A standardized framework for discovering, evaluating, and automating high-quality information source pools to build reusable research artifacts.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install source-research
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 source-research 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 Source Research?
The Source Research skill is designed to create a persistent information ecosystem within Openclaw Skills. It enables AI agents and developers to systematically discover new source pools, evaluate their long-term value, and define the most efficient methods for data acquisition and filtering. By moving beyond one-off searches, this skill builds a reusable framework where every research effort contributes to a growing body of knowledge.
At its core, this skill provides a structured approach to managing information flows. It ensures that valuable discoveries are recorded, acquisition methods are documented, and low-quality sources are permanently flagged. This allows Openclaw Skills to maintain a high standard of information hygiene while scaling research capabilities through automation and engineering.
Source Research Use Cases
- Creating and maintaining high-quality source pools for niche technical domains.
- Evaluating whether a new blog, social account, or database is worth ongoing investment within Openclaw Skills.
- Standardizing how information is captured and filtered from disparate web sources.
- Preserving rejection decisions to ensure future agents do not waste resources re-evaluating low-value pools.
- Transitioning manual data collection tasks into automated engineering workflows.
How Source Research Works
- Discovery: Identify and immediately record new potential information pools under the source-pools directory.
- Evaluation: Research and document the methods required to acquire and filter data from the pool.
- Implementation: Produce high-quality source lists and capture valuable information based on defined filtering rules.
- Automation: For pools that demonstrate repeated value, move from manual collection to automated monitoring using the programs directory.
- Persistence: Save all results, artifacts, and rejection notes locally to ensure continuous learning for Openclaw Skills across different sessions.
Source Research Setup
To begin using the Source Research framework, you must initialize the canonical workspace directory. Use the provided initialization script to set up the necessary structure for Openclaw Skills:
python <skill-dir>/scripts/init_source_research.py [workspace-root]
This command creates the .source-research/ directory and its sub-directories for pools, acquisition rules, and high-quality results.
Source Research Data Schema & Taxonomy
The skill utilizes a strict directory-based storage contract within the .source-research/ workspace to organize Openclaw Skills artifacts:
| Directory | Purpose |
|---|---|
source-pools/ |
Metadata and status (observed, worth research, rejected) for each pool. |
acquisition/ |
Documentation of rules and methods for extracting raw data. |
filtering/ |
Rules and logic used to identify high-quality content within a pool. |
high-quality-sources/ |
Curated lists of verified and valuable information sources. |
rejections/ |
Historical records of why specific pools were deemed not worth investment. |
programs/ |
Scripts, automation assets, and engineering designs for scaling research. |
Source Research Advanced Features
- Permanent rejection tracking to prevent redundant analysis by future agents using Openclaw Skills.
- Multi-layer result architecture that separates raw source pools from high-quality filtered insights.
- Support for both manual information capture and transition to full-scale automation via dedicated program directories.
- Standardized artifact storage that ensures research results exist as code and markdown files rather than just chat history.
- Scalable engineering-first design for moving from simple information discovery to automated monitoring systems.
SKILL.md
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