A structured reasoning framework that forces AI agents to decompose complex problems into logical steps for higher accuracy and verifiable conclusions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install sequential-thinking
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install sequential-thinking using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Sequential Thinking?
Sequential Thinking is a sophisticated reasoning skill designed to mitigate the tendency of LLMs to rush toward conclusions. By implementing a step-by-step decomposition process, this tool ensures that complex tasks are handled with architectural precision. It is an essential addition to any developer's toolkit of Openclaw Skills, providing a systematic way to solve sub-problems independently before synthesizing a final, verified answer.
This skill is particularly valuable for scenarios where logical consistency and depth of thought are more important than speed. It transforms a standard prompt into a multi-phase cognitive workflow that includes decomposition, independent step solving, cross-step verification, and final synthesis with an explicit confidence score.
Sequential Thinking Use Cases
- Debugging intricate code logic or identifying subtle architectural flaws in software systems.
- Conducting deep-dive research into complex environmental, economic, or scientific scenarios.
- Designing multi-layered systems such as sustainable cities or enterprise-grade cloud architectures.
- Solving multi-step logic puzzles or mathematical proofs that require high levels of consistency.
- Performing competitive analysis by evaluating technology frameworks through a structured, step-by-step lens.
How Sequential Thinking Works
- Decomposition: The skill identifies the core sub-questions and logical milestones required to answer the primary prompt.
- Step-Solving: It addresses each sub-question independently, passing context from previous steps to maintain logical continuity.
- Self-Verification: An optional verification pass checks for contradictions or inconsistencies between the findings of different steps.
- Synthesis: The conclusions from all steps are combined into a single, coherent narrative or solution.
- Confidence Scoring: A final confidence percentage is generated based on step agreement and verification pass results.
Sequential Thinking Setup
To integrate this capability into your workflow, ensure you have Python 3.10+ and a valid API key. Follow these steps to configure this within your Openclaw Skills environment:
# Set your environment variable
export OPENROUTER_API_KEY='your_api_key_here'
# Run a basic sequential reasoning task
python3 scripts/sequential_think.py "What would happen to Earth's climate if the Moon disappeared?"
# Run with self-verification enabled
python3 scripts/sequential_think.py "Analyze the impact of P vs NP" --verify
Sequential Thinking Data Schema & Taxonomy
The skill generates structured reasoning data that can be consumed by other tools or agents. When using the --json flag, the output follows this schema:
| Key | Type | Description |
|---|---|---|
steps |
Array | A list of objects containing step titles and detailed reasoning strings. |
verification |
String | A status indicating if the internal consistency check passed or failed. |
synthesis |
String | The final consolidated answer derived from all steps. |
confidence_score |
String | A qualitative and quantitative confidence rating (e.g., 90% High). |
Sequential Thinking Advanced Features
- Custom Model Selection: Use any supported model (e.g., Claude 3.5 Sonnet) via OpenRouter by using the
--modelflag. - Depth Control: Adjust the
--stepsflag to limit reasoning for simpler tasks or expand it for deep architectural reviews. - Verification Pass: Enable the
--verifyflag to force the agent to look for logical flaws in its own reasoning path. - Verbose Auditing: Utilize
--verboseto see the full intermediate reasoning of every sub-step for transparent debugging. - Automated Integration: Supports
--jsonoutput, allowing the results of this reasoning to be piped into other Openclaw Skills for further processing.
SKILL.md
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