Rank theoretical frameworks by fit and output theory_proposals.json.
npx skills add https://github.com/ShinyGua/AutoArtsResearch --skill ar-theory
Please help me install this Agent Skill. Skill: ar-theory Source: https://github.com/ShinyGua/AutoArtsResearch/tree/main/.claude/skills/ar-theory Command: npx skills add https://github.com/ShinyGua/AutoArtsResearch --skill ar-theory
This Theory Agent helps researchers identify relevant theoretical frameworks for a given research question, assess their fit, and surface rival theories to inform theoretical framing.
Provide your workspace with a literature_map and scoping report, then run the ar-theory skill to generate theory proposals.
High-intent search queries and answers about installing and using this skill.
To identify relevant theoretical frameworks, you can use an agent that analyzes your literature map and scoping report to propose 2-4 candidate frameworks ranked by evidence fit, question alignment, methodological compatibility, and novelty.
Ranking candidate theoretical frameworks involves assessing them across multiple criteria including evidence fit, question alignment, methodological compatibility, and novelty to produce a structured JSON file with ranked proposals.
Generating theory proposals from a literature map requires loading the map and scoping data into a workspace, then running an automated agent to extract and rank 2-4 candidate theoretical frameworks.
Yes, surfacing rival theories to inform theoretical framing is possible by analyzing scoping data, which outputs a debate_proposal flag in the generated JSON file when rival theories need to be considered.
Yes, proposing theoretical frameworks requires a scoping report and a literature map in the workspace to accurately assess framework fit and generate a structured justification for selection.
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