READ() transforms reading materials into structured insight and action, turning content into reasons, questions, evidence, next steps, and review points.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install zayn-read
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 zayn-read 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 READ() - Reading to Action Transformation?
READ() is one of the Openclaw Skills designed to convert articles, books, and saved content into a decision-ready analysis format.
Instead of producing a generic summary, Openclaw Skills with READ() extracts why the content matters, what problem it addresses, which evidence supports it, and what minimal action can be taken next. It is built for situations where the user wants usable judgment, not just passive notes.
READ() - Reading to Action Transformation Use Cases
- When you need to analyze a reading source into a structured decision framework.
- When a saved article or book should be converted into actionable notes.
- When you want to identify the collection reason, core question, key viewpoints, evidence, and minimal next action.
- When a user provides a target goal and expects the reading material to be mapped to that goal.
- When content must be reviewed later with clear checkpoints rather than being stored as an unread summary.
- When the source is partially incomplete and the skill should request missing inputs before analysis.
- When you need Openclaw Skills to distinguish viewpoints from evidence and avoid unsupported conclusions.
How READ() - Reading to Action Transformation Works
- Accept the reading source, collection reason, and the problem the user wants to solve.
- Build a parameter state table and verify whether required inputs are present.
- Check the minimum operating conditions: core object, analysis goal, and at least one reliable evidence item.
- If key information is missing, request user completion instead of inventing conclusions.
- If parameters conflict, preserve the original sources and flag the conflict for validation.
- Once the minimum conditions are met, perform structured analysis.
- Separate content theme, collection reason, corresponding issue, viewpoints, evidence strength, minimal action, and review node.
- Produce the final output in the required structure, ensuring suggestions remain verifiable and do not exceed the source material.
READ() - Reading to Action Transformation Setup
READ() is a skill definition rather than a runtime library, so setup is primarily about installing the skill into your Openclaw Skills workspace and using the expected input shape.
- Add the skill to your skills directory or skill registry.
- Confirm the skill metadata is present:
cat SKILL.md
- Ensure the agent can route reading-analysis requests to zayn-read.
- Provide user input with the required fields:
- reading material
- collection reason
- problem to solve
- Recommended optional inputs:
- key viewpoints
- evidence
- practice cost
- actions
- validation method
- review timing
- When information is incomplete, prompt for missing parameters before requesting a full analysis.
- Do not bypass the skill's boundary rules; let Openclaw Skills handle only reading-to-action transformation, not unrelated tasks.
Example invocation pattern:
# Pseudocode for agent routing
openclaw-skill run zayn-read --input "reading material + collection reason + problem to solve"
READ() - Reading to Action Transformation Data Schema & Taxonomy
READ() uses a parameter-driven analysis schema with explicit required fields, optional fields, and a controlled output structure.
Input parameter groups
| Group | Fields | Notes |
|---|---|---|
| Required | reading material | Core source to analyze |
| Required | collection reason | Why the material was saved |
| Required | problem to solve | The target issue or decision context |
| Optional | key viewpoints | Main interpretations or perspectives |
| Optional | evidence | Supporting facts or references |
| Optional | practice cost | Effort, time, or risk required to act |
| Optional | actions | Candidate next steps |
| Optional | validation method | How to verify the conclusion or action |
| Optional | review timing | When to revisit the material |
Parameter state taxonomy
The skill requires a parameter status table before formal analysis. Valid states are:
- matched
- partially matched
- missing
- conflicting
- pending verification
Output structure
| Output Section | Purpose |
|---|---|
| parameter completeness conclusion | States whether analysis can proceed |
| parameter state table | Shows input coverage and gaps |
| content theme | Identifies the main topic |
| collection reason | Explains why it was saved |
| corresponding problem | Maps the source to a real issue |
| key viewpoints | Separates interpretations from facts |
| evidence strength | Rates the reliability of support |
| minimal action | Defines the smallest executable next step |
| review node | Sets a follow-up checkpoint |
Boundary rules
Openclaw Skills using READ() must not:
- convert every viewpoint into an action
- treat subjective impressions as facts
- auto-fill missing parameters
- silently resolve conflicts
- expand the task beyond reading transformation
READ() - Reading to Action Transformation Advanced Features
- Strict parameter-gating logic that blocks premature analysis until minimum conditions are met.
- Evidence-first workflow that prevents assumptions from being written as facts.
- Conflict preservation behavior, keeping source discrepancies visible for validation.
- Missing-field prompting to force high-quality user input instead of fabricated completeness.
- Minimal-action extraction that turns reading into an executable next step.
- Review-node planning for long-term recall and follow-up.
- Clear separation of viewpoints, evidence, and judgment, making Openclaw Skills suitable for high-signal knowledge work.
SKILL.md
Loading
Related Openclaw Skills
Image to 3D Part STL Builder
Openclaw Skills converts dimensioned images into confirmation-ready SVG blueprints and accurate STL models for fabrication.
Luoshenfu Literary Research
Openclaw Skills for researching and writing a scholarly Simplified Chinese analysis of Ode to the Goddess of Luo River, with citations and annotations.
Zhiyanzhai Research Article Generator
Openclaw Skills for generating a long-form, citation-backed Simplified Chinese research article on the identity of Zhiyanzhai in Dream of the Red Chamber.
find-cargo-for-vessel
Openclaw Skills for vessel cargo matching finds domestic or international cargo boards by current port, destination port, and ship capacity.
PRACTICE()
PRACTICE() is an Openclaw Skills workflow that converts ideas, articles, or methods into minimal, verifiable, low-cost experiments with clear stop conditions.
PLAN() Planning Decomposition Skill
PLAN() is an Openclaw Skills planning agent that transforms vague goals into structured, executable task plans with dependencies, timing, resources, and completion criteria.
Featured*
