skills/neuro-symbolic-reasoning/SKILL.md
Neuro-symbolic AI combining LLMs with symbolic solvers. Use when exploring neuro-symbolic approaches (ideation, no code) or implementing solver integrations (code).
npx skillsauth add sundial-org/skills neuro-symbolic-reasoningInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Detect user intent and route accordingly:
→ Ideation: "How should I...", "What are the tradeoffs...", "Design an experiment..."
→ Implementation: "Implement...", "Build...", "Write code...", "Debug..."
Small files, few files:
NL Problem → LLM Formulator → Logic Program → Symbolic Solver → Answer
↑ |
└──── Self-Refinement ←────────┘
| Logic Type | Solver | Use When | |------------|--------|----------| | First-order logic | Prover9 | Expressive reasoning, theorem proving | | Constraints/SAT | Z3 | Scheduling, planning, satisfiability | | Rule-based | Pyke | Simple propositional rules |
development
Data visualization design based on Stanford CS448B. Use for: (1) choosing chart types, (2) selecting visual encodings, (3) critiquing visualizations, (4) building D3.js visualizations, (5) designing interactions/animations, (6) choosing colors, (7) visualizing networks, (8) visualizing text. Covers Bertin, Mackinlay, Cleveland & McGill.
testing
Guidelines for creating high-quality datasets for LLM post-training (SFT/DPO/RLHF). Use when preparing data for fine-tuning, evaluating data quality, or designing data collection strategies.
development
Fine-tune LLMs using the Tinker API. Covers supervised fine-tuning, reinforcement learning, LoRA training, vision-language models, and both high-level Cookbook patterns and low-level API usage.
data-ai
Calculate training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.