windiff: Tool that allows comparing symbol, type and syscall information of Microsoft Windows binaries across different

by ergrelet · Claude Skill · ★ 396

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 66/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

claude-skillsdiffinsidernextjsntoskrnlpdbportable-executablepreviewreverse-engineeringrust

Quick Facts

Stars396
Forks20
LanguageRust
CategoryClaude Skill
LicenseGPL-3.0
Quality Score66/100
Open Issues2
Last Updated2026-11-07
Created2023-05-08
Platformsbrowser, claude-code, rust
Est. Tokens~23k

About windiff

WinDiff About WinDiff is an open-source web-based tool that allows browsing and comparing symbol, type and syscall information of Microsoft Windows binaries across different versions of the operating system. The binary database is automatically updated to include information from the latest Windows updates (including Insider Preview).

Compatible Skills

These tools work well together with windiff for enhanced workflows:

  • mitmproxy-mcp — semantic(0.17)+complementary+similar_pop+shared_platform (46%)
  • claude-preview.nvim — semantic(0.17)+complementary+rare_topics+similar_pop+shared_platform (46%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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