LLM-Academic-Writing: A public repository going to study how we can leverage LLMs as tools to improve our academic writing

by dixiyao · Agent Tool · ★ 143

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.

academicacademic-writingagentllmwriting-assistant

Quick Facts

Stars143
Forks5
LanguageTypeScript
CategoryAgent Tool
LicenseApache-2.0
Quality Score67/100
Last Updated2026-05-24
Created2024-04-24
Platformsnode
Est. Tokens~61k

About LLM-Academic-Writing

LLM Academic Writing Leveraging Large Language Models to supercharge your academic writing — from automated reviews to grammar polish. Featured: AIReviewer AIReviewer — Project Page A local, PDF-first agentic workspace for reviewing academic papers.

Compatible Skills

These tools work well together with LLM-Academic-Writing for enhanced workflows:

  • scholar-search-mcp — semantic(0.45)+complementary+rare_topics+similar_pop (60%)
  • claude-skills-journalism — semantic(0.48)+complementary+rare_topics+similar_pop (56%)
  • SidonieAssistant — semantic(0.25)+complementary+same_lang+similar_pop+shared_platform (54%)
  • writing-agent — semantic(0.35)+complementary+rare_topics+similar_pop+shared_platform (52%)

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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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