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ai-reasoning

Identify planning tasks and apply DSPy reasoning strategies with structured plans.

11|1|Updated Feb 8, 2026
Search Tags:#workflow#planning#ai#reasoning#self-discovery#multi-step#dspy
One-click install
npx skills add https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-reasoning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-reasoning
Source: https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills/tree/main/skills/ai-reasoning
Command: npx skills add https://github.com/lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-reasoning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps AI systems tackle tasks that require planning, multi-step reasoning, and structured problem-solving, bridging the gap between simple prompts and reliable outcomes.

Core Features & Use Cases

  • ChainOfThought: provides step-by-step reasoning traces to diagnose and improve answers.
  • ProgramOfThought: lets the AI generate and run code for computations, data manipulation, and date handling.
  • MultiChainComparison: evaluates multiple reasoning approaches and selects the best solution.
  • Self-Discovery: enables dynamic strategy selection and planning for complex problems.

Quick Start

Ask the AI to break down a hard problem, plan a multi-step approach, and execute a structured reasoning process.

Frequently Asked Questions about ai-reasoning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I use structured AI reasoning to plan a multi-step workflow?▼

Structured AI reasoning plans a multi-step workflow by identifying complex tasks and applying DSPy strategies like ChainOfThought and Self-Discovery to generate step-by-step traces and structured execution plans.

What is the best way to apply multi-step reasoning for complex problem-solving?▼

The best way to apply multi-step reasoning is using strategies like MultiChainComparison to evaluate multiple approaches and Self-Discovery for dynamic strategy selection, ensuring reliable outcomes for complex problems.

How does ChainOfThought improve AI planning for hard problems?▼

ChainOfThought improves AI planning by providing step-by-step reasoning traces that diagnose intermediate steps, helping evaluate reasoning quality and optimize the overall approach for hard problems.

Can I use AI reasoning to generate and run code for data manipulation?▼

Yes, you can use the ProgramOfThought reasoning pattern to let AI generate and run code specifically for computations, data manipulation, and date handling within a structured problem-solving workflow.

When do I need guardrails for evaluating AI reasoning quality?▼

You need guardrails for evaluating AI reasoning quality when tackling complex tasks that require multi-step planning, ensuring the selected reasoning patterns produce reliable and accurate structured outcomes.

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