DocMason: Repo-native agent that turns your complex office files into a local LLM knowledge base and your second brain

by JetXu-LLM · Codex Skill · ★ 151

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

Security audit verdict: SAFE · quality 61/100

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.

agentscodexdeepresearchknowledge-baserepo-native-agent-app

Quick Facts

Stars151
Forks37
LanguagePython
CategoryCodex Skill
LicenseApache-2.0
Quality Score61/100
Open Issues7
Last Updated2026-11-02
Created2026-03-21
Platformscodex, python
Est. Tokens~16k

About DocMason

DocMason A repo-native agent app for deep research over private work files. The repo is the app. Codex is the runtime. Build a local, evidence-first knowledge base with provenance. Already paying for ChatGPT? Codex for macOS is included in your plan. Turn that unused capacity into a local Second Brain. Watch the ▶️ video demo to see how DocMason performs deep rese

Compatible Skills

These tools work well together with DocMason for enhanced workflows:

  • obsidian-second-brain — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (57%)
  • LLM-Wiki — semantic(0.28)+complementary+same_lang+similar_pop+shared_platform (55%)

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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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