name: sequential-thinking description: Structured reasoning through sequential thinking — break complex problems into steps, solve each independently, verify consistency, synthesize conclusions with confidence scoring. Use for complex analysis, debugging, and multi-step reasoning. homepage: https://www.agxntsix.ai license: MIT compatibility: Python 3.10+, OpenRouter API key metadata: {"openclaw": {"emoji": "\ud83e\udde9", "requires": {"env": ["OPENROUTER_API_KEY"]}, "primaryEnv": "OPENROUTER_API_KEY", "homepage": "https://www.agxntsix.ai"}}

🧩 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:

1. Decompose — Break the problem into discrete steps
2. Solve — Address each step independently
3. Verify — Check consistency between steps
4. 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

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

1. Decomposition prompt asks the model to identify the key sub-questions
2. Step-solving prompts address each sub-question with context from prior steps
3. Verification prompt (optional) checks for contradictions between steps
4. Synthesis prompt combines all step conclusions into a coherent answer
5. 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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