A professional framework for mastering ChatGPT through structured prompt packets, intelligent surface routing, and rigorous output quality assurance.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install chatgpt
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 chatgpt 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 ChatGPT?
The ChatGPT skill for Openclaw Skills is designed to elevate how developers and researchers interact with the ChatGPT web interface. Rather than relying on simple, one-off queries, this skill introduces a disciplined methodology for building high-signal prompt packets that include context, source material, and specific deliverables. It empowers users to manage complex, long-running work by organizing it into Projects and maintaining local records of successful workflows.
By prioritizing surface routing, this skill ensures that you always use the most appropriate environment—such as Temporary Chat for sensitive tasks or Projects for durable context—preventing memory contamination and generic output. This approach focuses on maximizing the value of ChatGPT as a research and planning partner while maintaining high standards for accuracy and evidence-based results within the Openclaw Skills ecosystem.
ChatGPT Use Cases
- Creating complex prompt packets for technical writing, analysis, and strategic planning.
- Structuring long-term research within ChatGPT Projects to maintain a consistent decision log.
- Routing sensitive or one-off tasks to Temporary Chat to ensure memory hygiene.
- Implementing multi-pass workflows to refine AI outputs from initial discovery to final critique.
- Troubleshooting and recovering from model drift when AI outputs become repetitive or bland.
How ChatGPT Works
- The agent analyzes the task to determine the optimal ChatGPT surface, such as a standard chat, Project, or custom GPT.
- A comprehensive prompt packet is constructed, defining the goal, constraints, and the required output shape.
- The workflow is split into logical passes, such as outlining and drafting, to ensure depth and clarity.
- Local memory in the chatgpt directory is updated with project logs and reusable prompt patterns that yielded high-quality results.
- The final output undergoes a QA process to verify evidence, check for hallucinations, and ensure the brief was fully met.
ChatGPT Setup
To initialize this skill for Openclaw Skills, follow these steps to set up your local workspace:
- Create the necessary local directory structure:
mkdir -p ~/chatgpt/
- Review the activation rules and align your privacy boundaries by accessing the setup guide:
# Review the setup configuration
cat setup.md
- Initialize your default workflow preferences in the memory file to define how the agent should interact with your ChatGPT sessions.
ChatGPT Data Schema & Taxonomy
This skill maintains a local taxonomy within ~/chatgpt/ to ensure your workflows remain structured and reusable across Openclaw Skills:
| File | Description |
|---|---|
memory.md |
Primary storage for activation preferences and default workflow constraints. |
workflows.md |
A repository of prompt packet patterns that have successfully delivered high-signal results. |
projects.md |
A log of active ChatGPT projects, including file references and decision history. |
gpts.md |
Documentation for custom GPT roles, their specific guardrails, and known limitations. |
qa.md |
A record of output failures and hallucination catches used to refine future prompting strategies. |
ChatGPT Advanced Features
- Intelligent Surface Routing: Dynamically chooses between Projects, Temporary Chats, and Custom Instructions based on task sensitivity.
- Multi-Pass Iteration: Supports structured workflows that move from discovery and outlining to drafting and final critique.
- Evidence-Based Requirements: Forces the model to explicitly label assumptions, confirmed facts, and missing information.
- Drift Recovery Protocols: Standardized steps to diagnose and fix model performance issues like blandness or repetitive output.
- Local Knowledge Base: Maintains a local source of truth for your AI workflows, separate from OpenAI's cloud-based memory.
SKILL.md
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