research-architect-brainstorm

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Use when a user needs distinct, feasible Research Architect research-question options from a raw topic, available materials, constraints, and—when available—transferable logic from a target reference paper.

AI & Automation 68 stars 2 forks Updated 2 weeks ago MIT

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Quality Score: 82/100

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90
Frontmatter 20%
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Issue Health 10%
80
License 10%
100
Description 5%
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Skill Content

# Research Architect Brainstorm Generate genuinely different research options, not topic paraphrases. A strong option changes the research logic, evidence path, contribution, or scope. ## Inputs Read when available: ```text paper_output/project_config.json paper_output/source_map.md paper_output/source_inventory.md paper_output/terminology_ledger.md # when present paper_output/exemplar_logic_profile.md # when accessible target references exist paper_output/exemplar_adaptation_plan.md # preliminary or approved version when available ``` Read `../research-architect/references/brainstorming-framework.md`. Use `../research-architect/templates/idea_candidate_matrix.csv` as the column contract and write `paper_output/idea_candidate_matrix.md` as a Markdown table. ## Lenses and outputs Generate 2–5 candidates using the reference-logic, adaptation, problem, material, inquiry, warrant, disconfirmation, and reader-value lenses. Create: ```text paper_output/problem_landscape.md paper_output/idea_candidate_matrix.md paper_output/research_question_options.md paper_output/feasibility_filter.md ``` Each option states the problem/tension, research question, inherited reference function when applicable, required adaptation, independent contribution, needed and available evidence, provisional design family, claim boundary, feasibility, and first action. Classify candidates as `advance`, `needs_more_material`, `defer`, or `reject`. A conceptually strong option ...

Details

Author
mmTheBest
Repository
mmTheBest/Research-Architect
Created
2 months ago
Last Updated
2 weeks ago
Language
Python
License
MIT

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