vulnerability-data-processing
Process vulnerability scan results from Trivy JSON output, extract HIGH/CRITICAL severity vulnerabilities with complete metadata (CVE, CVSS scores, fix versions, references). Use this skill whenever you need to transform raw vulnerability data into structured format with proper field mapping and CVSS score prioritization.
- License 许可
- MIT
- 最近更新
- 2026-10-02
- Stars
- ★ 93
- Forks
- 8
01 前言 Front Matter
- name
- vulnerability-data-processing
- description
- Process vulnerability scan results from Trivy JSON output, extract HIGH/CRITICAL severity vulnerabilities with complete metadata (CVE, CVSS scores, fix versions, references). Use this skill whenever you need to transform raw vulnerability data into structured format with proper field mapping and CVSS score prioritization.
03 SKILL.md 正文
Vulnerability Data Processing and Extraction
Overview
This skill handles transforming raw Trivy JSON vulnerability scan results into structured data for reporting. It handles field extraction, CVSS score selection, and data validation.
Input Format
Trivy JSON output with structure:
{
"Results": [
{
"Target": "package-lock.json",
"Type": "npm",
"Vulnerabilities": [
{
"VulnerabilityID": "CVE-2021-12345",
"PkgName": "package-name",
"InstalledVersion": "1.0.0",
"FixedVersion": "1.0.1",
"Severity": "HIGH",
"Title": "Vulnerability description",
"Description": "Detailed description",
"CVSS": {
"nvd": {
"V3Vector": "CVSS:3.1/AV:N/AC:L/...",
"V3Score": 7.5
},
"ghsa": {
"V3Score": 7.4
}
},
"References": ["https://..."]
}
]
}
]
}
Processing Steps
1. Parse Trivy JSON Output
Read the JSON file and validate structure:
- Check that
Resultsarray exists - Filter to
Results[].Vulnerabilities[]entries - Skip if
Vulnerabilitiesis empty or null
2. Filter by Severity
Keep only Severity == "HIGH" or Severity == "CRITICAL".
3. Extract CVSS Score
CVSS scores are nested under the CVSS object with multiple sources. Extract in priority:
def get_cvss_score(vuln):
cvss = vuln.get("CVSS", {})
# Priority: NVD > GHSA > RedHat
if "nvd" in cvss and "V3Score" in cvss["nvd"]:
return cvss["nvd"]["V3Score"]
if "ghsa" in cvss and "V3Score" in cvss["ghsa"]:
return cvss["ghsa"]["V3Score"]
if "redhat" in cvss and "V3Score" in cvss["redhat"]:
return cvss["redhat"]["V3Score"]
return None # Mark as N/A if missing
4. Extract Fixed Version
fixed = vuln.get("FixedVersion", "")
fixed_version = fixed if fixed else "N/A"
5. Extract Reference URL
Take the first valid HTTP/HTTPS URL from the References array:
references = vuln.get("References", [])
url = next((ref for ref in references if ref.startswith("http")), "N/A")
6. Build Output Record
Map each vulnerability to:
record = {
"Package": vuln["PkgName"],
"Version": vuln["InstalledVersion"],
"CVE_ID": vuln["VulnerabilityID"],
"Severity": vuln["Severity"],
"CVSS_Score": cvss_score if cvss_score else "N/A",
"Fixed_Version": fixed_version,
"Title": vuln.get("Title", vuln.get("Description", "N/A")),
"Url": url
}
7. Deduplication
If the same CVE appears multiple times (different packages), keep all entries. If the same package+CVE appears multiple times, keep first occurrence.
Validation Checks
- CVE_ID format: Should match
CVE-\d{4}-\d{4,}pattern - Severity: Must be "HIGH" or "CRITICAL"
- CVSS_Score: Should be numeric 0-10 or "N/A"
- Version fields: Non-empty strings
- Title length: Use first 200 chars if exceeds limit
Python Implementation Pattern
import json
def process_vulnerabilities(json_file):
with open(json_file, 'r') as f:
data = json.load(f)
records = []
for result in data.get("Results", []):
for vuln in result.get("Vulnerabilities", []):
if vuln["Severity"] not in ["HIGH", "CRITICAL"]:
continue
# Extract fields (use helper functions above)
record = {
"Package": vuln["PkgName"],
"Version": vuln["InstalledVersion"],
"CVE_ID": vuln["VulnerabilityID"],
"Severity": vuln["Severity"],
"CVSS_Score": get_cvss_score(vuln),
"Fixed_Version": vuln.get("FixedVersion") or "N/A",
"Title": vuln.get("Title", ""),
"Url": get_first_url(vuln.get("References", []))
}
records.append(record)
return records
Common Field Transformations
| Source Field | Target Field | Transformation |
|---|---|---|
| VulnerabilityID | CVE_ID | Use as-is |
| PkgName | Package | Use as-is |
| InstalledVersion | Version | Use as-is |
| FixedVersion | Fixed_Version | Empty → "N/A" |
| Severity | Severity | Use as-is (pre-filtered) |
| CVSS.*.V3Score | CVSS_Score | Select first available, fallback "N/A" |
| Title/Description | Title | Prefer Title, fallback to Description |
| References[0] | Url | Extract first HTTP URL, fallback "N/A" |
Error Handling
- Invalid JSON: Log error and exit — file must be valid JSON
- Missing fields: Use "N/A" as fallback
- Empty results: Return empty list (no vulnerabilities found)
- Malformed severity: Log warning and skip record
Output
Returns list of dictionaries ready for CSV export:
[
{
"Package": "express",
"Version": "4.17.1",
"CVE_ID": "CVE-2022-12345",
"Severity": "HIGH",
"CVSS_Score": 7.5,
"Fixed_Version": "4.17.2",
"Title": "Vulnerability in express",
"Url": "https://nvd.nist.gov/vuln/detail/CVE-2022-12345"
},
...
]
Next Steps
Pass the processed records to the CSV export skill for final report generation.
04 同仓库更多 Skills
object_counter
Count occurrences of an object in the image using computer vision algorithm.
travel-planning-workflow
Invoke this skill when performing travel planning. It defines a structured workflow for generating itineraries. Following this workflow is essential to ensure the final plan satisfies user requirements.
bundled-dataset-search-tool
invoke this skill when you need to perform database search for travel planning. This skill provides some useful pre-packaged tools to look up accommodations, attractions, cities, driving distance, flights, and restaurants from the bundled dataset.
glm-output
Output-processing guidance for GLM tasks. Especially useful after glm-basics and glm-calibration when you need verifier-matching metrics from output.nc and a final /root/metrics.json.
glm-calibration
Calibration guidance for GLM tasks. Often most effective after glm-basics has clarified the setup; glm-output is the companion skill for exact final metric computation.
glm-basics
A good starting point for GLM calibration tasks. Use to inspect glm3.nml, confirm how GLM runs, and identify the relevant files before moving on to calibration and output evaluation.