Chain-of-Thought Skill Skill

Chain-of-Thought Skill

Bonded to: chain-of-thought-agent

Quick Start

Skill("custom-plugin-prompt-engineering:chain-of-thought")

Parameter Schema

parameters:
  cot_type:
    type: enum
    values: [zero_shot, few_shot, self_consistency, tree_of_thought]
    default: zero_shot

  verbosity:
    type: enum
    values: [minimal, standard, detailed]
    default: standard

  verification:
    type: boolean
    default: true
    description: Include self-verification step

CoT Variants

| Variant | Description | Accuracy | Token Cost | |---------|-------------|----------|------------| | Zero-shot CoT | "Let's think step by step" | Good | Low | | Few-shot CoT | Examples with reasoning | Better | Medium | | Self-consistency | Multiple paths, vote | Best | High | | Tree-of-Thought | Branch and evaluate | Best | Highest |

Core Patterns

1. Zero-Shot Chain-of-Thought

[Problem statement]

Let's think step by step.

Trigger phrases:

"Let's think step by step"
"Let's work through this systematically"
"Let me break this down"
"Let's solve this step by step"

2. Few-Shot Chain-of-Thought

Q: [Example problem 1]
A: Let's think step by step.
   Step 1: [reasoning]
   Step 2: [reasoning]
   Step 3: [reasoning]
   Therefore, the answer is: [answer]

Q: [Example problem 2]
A: Let's think step by step.
   Step 1: [reasoning]
   Step 2: [reasoning]
   Therefore, the answer is: [answer]

Q: [Actual problem]
A: Let's think step by step.

3. Self-Consistency

process:
  1_generate: "Generate N reasoning paths (temperature > 0)"
  2_extract: "Extract final answer from each path"
  3_vote: "Select answer with highest frequency"
  4_confidence: "Confidence = frequency / N"

parameters:
  n_paths: 5
  temperature: 0.7
  selection: majority_vote

4. Plan-and-Solve

Q: [Complex problem]

A: Let's first understand the problem and devise a plan.

## Understanding
[Restate the problem and identify key elements]

## Plan
1. [Step 1 description]
2. [Step 2 description]
3. [Step 3 description]

## Execution
Step 1: [Execute and explain]
Step 2: [Execute and explain]
Step 3: [Execute and explain]

## Verification
[Check if solution satisfies requirements]

## Final Answer
[Conclusion]

Domain-Specific Triggers

triggers_by_domain:
  mathematics:
    - "Let's solve this step by step, showing all work"
    - "First, let's identify what we know and what we need to find"

  coding:
    - "Let's trace through the logic step by step"
    - "Let me walk through this algorithm"

  logic:
    - "Let's analyze each premise systematically"
    - "Let me reason through this logically"

  analysis:
    - "Let's break this down into components"
    - "Let me examine this from multiple angles"

Validation

validation_checklist:
  logical_flow:
    - [ ] Each step follows from previous
    - [ ] No logical gaps
    - [ ] No circular reasoning

  completeness:
    - [ ] All sub-problems addressed
    - [ ] Edge cases considered
    - [ ] Assumptions stated

  conclusion:
    - [ ] Follows from final step
    - [ ] Directly answers question
    - [ ] Confidence indicated

Troubleshooting

| Issue | Cause | Solution | |-------|-------|----------| | Skips steps | Steps too obvious | Add intermediate steps | | Wrong answer | Error in early step | Add verification at each step | | Inconsistent | Single path variance | Use self-consistency | | Too verbose | Over-explanation | Use minimal verbosity | | Token overflow | Too many steps | Consolidate related steps |

Integration

integrates_with:
  - few-shot-prompting: Examples with reasoning
  - prompt-design: Base structure
  - self-reflection: Verify reasoning

combination_patterns:
  cot_plus_few_shot: "Examples showing reasoning steps"
  cot_plus_self_consistency: "Multiple reasoning paths"
  cot_plus_verification: "Reasoning with self-check"

References

See references/GUIDE.md for advanced CoT techniques. See assets/config.yaml for configuration options.

Agent Skills: Chain-of-Thought Skill

Skill Files