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sciatlas-idea-grounding【数据与研究】Use only SciAtlas search-papers to take a novice user from zero setup to prior-art grounding for a research idea. Trigger when the user gives an idea and asks for similar work, differentiation evidence, related work, motivation support, or literature-grounded refinement.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this skill to position a research idea against prior work using only sciatlas search-papers.
search-papers.search-papers, inspect artifacts, and produce a grounded positioning memo.sciatlas executable exists without invoking a SciAtlas command: use Get-Command sciatlas -ErrorAction SilentlyContinue on Windows PowerShell or command -v sciatlas on macOS/Linux. If missing, install with python -m pip install -e ./sciatlas in this repo or python -m pip install "git+https://github.com/zjunlp/SciAtlas.git#subdirectory=sciatlas".SCIATLAS_API_KEY is absent, guide the user to http://sciatlas.openkg.cn/register.sciatlas_xxx token after registration.$env:SCIATLAS_API_BASE_URL = "http://sciatlas.openkg.cn"
$env:SCIATLAS_API_KEY = "<token>"
setx SCIATLAS_API_BASE_URL "http://sciatlas.openkg.cn"
setx SCIATLAS_API_KEY "<token>"
export SCIATLAS_API_BASE_URL="http://sciatlas.openkg.cn"
export SCIATLAS_API_KEY="<token>"
search-papers query, not health or config.Before searching, decompose the idea into:
If any of these are missing, infer reasonable defaults. Ask one short question only when the idea is too vague to search.
Run only search-papers. Use at least one close-prior-art pass:
sciatlas search-papers --retrieval-mode hybrid --query "<full idea in one sentence>" --keyword "high:<core mechanism>" --keyword "middle:<target domain>" --top-k 8 --top-keywords 0 --max-titles 0 --max-refs 0 --bias-keyword high --bias-related high --bias-citation low --bias-exploration low --ranking-profile precision --report-max-items 8
If results are sparse, run a second pass around the problem rather than the mechanism:
sciatlas search-papers --retrieval-mode hybrid --query "<problem statement>" --keyword "high:<problem>" --keyword "middle:<neighbor concept>" --top-k 8 --top-keywords 0 --max-titles 0 --max-refs 0 --bias-related high --ranking-profile balanced --report-max-items 8
Read summary.txt, report.md, request.json, and response.json. Build a prior-art matrix:
search-papers query to test the weakest point.Return: