A sophisticated research framework for AI agents to perform multi-source web investigation, fact-checking, and structured synthesis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install deepresearchwork
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 deepresearchwork 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 Research?
The Deep Research skill is an advanced framework designed for Openclaw Skills that enables AI agents to move beyond simple search queries into the realm of professional investigation. It provides a systematic methodology for gathering, analyzing, and verifying information from across the web, ensuring that the resulting insights are grounded in diverse and authoritative sources.
By leveraging an iterative approach, this skill allows agents to refine their search parameters based on initial findings, cross-reference conflicting claims, and assess the credibility of various information outlets. It is the ideal solution for anyone needing high-fidelity reports where accuracy and depth are paramount within the Openclaw Skills ecosystem.
Deep Research Use Cases
- Academic research including literature reviews and rigorous source verification.
- Business intelligence for market analysis, competitive landscape mapping, and technology trend tracking.
- High-stakes fact-checking to identify potential misinformation and evaluate source objectivity.
- Comprehensive personal learning projects that require deep exploration of complex technical or historical topics.
How Deep Research Works
- Topic Analysis: The agent defines the research scope, identifies core concepts, and establishes specific questions to be answered.
- Broad Investigation: It utilizes web search tools to map the landscape, identify major stakeholders, and gather diverse perspectives.
- Detailed Extraction: The agent fetches full content from prioritized sources to extract specific data points while maintaining clear attribution.
- Synthesis & Validation: Findings are cross-referenced across multiple sources to identify patterns, resolve contradictions, and assess bias.
- Structured Reporting: The lifecycle concludes with the generation of a comprehensive report including an executive summary, evidence-backed findings, and credibility scores.
Deep Research Setup
To deploy the Deep Research framework within your agent environment, ensure your search and retrieval tools are correctly configured.
# Install the research skill via the CLI
openclaw install deep-research
Verify that your configuration file includes the necessary API keys for web search and that the browser component is active to handle complex JavaScript-rendered sites.
Deep Research Data Schema & Taxonomy
The skill organizes its findings into a standardized taxonomy to ensure data integrity across various Openclaw Skills research tasks.
| Component | Description |
|---|---|
| Executive Summary | A concise overview of the most critical discoveries. |
| Methodology | Documentation of the tools, search queries, and logic used. |
| Key Findings | Discovery points organized by thematic relevance. |
| Source Matrix | A detailed list of citations with associated authority scores. |
| Limitations | Identification of gaps in current knowledge or potential data conflicts. |
Deep Research Advanced Features
- Iterative Cycle Management: Automatically runs multiple research cycles to drill deeper into specific sub-topics discovered during initial phases.
- Bias Assessment: Evaluates source objectivity and flags potential conflicts of interest or sensationalist language.
- Temporal Relevance Tracking: Prioritizes recent data for fast-moving sectors while maintaining historical context for trend analysis.
- Memory Integration: Seamlessly utilizes memory_get and memory_search within Openclaw Skills to build upon previously gathered data points across different sessions.
SKILL.md
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