Implementing Stanford's STORM Research Method in Claude

The STORM Research Framework

The author implements the Stanford STORM (Synthesis of Topic-based Research and Modeling) method as a custom skill for Claude. Unlike standard deep research tools that generate broad markdown dumps, this approach forces the model to adopt five specific expert personas: practitioner, academic, skeptic, economist, and historian. By running these perspectives in parallel, the system identifies blind spots and contradictions, followed by an adversarial peer-review phase that verifies or demotes citations before generating a final HTML report.

Implementation and Workflow

To deploy this, the author chains four specific prompts into a single skill file (skill.md) placed within the Claude desktop or VS Code environment. The workflow follows a strict sequence:

• Scoping: The system defines the research topic and identifies the user's persona to tailor the output.
• Parallel Research: Five sub-agents execute research based on their assigned expert lens.
• Contradiction Mapping: The system analyzes where the five perspectives disagree and evaluates the strength of evidence for each claim.
• Adversarial Verification: A secondary set of agents verifies every citation against primary sources, labeling them as confirmed, corrected, or demoted.
• HTML Synthesis: The output is rendered into a consistent HTML template that includes a 60-second summary, key findings ranked by reliability, and a list of assumptions.

Agent Teams vs. Sub-agents

The author distinguishes between sub-agents and agent teams. In this implementation, the sub-agents work for the main session but do not communicate with each other. Conversely, agent teams allow for internal debate and consensus-building, which is more effective for complex decision-making but significantly more expensive in terms of token usage and API costs.