A high-precision research framework for AI agents to conduct structured secondary research, market landscape scans, and evidence-based synthesis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install desk-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 desk-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 Desk Research Skill?
The Desk Research skill is a professional-grade workflow designed for Openclaw Skills that transforms AI agents into rigorous research analysts. It moves beyond simple web searches by enforcing a strict methodology of source triangulation, reliability scoring (A/B/C tiers), and insight synthesis. By utilizing this skill, users can ensure their AI outputs are backed by primary official sources and reputable secondary analysis, minimizing hallucinations and maximizing decision-utility.
This Openclaw Skills component is essential for anyone needing evidence-based summaries, competitor landscape scans, or policy research that requires a clear distinction between hard facts and reasoned interpretations. It enforces a deep-thinking process that includes a mandatory deepening loop to challenge conclusions and identify data gaps before the final output is delivered.
Desk Research Skill Use Cases
- Market landscape scans and industry trend analysis to identify emerging opportunities.
- Competitor benchmarking and product teardowns for strategic planning.
- Regulatory policy research and government filing synthesis for compliance or investment.
- Startup due diligence including business model and growth metric verification.
- Evidence-based white paper drafting and insight triangulation for thought leadership.
How Desk Research Skill Works
- Initialize the workflow by loading methodology guardrails from local reference files.
- Define a strict research brief including the core question, scope, and specific sub-questions.
- Execute a prioritized source plan, targeting official primary sources first, followed by credible secondary analysis.
- Gather high-signal evidence, extracting claims with associated dates and direct source links.
- Score sources (A, B, or C) to manage reliability and resolve conflicting claims using a recency bias.
- Perform a mandatory deepening loop (Coverage and Decision challenges) to challenge conclusions.
- Generate a 9-part structured report featuring evidence tables, confidence tags, and falsification conditions.
Desk Research Skill Setup
To integrate this skill into your workflow, ensure the necessary reference files are present in your project directory.
# Create the references directory for your Openclaw Skills
mkdir -p references
# Initialize the required methodology and template files
touch references/methodology.md references/deep-writing-patterns.md references/quality-checklist.md references/output-template.md
Once the files are prepared, point your AI agent to the desk-research.md skill file to begin the structured research process.
Desk Research Skill Data Schema & Taxonomy
The skill organizes research data into a specific taxonomy to ensure traceability and professional standards:
| Category | Description |
|---|---|
| Source Tiers | A (Official/Primary), B (Credible Secondary), C (Weak/Indicative) |
| Metadata | Date, Link, Source Name, Reliability Score, Metric Anchors |
| Output Sections | Core Questions, Verdict, Summary, Findings, Evidence Table, Confidence, Risks |
| Synthesis | Clear separation between well-supported Facts and reasoned Interpretations |
Desk Research Skill Advanced Features
- Automated source triangulation requiring at least two independent sources for every major claim.
- Multi-round deepening loop for coverage challenge and decision-utility validation.
- Case-depth hard rules for startup research including feature analysis and MRR/growth metric anchors.
- Built-in falsification logic to identify specific data points that would prove the current conclusion wrong.
- Decision-implication mapping (so-what) at the end of every key finding section.
SKILL.md
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