{"repo":"foryourhealth111-pixel/research-innovation-explorer","free":true,"listed":false,"github":"https://github.com/foryourhealth111-pixel/research-innovation-explorer","clone":"git clone https://github.com/foryourhealth111-pixel/research-innovation-explorer.git","description":"The automated approach leverages the cross-combination of high-quality papers from top conferences to uncover feasible research and innovation ideas. Through multi-level verification and convergence screening, it identifies research schemes that are feasible and have in-depth value.","language":"Python","stars":85,"topics":["academic","agent-skills","ai-science","auto-research","automation","claude-code","codex","openclaw-skills","research","science"],"license":"MIT","category":"workflow-automation","readme_excerpt":"Research Innovation Explorer This search-first workflow turns a structured paper pool and an A+B matrix into an evidence-grounded landscape of research questions, uncertainties, and next checks for researcher review. 中文文档 Why This Exists Most research-idea workflows fail in one of three ways: - they rely on vague intuition instead of systematic search - they generate combinations but cannot explain why the combination matters - they lose the evidence and uncertainty that should guide the next research decision research-innovation-explorer is built to close those gaps with one coherent workflow: 1. Search broadly and repeatedly. 2. Decompose papers into reusable capabilities. 3. Generate and rank candidate combinations for review. 4. Check both combination directions against source evidence. 5. Return a provisional candidate landscape with supporting evidence, uncertainty, and next checks. Theory framing, experiment planning, and publication-oriented reporting remain available as explicit follow-up layers for a researcher-selected candidate. Core Methodology This skill is built around one explicit research-production loop: 1. Collect roughly 40 relevant, high-quality papers with enough detail to support comparison. 2. Build a pairwise combination matrix over those papers. 3. Keep one row per unique paper pair; 40 papers produce 40 x 39 / 2 = 780 rows with the current generator. 4. Use matrix scores to build a review queue, then examine both A - B and B - A through focused sour","default_branch":null,"files":null,"tree":[],"storefront":"/r/foryourhealth111-pixel","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/foryourhealth111-pixel/research-innovation-explorer/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}