DeepThink is a structured multi-stage reasoning skill for AI agents that enforces systematic analysis, strategic planning, and rigorous execution validation before taking action.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install deepthink-mode
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 deepthink-mode 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 DeepThink?
DeepThink brings an advanced depth-first reasoning framework to AI coding environments, heavily inspired by Claude Extended Thinking and Deep Reasoning protocols. It acts as an elite cognitive loop that helps autonomous entities avoid premature execution. By enforcing a deliberate mental sequence—Analyze, Plan, Execute, and Review—the skill transforms raw agent capabilities into structured engineering outcomes.
This framework is particularly valuable for complex engineering tasks such as system architecture redesign, advanced debugging, deep code reviews, and tactical trade-off optimization. Instead of impulsively generating code, an agent integrated with this Openclaw Skills plugin systematically addresses hidden constraints, maps potential technical blind spots, and formulates fail-safe deployment pathways.
DeepThink Use Cases
- Complex Technical Problem Solving: Handling architecture designs, legacy refactoring, and multi-variable logic errors.
- Risk-Mitigation & Trade-off Assessment: Evaluating architectural or operational decisions that impact infrastructure stability.
- Comprehensive Code Reviews: Auditing codebases for logic holes, security vulnerabilities, or performance optimization pathways.
- First-Time Task Exploration: Guiding agent execution safely through unfamiliar programming domains or newly introduced toolchains.
How DeepThink Works
- Analyze: The skill maps the core technical problem, identifies hidden operational assumptions, evaluates constraints, and surfaces potential blind spots.
- Plan: It selects an optimal execution strategy, breaks down the implementation details into actionable units, and builds fallback options.
- Execute: The agent processes the steps sequentially, validating runtime outcomes continuously and making micro-adjustments as roadblocks appear.
- Review: The module executes an evaluation cycle to verify if results match expected technical conditions and logs operational insights for continuous improvement.
DeepThink Setup
Ensure python3 is available on your runtime platform. Integrate DeepThink directly via the command line:
# Run deep analysis on a targeted database query issue
python3 deepthink.py --mode analyze "How to optimize this database query?"
# Map an extensive software architectural restructuring plan
python3 deepthink.py --mode plan "Refactor user authentication module"
# Generate a complete reasoning chain and save it to a markdown document
python3 deepthink.py --mode full "Design a microservices architecture" --output chain.md
DeepThink Data Schema & Taxonomy
DeepThink manages execution output using semantic markdown structures and explicit parameter flags. It outputs structural context as follows:
| Execution Mode | Parameter | Output Target | Primary Content Focus |
|---|---|---|---|
analyze |
--mode analyze |
Stdout / Log | Problem essence, boundaries, hidden constraints, and multi-angle risk surfaces. |
plan |
--mode plan |
Stdout / Log | Strategic execution pathways, modular steps, feasibility checks, and fallbacks. |
full |
--mode full |
Configurable File (--output) |
End-to-end reasoning chains, linking analysis directly to runtime architecture steps. |
DeepThink Advanced Features
- Targeted Execution Modes: Switch easily between dedicated analysis, rapid planning, or comprehensive reasoning chains depending on problem complexity.
- Reasoning Chain Persistence: Pipe complete cognitive graphs directly into markdown files (
--output chain.md) for auditing, review, or CI/CD logging. - Contextual Self-Correction: Implements an active feedback loop that dynamically adjusts its script plan when validation phases return unexpected results.
SKILL.md
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