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Manage dormant problems and evoke fresh ideas with Feynman's method.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "io.github.pierreb4/seven-dpt-mcp" MCP server from askskill: Run: claude mcp add 'io-github-pierreb4-seven-dpt-mcp' -- npx -y seven-dpt-mcp
Use seven-dpt-mcp to create a dormant-problems list for these topics: reducing new-user churn, improving experiment reproducibility, and designing clearer feature names. Group them briefly by theme.
A themed dormant-problems list for continued reflection later.
Run an evoke loop for “how to improve team weekly meetings” and suggest several new tricks or angles, explaining why each is worth trying.
Several fresh ideas or tricks, each with a short rationale.
Review my existing dormant problems and connect a recent idea about “reducing context switching” to any relevant ones, producing directions worth exploring further.
Suggested links between old problems and new insights, plus follow-up directions to explore.
Researchers can store important but currently unsolved questions as dormant problems and revisit them over time. This helps preserve lines of inquiry instead of dropping them too early.
When a study topic or product problem gets stuck, users can use the evoke loop to look for new tricks or perspectives. It fits situations that need structured inspiration rather than an immediate final answer.
It is an MCP server that turns Feynman's “twelve problems” method into a callable tool. The description says it supports dormant problems and an evoke loop for discovering new tricks.
It is suited for managing long-running thinking problems and triggering fresh ideas or tricks when you are stuck. The provided information does not specify more detailed features.
The provided material does not include installation steps, runtime details, or key requirements. For exact prerequisites and integration steps, see the source repository.
Track structured reasoning with confidence, branches, and revisions for complex problem solving.
Search FTC and Pedro Pathing resources for robotics coding and development.
Run deep research and grouped tasks in parallel for faster execution.
A minimal Python MCP server for learning, testing, and integrating core primitives.
Break down complex problems into structured steps and actionable plans.
Access research workflows, retrieval tools, and knowledge resources for faster analysis.