Openclaw Skills adapts AI explanations for first-principles learners, starting at step zero with clear reasoning, analogies, and guess validation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install sinan-thinking-adapter-v4
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 sinan-thinking-adapter-v4 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 Deep Learner Thinking Adapter?
Deep Learner Thinking Adapter
Openclaw Skills provides a response strategy for explaining technical or conceptual topics to learners who want the full picture before the answer. It is designed to start from step zero, explain why something exists, and build understanding in a sequence that respects curiosity, caution, and incomplete background knowledge.
This skill is especially valuable when a user asks multiple nested questions, offers several hypotheses, or challenges vague explanations. Openclaw Skills helps the agent respond with precise structure, explicit hierarchy, simple language, and accurate analogies so the learner can trust the explanation and follow the logic without being overloaded.
Deep Learner Thinking Adapter Use Cases
- Explaining a new concept to a beginner who asks "why does this exist?" before "how does it work?"
- Responding to learners who propose multiple guesses and need each one evaluated explicitly
- Reframing an explanation when the previous answer was too abstract, too compressed, or started in the middle
- Teaching workflows, systems, or tools where the order of operations matters
- Handling questions with multiple sub-questions that need separate headings and direct answers
- Presenting safe, non-dismissive guidance that validates partial understanding before correcting mistakes
- Adapting explanations to a learner who prefers short sentences, no jargon, and one idea per paragraph
How Deep Learner Thinking Adapter Works
- Inspect the learner’s message and identify whether they are asking for first principles, challenging an analogy, testing possibilities, or requesting a reset.
- Validate the guesses first if the learner offered multiple theories, clearly marking which are correct, partially correct, or incorrect.
- Reconstruct the full context from step zero, explaining the broader system, purpose, and origin before introducing the first technical action.
- Use one accurate analogy per concept to anchor understanding, and explicitly state the limits of the analogy when needed.
- Separate sub-questions into headings so each question gets a direct, non-merged answer.
- Clarify authority and hierarchy by explaining what takes priority, what can be overridden, and what remains safe alongside existing settings.
- End with a summary table that compresses the explanation into a simple question-to-answer format for fast review.
Deep Learner Thinking Adapter Setup
- Copy the skill into your Openclaw Skills setup so the agent can use it as a response-shaping behavior.
- Register or reference the skill in the agent’s skill directory or configuration used by your Openclaw Skills runtime.
- Ensure the agent is instructed to apply this skill when the user is a beginner, asks for first-principles reasoning, or requests a reframe.
- If your environment supports skill metadata, keep the YAML frontmatter aligned with the skill name and version.
# Example: place the skill file in your skills directory
mkdir -p ~/.openclaw/skills
cp sinan-thinking-adapter-v4/SKILL.md ~/.openclaw/skills/
# Example: verify the file is available to the agent runtime
ls -la ~/.openclaw/skills/
- Test the behavior with a prompt that includes multiple guesses, a request for "step zero," or a request to explain the "why" before the "how."
- No additional dependencies are required from the skill itself; the value comes from the agent consistently following the explanation rules.
Deep Learner Thinking Adapter Data Schema & Taxonomy
The skill is lightweight and metadata-driven. It does not define external APIs or generated output files, but it does rely on a clear internal structure for response formatting.
| Element | Purpose | Notes |
|---|---|---|
name |
Skill identifier | sinan-thinking-adapter-v4 |
version |
Release/version marker | 1.0.1 |
description |
One-line behavior summary | Explains step-zero, analogy-based adaptation |
| Core rules | Behavioral constraints | Start from zero, validate guesses, avoid jargon |
| Language rules | Style defaults | Short sentences, define terms, one idea per paragraph |
| Evolution Log | Optional memory record | Update only when explicitly asked |
Response taxonomy
- Validation layer: confirm which user guesses are right first.
- Context layer: explain why the concept exists and what problem it solves.
- Analogy layer: map the concept to one real-world comparison.
- Hierarchy layer: state priorities and override rules clearly.
- Summary layer: close with a compact table of question vs. simple answer.
Operational constraints
- Do not assume prior vocabulary.
- Do not auto-write to the Evolution Log.
- Do not skip origin, purpose, or conflict-resolution details when relevant.
Deep Learner Thinking Adapter Advanced Features
- Step-zero teaching mode for first-principles explanations
- Multi-guess validation that evaluates each user theory individually
- Analogy enforcement with accuracy checks and explicit limitation handling
- Multi-question decomposition with numbered headings
- Hierarchy and override clarification for conflicting rules or system interactions
- Adaptive reset behavior when the learner says the explanation is not working
- Optional evolution tracking, only when explicitly requested
- Built-in summary table pattern for fast comprehension and review
SKILL.md
Loading
Related Openclaw Skills
Schedulala Social Publishing Skill
Openclaw Skills for Schedulala automate social post creation, validation, scheduling, publishing, and monitoring across 12 platforms.
SandBase MCP Setup and Verification
SandBase Openclaw Skills helps you safely connect, configure, and verify the SandBase MCP server in supported AI clients without exposing secrets.
SQLGuard — Authorize Before Mutate
SQLGuard Authorize Before Mutate is an Openclaw Skills safety gate that requires a verified permission receipt before any production SQL write is executed.
WorkBuddy Skin Injection Skill
Openclaw Skills for WorkBuddy lets you inject, update, and roll back the app skin entirely from the command line.
persistent-sshfs
persistent-sshfs is a Bash-based Openclaw Skills utility that retries SSHFS mounts until they connect, then relies on sshfs reconnect for transient drop recovery.
supervisor-config-gen
supervisor-config-gen is a zero-flag bash utility that generates a Supervisor program config from the current app directory using a required run.sh entrypoint.
Featured*
