No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by LAMDASZ-ML · Agent Tool · ★ 271
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Awesome-LLM-Reasoning-with-NeSy safe to install? View the security audit →
Towards Improving Reasoning & Planning Capabilities of LLMs with Neuro-Symbolic Learning ✨✨ Curated collection of papers and resources on latest advances on improving reasoning and planning abilities of LLM/MLLMs with neuro-symbolic learning 🗂️ Table of Contents Awesome Tutorials & Workshops & Talks Awesome Survey Basic Neuro-Symbolic Frameworks Symbolic to LLM Symbolic Generation, LLM Imitation LLM Formalize, Symbolic Augment LLM to Symbolic Symbolic Solver Aided Methods Program Aided Methods Tool Aided Methods Search Augmented Methods LLM plus Symbolic <a href="#symbolic-fo
| Stars | 271 |
| Forks | 18 |
| Category | Agent Tool |
| Quality Score | 45.7322845481372/100 |
| Open Issues | 1 |
| Last Updated | 2025-06-19 |
| Created | 2024-10-31 |
| Est. Tokens | ~105k |
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Awesome-LLM-Reasoning-with-NeSy is ✨✨Latest Advances on Neuro-Symbolic Learning in the era of Large Language Models. It is categorized as a Agent Tool with 271 GitHub stars.
You can find installation instructions and usage details in the Awesome-LLM-Reasoning-with-NeSy GitHub repository at github.com/LAMDASZ-ML/Awesome-LLM-Reasoning-with-NeSy. The project has 271 stars and 18 forks, indicating an active community.
The top alternatives to Awesome-LLM-Reasoning-with-NeSy on Agent Skills Hub include free-ai-resources-x, Awesome-LLM-Papers-Comprehensive-Topics, synalinks-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: