external/anthropic-cybersecurity-skills/skills/implementing-secrets-scanning-in-ci-cd/SKILL.md
Integrate gitleaks and trufflehog into CI/CD pipelines to detect leaked secrets before deployment
npx skillsauth add seikaikyo/dash-skills implementing-secrets-scanning-in-ci-cdInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
This skill covers implementing automated secrets scanning in CI/CD pipelines using gitleaks and trufflehog. It enables security teams to detect API keys, tokens, passwords, and other credentials that have been accidentally committed to source code repositories, providing a CI gate that blocks deployments containing high-severity findings.
Gitleaks scans git repositories and directories for hardcoded secrets using regex patterns and entropy analysis. TruffleHog performs filesystem and git history scans with optional secret verification against live services. Together they provide comprehensive coverage for secrets detection.
Install scanning tools: Install gitleaks via package manager or binary download. Install trufflehog via brew install trufflehog or download from GitHub releases.
Configure gitleaks: Create a .gitleaks.toml configuration file in the repository root to define custom rules, allowlists, and path exclusions. Use --config flag to point to custom configs.
Run gitleaks directory scan: Execute gitleaks dir --source . --report-format json --report-path gitleaks-report.json to scan the working directory and generate a JSON report.
Run trufflehog filesystem scan: Execute trufflehog filesystem /path/to/repo --json > trufflehog-report.json to scan files and output JSON findings to a report file.
Parse and filter findings: Use the agent script to parse both JSON reports, filter findings by severity (critical, high, medium, low), and determine whether the CI pipeline should pass or fail.
Integrate into CI pipeline: Add the scanning step to your GitHub Actions workflow, GitLab CI config, or Jenkins pipeline as a pre-deployment gate. Use --exit-code flag in gitleaks to control pipeline behavior.
Configure pre-commit hooks: Set up gitleaks as a pre-commit hook using gitleaks protect --staged to catch secrets before they are committed.
Review and triage findings: Examine the JSON output for false positives, add legitimate entries to .gitleaksignore, and rotate any confirmed leaked credentials immediately.
The agent script produces a JSON report containing:
{
"scan_summary": {
"tool": "both",
"total_findings": 3,
"critical": 1,
"high": 1,
"medium": 1,
"low": 0,
"ci_gate": "FAIL",
"fail_reason": "Found 1 critical and 1 high severity findings"
},
"findings": [...]
}
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.