oil-git: A read-only Git GUI and diff viewer for macOS & Windows. 帮你看清 AI 改了什么

by oil-oil · Codex Skill · ★ 60

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

Security audit verdict: SAFE · quality 67/100

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. Full audit →

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.

ai-codingclaude-codecodexdesktop-appdiffdiff-viewergitgit-clientgit-guigit-viewer

Quick Facts

Stars60
Forks7
LanguageTypeScript
CategoryCodex Skill
LicenseMIT
Quality Score67/100
Open Issues1
Last Updated2026-10-13
Created2026-10-04
Platformsclaude-code, cli, codex, node
Est. Tokens~15k

About oil-git

· MIT · Website · Downloads · Usage · Contributing · Agent Skill · 简体中文 Oil Git is a read-only desktop viewer for local Git repositories. Open a project to inspect its current branch, working tree changes, commit history, and file diffs. Keep using Git in your editor, terminal, or agent; Oil Git follows changes made to the real repository. What you can inspect Files grouped by mer

Compatible Skills

These tools work well together with oil-git for enhanced workflows:

  • GitWand — semantic(0.44)+complementary+rare_topics+same_lang+similar_pop+shared_platform (79%)
  • recensa — semantic(0.32)+complementary+rare_topics+same_lang+similar_pop+shared_platform (66%)

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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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