A systematic framework for capturing fleeting inspirations and refining them into structured, actionable knowledge.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install thinking-spark
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 thinking-spark 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 Thinking Spark?
Thinking Spark is a specialized system designed for the rapid capture of spontaneous thoughts, ideas, and inspirations that occur during the creative or technical process. It serves as a bridge between raw, often incomplete mental sparks and polished, high-value insights that can be integrated into long-term memory or project documentation.
Built to integrate seamlessly with the Openclaw Skills ecosystem, it provides a structured JSON-based storage layer that tracks the lifecycle of an idea from its initial 'raw' state through to a 'polished' and 'archived' status. This ensures that no valuable thought is lost and that every spark has a clear path toward becoming useful knowledge.
Thinking Spark Use Cases
- Capturing immediate technical insights during a coding session.
- Recording fleeting inspirations while reading articles or research papers.
- Organizing brain-dumped ideas into structured, professional summaries.
- Linking specific thoughts to external project IDs or article references for better context.
- Maintaining a prioritized queue of concepts that require further reflection or refinement.
How Thinking Spark Works
- Capture: Users record a raw 'spark' using simple commands, which is immediately assigned a unique ID and a 'raw' status.
- Categorization: Metadata such as tags, priority levels, and source references are attached to provide context and searchability.
- Refinement: The system assists in 'polishing' the raw content, transforming fragmented notes into coherent, well-structured text.
- Integration: Polished insights can be linked to external systems like reading lists, open-source projects, or long-term memory files.
- Archival: Once the thought has been fully processed and utilized, it is moved to an archived state to keep the active workspace clean.
Thinking Spark Setup
To get started with Thinking Spark in your environment, ensure you have the collection directory initialized. You can use the provided Python scripts for atomic operations:
# Create a new thinking spark
python3 create_spark.py "Your raw idea here" "tag1,tag2"
# List all pending sparks that need refinement
python3 list_sparks.py raw
# Update and polish a specific spark by ID
python3 update_spark.py 20260310_001 refine "Your polished and structured insight content"
Thinking Spark Data Schema & Taxonomy
The system utilizes a structured JSON schema located at collection/思考火花.json to manage data integrity.
| Field | Type | Description |
|---|---|---|
| id | string | Unique identifier in YYYYMMDD_NNN format. |
| content | string | The original, raw thought recorded by the user. |
| polished_content | string | The refined and structured version of the thought. |
| status | string | Lifecycle state: raw, refining, polished, or archived. |
| source_refs | array | External references (e.g., article:ID, project:ID). |
| history | array | Audit log of changes and refinements. |
Thinking Spark Advanced Features
- Atomic Writes: Uses temporary files and rename operations to prevent JSON corruption during high-frequency updates.
- External Linkage: Sophisticated source referencing allows for a 'Read -> Think -> Refine' closed-loop workflow.
- Long-term Memory Promotion: High-priority sparks (level 4+) are flagged for automatic inclusion in MEMORY.md for persistent AI context.
- Status Tracking: Granular state management from 'raw' to 'archived' ensures a clean pipeline for Openclaw Skills users.
- Search & Filter: Powerful CLI-based filtering by tags, dates, and refinement status.
SKILL.md
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