PLAN() is an Openclaw Skills planning agent that transforms vague goals into structured, executable task plans with dependencies, timing, resources, and completion criteria.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install zayn-plan
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install zayn-plan using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
PLAN() is a planning decomposition skill built to convert ambiguous user goals into a clear, actionable execution plan. It forces the input into a parameter state table, validates evidence, and only then produces a structured output that includes goals, key paths, staged tasks, dependencies, resources, constraints, and completion standards.
This Openclaw Skills workflow is designed for situations where a user has core material and wants a reliable decision or action path, not a brainstorm or wishlist. By enforcing minimum operating conditions, explicit boundary checks, and human judgment when information is incomplete, PLAN() helps keep plans realistic, verifiable, and execution-ready.
PLAN() is a skill-based workflow rather than a package with a traditional install command. To use it effectively in Openclaw Skills, follow this configuration flow:
# Example input preparation for PLAN()
cat > plan-input.txt << 'EOF'
Target: ...
Current state: ...
Deadline: ...
Scope: ...
Evidence: ...
Optional context: resources, constraints, priority, dependencies, owner, completion criteria
EOF
# Then pass the structured request into your Openclaw Skills workflow
openclaw skill run zayn-plan --input plan-input.txt
PLAN() organizes information into a parameter-first planning schema.
| Field group | Required fields | Optional fields | Purpose |
|---|---|---|---|
| Core parameters | Target, current state, deadline, scope | — | Establishes the planning boundary |
| Context parameters | — | Resources, constraints, priority, dependencies, owner, completion criteria | Adds execution realism and accountability |
| Evidence layer | At least one reliable source | Additional supporting materials | Prevents assumption-driven planning |
| Validation state | Fully matched, partially matched, missing, conflicting, pending verification | — | Tracks input completeness before formal analysis |
Loading
PRACTICE() is an Openclaw Skills workflow that converts ideas, articles, or methods into minimal, verifiable, low-cost experiments with clear stop conditions.

READ() transforms reading materials into structured insight and action, turning content into reasons, questions, evidence, next steps, and review points.

Openclaw Skills converts dimensioned images into confirmation-ready SVG blueprints and accurate STL models for fabrication.

Openclaw Skills for researching and writing a scholarly Simplified Chinese analysis of Ode to the Goddess of Luo River, with citations and annotations.

LEARN() turns vague learning intent into a validated, actionable learning path with goals, stages, practice tasks, and verification.

REQUEST() turns vague internal asks into structured, evidence-backed requests with clear scope, deadlines, and decision-ready context.








































