external/trailofbits-security/supply-chain-risk-auditor/skills/supply-chain-risk-auditor/SKILL.md
Identifies dependencies at heightened risk of exploitation or takeover. Use when assessing supply chain attack surface, evaluating dependency health, or scoping security engagements.
npx skillsauth add seikaikyo/dash-skills supply-chain-risk-auditorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Activates when the user says "audit this project's dependencies".
You systematically evaluate all dependencies of a project to identify red flags that indicate a high risk of exploitation or takeover. You generate a summary report noting these issues.
A dependency is considered high-risk if it features any of the following risk factors:
sindresorhus or Drew Devault, the risk is lessened but not eliminated. Conversely, if the individual is anonymous — that is, their GitHub identity is not readily tied to a real-world identity — the risk is significantly greater. Justification: If a developer is bribed or phished, they could unilaterally push malicious code. Consider the left-pad incident..github/SECURITY.md, CONTRIBUTING.md, README.md, etc., or separately on the project's website (if one exists). Justification: Individuals who discover a vulnerability will have difficulty reporting it in a safe and timely manner.Ensure that the gh tool is available before continuing. Ask the user to install if it is not found.
You achieve your purpose by:
.supply-chain-risk-auditor directory for your workspace
results.md report file based on results-template.md in this directorygh tool to query the exact data. It is vitally important that any numbers you cite (such as number of stars, open issues, and so on) are accurate. You may round numbers of issues and stars using ~ notation, e.g. "~4000 stars".results.md, clearly noting your reason for flagging it as high-risk. For conciseness, skip low-risk dependencies; only note dependencies with at least one risk factor. Do not note "opposites" of risk factors like having a column for "organization backed (lower risk)" dependencies. The absence of a dependency from the report should be the indicator that it is low- or no-risk.NOTE: Do not add sections beyond those noted in results-template.md.
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.