_provider": "anthropic",
  "use_virustotal": false,
  "vt_upload_files": false,
  "use_trigger": false,
  "enable_meta": false,
  "llm_consensus_runs": 1,
  "use_aidefense": false,
  "aidefense_api_url": null
}

/scan-batch supports the same optional analyzer fields and request headers (X-VirusTotal-Key, X-AIDefense-Key) as /scan, plus:

Response:

{
  "scan_id": "uuid",
  "status": "processing",
  "message": "Batch scan started. Use GET /scan-batch/{scan_id} to check status."
}

Get Batch Scan Results

GET /scan-batch/{scan_id}

Response (Processing):

{
  "scan_id": "uuid",
  "status": "processing",
  "started_at": "2025-01-01T12:00:00"
}

Response (Completed):

{
  "scan_id": "uuid",
  "status": "completed",
  "started_at": "2025-01-01T12:00:00",
  "completed_at": "2025-01-01T12:05:30",
  "result": {
    "summary": {...},
    "results": [...]
  }
}

List Analyzers

GET /analyzers

Response:

{
  "analyzers": [
    {
      "name": "static_analyzer",
      "description": "Pattern-based detection using YAML and YARA rules",
      "available": true,
      "rules_count": "90+"
    },
    {
      "name": "bytecode_analyzer",
      "description": "Python bytecode integrity verification against source",
      "available": true
    },
    {
      "name": "pipeline_analyzer",
      "description": "Command pipeline taint analysis for data exfiltration",
      "available": true
    },
    {
      "name": "behavioral_analyzer",
      "description": "Static dataflow analysis for Python files",
      "available": true
    },
    {
      "name": "llm_analyzer",
      "description": "Semantic analysis using LLM as a judge",
      "available": true,
      "providers": ["anthropic", "openai", "azure", "bedrock", "gemini"]
    },
    {
      "name": "aidefense_analyzer",
      "description": "Cisco AI Defense cloud-based threat detection",
      "available": true,
      "requires_api_key": true
    },
    {
      "name": "virustotal_analyzer",
      "description": "Hash-based malware detection for binary files via VirusTotal",
      "available": true,
      "requires_api_key": true
    },
    {
      "name": "trigger_analyzer",
      "description": "Trigger specificity analysis for overly generic descriptions",
      "available": true
    },
    {
      "name": "meta_analyzer",
      "description": "Second-pass LLM analysis for false positive filtering",
      "available": true,
      "requires": "2+ analyzers, LLM API key"
    }
  ]
}

Error Response Format

{
  "detail": "Error message describing what went wrong"
}