Experience Distiller intelligently routes completed work into specific knowledge layers to ensure long-term project intelligence and reusability.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install experience-distiller
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 experience-distiller 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 Experience Distiller?
Experience Distiller is a specialized framework designed to prevent knowledge rot by capturing and categorizing insights immediately after a task is completed. Instead of allowing valuable lessons to be lost in chat histories, this tool analyzes finished work—such as bug fixes, integrations, or workflow setups—and determines the most effective storage location within the Openclaw Skills architecture.
By differentiating between transient logs and reusable tactical lessons, Experience Distiller ensures that your AI agents become smarter with every interaction. It transforms raw project noise into structured organizational intelligence, making it easier to scale operations and automate repeated tasks through high-quality documentation.
Experience Distiller Use Cases
- Capturing tactical lessons after resolving a complex technical debt or bug fix.
- Converting a successful manual deployment process into a canonical playbook.
- Routing daily progress and evidence into chronological logs for audit trails.
- Identifying opportunities to package repeated logic into new Openclaw Skills.
- Responding to user triggers like "save this experience" or "distill this workflow."
How Experience Distiller Works
- The agent parses the finished task or result to identify the core value and reusable components.
- It consults the decision-rules.md file to determine the optimal knowledge layer for the content.
- The data is separated into dated evidence, action-level lessons, or workflow changes.
- A recommendation is generated specifying the target route (e.g., daily-log, experience-bank, or skill).
- Upon confirmation, the agent executes the file writes to the specified directory using standardized templates.
Experience Distiller Setup
To integrate Experience Distiller into your environment, ensure your workspace follows the Openclaw Skills structure. Use the following commands to initialize your reference files:
mkdir -p references memory/experience-bank/entries playbooks skills
touch references/decision-rules.md references/template.md references/examples.md
Configure your AI agent to prioritize these reference files during post-task evaluations.
Experience Distiller Data Schema & Taxonomy
Experience Distiller organizes data according to the standard Openclaw Skills taxonomy:
| Knowledge Layer | Directory Path | Content Type |
|---|---|---|
| Daily Logs | memory/YYYY-MM-DD.md |
Dated facts, raw evidence, and event logs. |
| Experience Bank | memory/experience-bank/entries/ |
Trigger-action-failure reusable tactical lessons. |
| Playbooks | playbooks/ |
Multi-step canonical workflows and procedures. |
| Skills | skills/ |
Reusable capability packages and modular code units. |
Experience Distiller Advanced Features
- Multi-layer routing: Simultaneously update logs and create new playbooks from a single task output.
- Confidence-weighted recommendations: Provides a certainty score for the suggested knowledge route.
- Pattern recognition: Uses bundled examples to identify common task types like cron setups or API integrations.
- Template-driven generation: Ensures consistent formatting across all Openclaw Skills documentation.
SKILL.md
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