A powerful extraction skill that converts messy conversational history or raw text into structured Q&A pairs and documented user preferences.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install text2qa
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 text2qa 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 text2qa?
The text2qa skill is a specialized utility designed to bridge the gap between unstructured dialogue and organized knowledge. By analyzing any text source—including active chat sessions, uploaded documents, or transcripts—it identifies the core questions and answers that define the context. This skill is part of the broader Openclaw Skills library, enabling developers and researchers to instantly turn a rambling conversation into a structured knowledge base.
Beyond simple Q&A, text2qa excels at identifying selection preferences and technical constraints. It distinguishes between direct statements and inferred choices, allowing users to capture their specific requirements for formatting, persona, or tool usage without manual documentation.
text2qa Use Cases
- Converting a brainstorming session into a clean FAQ or documentation set.
- Generating study flashcards or quizzes from long-form articles or textbooks.
- Capturing technical constraints and user preferences during complex development tasks using Openclaw Skills.
- Summarizing support chat history into reusable knowledge base articles.
- Documenting decisions made during a multi-turn AI coding session.
How text2qa Works
- The AI scans the entire input source, starting from the very first message in a chat or the beginning of a document.
- It identifies explicit questions and synthesizes implicit Q&A from informative sections of the text.
- The skill extracts specific preferences (format, content, tools, tone) and classifies them as direct or inferred.
- It filters out conversational filler and meta-talk to ensure high signal-to-noise in the final output.
- The data is formatted into two distinct sections: a Q&A list and a detailed Selection Preferences table.
text2qa Setup
To activate the text2qa capability within your agent environment, ensure the skill is registered in your local Openclaw Skills directory. You can invoke it using natural language prompts like "extract Q&A" or via a CLI command.
# Example: Running the skill against a local markdown file
openclaw-agent run text2qa --input documentation.md --output-format markdown
text2qa Data Schema & Taxonomy
The skill organizes extracted data into a standardized Markdown structure for easy portability. The data schema is outlined below:
| Data Component | Format | Description |
|---|---|---|
| Q&A Pairs | Markdown List | Cleaned and rephrased question-answer exchanges. |
| Selection Preferences | Markdown Table | Includes the preference, the specific constraint, and the source (direct/inferred). |
| Topic Clusters | Sub-headings | Logical groupings of related questions for long chat sessions. |
| Metadata Summary | Text | A summary count of all found pairs and preferences. |
text2qa Advanced Features
- Implicit Question Detection: Identifies factual information and frames it into a Q&A format even when no question was asked.
- Preference Taxonomy: Categorizes constraints into format, content, tool, persona, and domain types.
- Multi-turn Synthesis: Aggregates information spread across multiple messages into a single, comprehensive answer.
- Export Automation: Offers one-click export to standalone .md files for inclusion in Openclaw Skills repositories.
- Smart Filtering: Automatically excludes small talk and administrative messages from the final knowledge output.
SKILL.md
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