🧩 Sequential Thinking
Structured reasoning through sequential thinking. Break complex problems into logical steps, solve each independently, verify consistency, and synthesize a final answer with a confidence score.
Why Sequential Thinking?
LLMs often rush to conclusions. This skill forces step-by-step decomposition:
- Decompose — Break the problem into discrete steps
- Solve — Address each step independently
- Verify — Check consistency between steps
- Synthesize — Combine into a final answer with confidence
Usage
# Basic sequential reasoning
python3 {baseDir}/scripts/sequential_think.py "What would happen to Earth's climate if the Moon disappeared?"
# Limit to 5 steps
python3 {baseDir}/scripts/sequential_think.py "Design a sustainable city for 1M people" --steps 5
# Enable self-verification
python3 {baseDir}/scripts/sequential_think.py "Is P=NP?" --verify
# Use a specific model
python3 {baseDir}/scripts/sequential_think.py "Explain quantum computing" --model anthropic/claude-sonnet-4
# JSON output
python3 {baseDir}/scripts/sequential_think.py "Compare React vs Vue" --json
# Verbose mode (show all intermediate reasoning)
python3 {baseDir}/scripts/sequential_think.py "Solve this logic puzzle..." --verbose
Flags
| Flag | Default | Description |
|---|---|---|
--steps | 7 | Maximum number of reasoning steps |
--verify | off | Enable self-verification pass |
--model | anthropic/claude-sonnet-4 | Model to use |
--json | off | Output structured JSON |
--verbose | off | Show full intermediate reasoning |
--temperature | 0.3 | Temperature for reasoning (lower = more focused) |
Output Format
🧩 Sequential Thinking: "Your question here"
══════════════════════════════════════════
Step 1/5: [Step Title]
→ [Reasoning and conclusion for this step]
Step 2/5: [Step Title]
→ [Reasoning and conclusion for this step]
...
✅ Verification: [Pass/Fail — consistency notes]
📋 Synthesis:
[Final combined answer]
🎯 Confidence: 85% (High)
How It Works
- Decomposition prompt asks the model to identify the key sub-questions
- Step-solving prompts address each sub-question with context from prior steps
- Verification prompt (optional) checks for contradictions between steps
- Synthesis prompt combines all step conclusions into a coherent answer
- Confidence scoring based on step agreement, verification results, and hedging language
Credits
Built by M. Abidi | agxntsix.ai YouTube | GitHub Part of the AgxntSix Skill Suite for OpenClaw agents.
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