PRACTICE() is an Openclaw Skills workflow that converts ideas, articles, or methods into minimal, verifiable, low-cost experiments with clear stop conditions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install zayn-practice
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-practice 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 PRACTICE()?
PRACTICE() is designed to help users transform promising ideas, articles, and methods into a smallest-possible practice plan that can be tested quickly and evaluated objectively. In Openclaw Skills, this means taking abstract intent and converting it into a structured, evidence-backed, human-reviewable action model.
The skill emphasizes low-cost experimentation, explicit parameter validation, minimum operating conditions, and clear success and stop criteria. It is useful when the goal is not to design a full system, but to produce a minimal practice that can be executed, verified, and refined with minimal risk.
PRACTICE() Use Cases
- Converting a promising concept into a small, testable action plan
- Turning an article or method into a practical experiment
- Structuring user-provided materials into decision-ready output
- Defining minimal actions, evidence requirements, and stop conditions
- Validating whether an idea is actionable before investing more resources
- Preventing over-design by keeping the scope to the smallest verifiable practice
How PRACTICE() Works
- Parse the user's input and map it into a parameter state table.
- Check whether the required inputs are present: idea/method, problem to solve, current scenario, and practice goal.
- Collect optional parameters such as time budget, resources, constraints, success criteria, stop conditions, and review timing.
- Evaluate evidence priority and require at least one reliable source; do not treat feelings, assumptions, or AI guesses as facts.
- Validate minimum operating conditions, missing parameters, conflicts, and verification readiness.
- If information is insufficient, request user clarification instead of generating a complete plan.
- When conditions are met, produce a minimal practice output with goal, smallest action, resources, success standard, stop condition, and review node.
- Ensure the result is executable, verifiable, and bounded by explicit human judgment boundaries.
PRACTICE() Setup
This skill is primarily a prompt-level workflow and does not require traditional package installation.
- Add the skill to your Openclaw Skills environment.
- Ensure the agent routes user input through the PRACTICE() parameter analysis flow.
- Require explicit collection of core inputs before formal analysis begins.
- Enforce evidence-first validation and human review for ambiguous or incomplete cases.
# Example: place the skill in the agent's skills directory
mkdir -p ~/.openclaw/skills
cp -r zayn-practice ~/.openclaw/skills/
# Verify the skill file is available
ls ~/.openclaw/skills/zayn-practice
- Confirm the agent respects the required output structure: parameter completeness, state table, minimal action, resources, success criteria, stop condition, and review nodes.
- Test with an incomplete prompt to verify clarification behavior before using it in production.
PRACTICE() Data Schema & Taxonomy
PRACTICE() organizes content into a strict parameter-driven schema rather than freeform notes.
| Section | Purpose | Typical Fields |
|---|---|---|
| Basic Info | Identifies the skill and version | Skill ID, Display Name, Project, Author Prefix, Category, Version, Status |
| Required Parameters | Core inputs needed before formal analysis | Idea or method, problem to solve, current scenario, practice goal |
| Optional Parameters | Context that shapes the practice | Time budget, resources, constraints, success criteria, stop conditions, review timing |
| Parameter State Table | Tracks readiness before analysis | 已命中, 部分命中, 缺失, 冲突, 待验证 |
| Output Structure | Standardized result format | Completeness conclusion, state table, practice goal, minimal action, resources, success criteria, stop condition, review node |
| Status Metadata | Tracks lifecycle state | Version 0.1.0, Draft for testing |
Validation rules in the schema:
- At least one reliable piece of evidence must be present.
- Missing required parameters must be requested, not auto-filled.
- Conflicts must be preserved with source notes and verification prompts.
- Unverified information must never be stated as fact.
- Analysis only proceeds when minimum operating conditions and risk boundaries are satisfied.
PRACTICE() Advanced Features
- Parameter state table validation with explicit readiness states
- Evidence-first analysis that rejects assumptions and unsupported claims
- Human-in-the-loop boundaries to prevent task expansion beyond scope
- Minimal-action planning that avoids overbuilding full systems
- Stop-condition design for bounded experiments and safe iteration
- Conflict preservation, so disagreements are surfaced instead of silently resolved
- Clarification gating when core inputs are incomplete
- Structured review nodes for post-experiment reflection and refinement
SKILL.md
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