awesome-language-model-analysis: This paper list focuses on the theoretical and empirical analysis of language models, especially large

by Furyton · Agent Tool · ★ 102

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Security audit verdict: SAFE · quality 53/100

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.

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Quick Facts

Stars102
Forks1
LanguagePython
CategoryAgent Tool
LicenseCC0-1.0
Quality Score53/100
Open Issues11
Last Updated2026-07-29
Created2024-04-25
Platformspython
Est. Tokens~13k

About awesome-language-model-analysis

Awesome Language Model Analysis This paper list focuses on the theoretical analysis of language models, especially large language models (LLMs). The papers in this list investigate the learning behavior, generalization ability, and other properties of language models through formal/mathematical analysis -- proofs, provable guarantees, bounds, expressivity results, convergence analysis, and...

Compatible Skills

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  • OpenRCA — semantic(0.41)+complementary+same_lang+similar_pop+shared_platform (64%)
  • mxcp — semantic(0.35)+complementary+same_lang+similar_pop+shared_platform (62%)

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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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