Which symbolic solvers are supported by this skill?

The skill provides guidance and implementation patterns for Z3 (Constraint/SAT), Prover9 (First-order logic), and Pyke (Rule-based reasoning).

How does the 'Self-Refinement' loop work?

When a logic program fails in a symbolic solver, the skill helps Claude analyze the error, refine the logic formulation, and re-attempt the solution until a valid answer is reached.

What is the primary benefit of neuro-symbolic reasoning in Claude Code?

It allows Claude to move beyond probabilistic text generation to perform rigorous, mathematically sound reasoning by offloading logic tasks to dedicated symbolic solvers.

Can I use this skill for high-level AI research without writing code?

Yes, the skill includes an 'Ideation' mode specifically designed for discussing tradeoffs, designing experiments, and exploring neuro-symbolic architectures without creating files.

Neuro-Symbolic Reasoning

Name: Neuro-Symbolic Reasoning
Author: sundial-org

bysundial-org

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144

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Data Science & ML

Integrates large language models with symbolic logic solvers to perform complex reasoning, theorem proving, and constraint satisfaction tasks.

The Neuro-Symbolic Reasoning skill bridges the gap between the intuitive pattern recognition of LLMs and the rigorous logic of symbolic solvers. It enables Claude to tackle complex problems by formulating natural language into logic programs, which are then processed by specialized solvers like Z3, Prover9, or Pyke. This skill is ideal for tasks requiring high precision, such as scheduling, formal verification, and multi-step logical deduction, offering both high-level architectural ideation and hands-on implementation of hybrid AI systems.

Key Features

01Self-refining pipeline that iterates on logic programs based on solver feedback

02Dual-mode operation for both theoretical ideation and practical code implementation

03Pre-configured support for industry-standard solvers including Z3, Prover9, and Pyke

04Automated solver selection across First-order logic, SAT/Constraints, and Rule-based systems

05Optimized file creation policy for clean, modular logic programming scripts

06144 GitHub stars

Use Cases

01Developing hybrid AI agents that combine neural flexibility with symbolic reliability

02Solving complex constraint satisfaction problems like automated scheduling or resource allocation

03Designing and debugging formal theorem proving systems and logic-based experiments

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add sundial-org/skills neuro-symbolic-reasoning

For use in Claude.ai and ChatGPT

Download Skill