external/trailofbits-security/skill-improver/skills/skill-improver/SKILL.md
Iteratively reviews and fixes Claude Code skill quality issues until they meet standards. Runs automated fix-review cycles using the skill-reviewer agent. Use to fix skill quality issues, improve skill descriptions, run automated skill review loops, or iteratively refine a skill. Triggers on 'fix my skill', 'improve skill quality', 'skill improvement loop'. NOT for one-time reviews—use /skill-reviewer directly.
npx skillsauth add seikaikyo/dash-skills skill-improverInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Iteratively improve a Claude Code skill using the skill-reviewer agent until it meets quality standards.
Requires the plugin-dev plugin which provides the skill-reviewer agent.
Verify it's enabled: run /plugins — plugin-dev should appear in the list. If missing, install from the Trail of Bits plugin repository.
/skill-reviewer directly insteadThese block skill loading or cause runtime failures:
These significantly degrade skill effectiveness:
These are polish items that may or may not improve the skill:
Before implementing any minor issue fix, evaluate:
Only implement minor fixes that are clearly beneficial. Skill-reviewer may produce false positives.
Use the skill-reviewer agent from the plugin-dev plugin. Request a review by asking Claude to:
Review the skill at [SKILL_PATH] using the plugin-dev:skill-reviewer agent. Provide a detailed quality assessment with issues categorized by severity.
Replace [SKILL_PATH] with the absolute path to the skill directory (e.g., /path/to/plugins/my-plugin/skills/my-skill).
Iteration 1 — skill-reviewer output:
Critical: SKILL.md:1 - Missing required 'name' field in frontmatter
Major: SKILL.md:3 - Description uses second person ("you should use")
Major: Missing "When NOT to Use" section
Minor: Line 45 is verbose
Fixes applied:
Iteration 2 — run skill-reviewer again to verify fixes:
Minor: Line 45 is verbose
Minor issue evaluation: Line 45 communicates effectively as-is. The verbosity provides useful context. Skip.
All critical/major issues resolved. Output the completion marker:
<skill-improvement-complete>
Note: The marker MUST appear in the output. Statements like "quality bar met" or "looks good" will NOT stop the loop.
CRITICAL: The stop hook ONLY checks for the explicit marker below. No other signal will terminate the loop.
Output this marker when done:
<skill-improvement-complete>
When to output the marker:
When NOT to output the marker:
The marker is the ONLY way to complete the loop. Natural language like "looks good" or "quality bar met" will NOT stop the loop.
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.