by LAMDASZ-ML · Agent Tool · ★ 271
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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.
| Stars | 271 |
| Forks | 18 |
| Category | Agent Tool |
| Quality Score | 46/100 |
| Open Issues | 1 |
| Last Updated | 2025-06-19 |
| Created | 2024-10-31 |
| Est. Tokens | ~105k |
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...
These tools work well together with Awesome-LLM-Reasoning-with-NeSy for enhanced workflows:
Looking for a Awesome-LLM-Reasoning-with-NeSy alternative? If you're comparing Awesome-LLM-Reasoning-with-NeSy with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
🌟 A curated collection of free, high quality AI tools 🤖, APIs 🔗, datasets 📊, and learning resources 📚 cov
Awesome LLM Papers and repos on very comprehensive topics.
Coding Agents skills for Synalinks OSS
Open, source-backed catalog of computer-use agents: reviewed papers, GUI models, datasets, benchmarks, tools,
[NeurIPS 2024] ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
[Up-to-date] A curated list of resources on graph-empowered agents and agent-facilitated graph learning (Graph
Explore other popular agent tool tools:
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: