# Symbolic Logic Reasoning

> Symbolic Logic Reasoning

- Skill: `siddhu1716/symbolic-logic-reasoning` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add siddhu1716/symbolic-logic-reasoning`
- Raw SKILL.md: https://api.skillmd.com/api/skills/siddhu1716/symbolic-logic-reasoning/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: siddhu1716 (https://skillmd.com/u/siddhu1716)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/siddhu1716/symbolic-logic-reasoning

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# Symbolic Logic Reasoning

## When to use this skill
Solve Symbolic Logic Reasoning (SLR) tasks, specifically formulating a Prolog rule to classify eastbound vs. westbound trains based on their composition.

## Approach
1. **Analyze Train Composition**: Review the ground facts (like `has_car`, `car_color`, `car_len`, `has_wall`) for positive (eastbound) and negative (westbound) examples.
2. **Find Mappings**: Locate a property or combination of properties that is true for all eastbound trains and false for all westbound trains.
3. **Formulate Prolog Rule**: Formulate the rule as `eastbound(T) :- Body.` (e.g. `eastbound(Train) :- has_car(Train, Car), car_len(Car, short).`).
4. **Minimize Body Literals**: Make the rule body as short and general as possible without sacrificing accuracy.

## Known constraints
- The rule must perfectly separate positive and negative examples.
- Must use only predefined predicates and constants.

## Examples
### Good rule pattern
`eastbound(Train):- has_car(Train, Car1), car_color(Car1, yellow).`