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sciatlas-literature-review

sciatlas-literature-review

Use only SciAtlas search-papers to take a novice user from zero setup to an evidence-grounded literature review, related-work summary, survey outline, or reading list. Trigger when the user asks for a literature review, related work section, paper map, or topic overview.

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Skill-Dokumentation

GitHub

name: sciatlas-literature-review description: Use only SciAtlas search-papers to take a novice user from zero setup to an evidence-grounded literature review, related-work summary, survey outline, or reading list. Trigger when the user asks for a literature review, related work section, paper map, or topic overview.

SciAtlas Literature Review

Use this skill to turn search-papers output into a real literature review. The only SciAtlas retrieval command allowed in this skill is sciatlas search-papers.

Operating Contract

  • Do not call any SciAtlas downstream command. The only SciAtlas retrieval command allowed is search-papers.
  • Run setup yourself when possible; ask the user only for email, verification code, token, or a necessary topic clarification.
  • Keep going until search-papers has produced artifacts or the user must supply a human-only value.
  • Never include the full API token in the final answer.

Zero-Start Bootstrap

  1. Check whether the sciatlas executable exists without invoking a SciAtlas command: use Get-Command sciatlas -ErrorAction SilentlyContinue on Windows PowerShell or command -v sciatlas on macOS/Linux.
  2. If missing, install from the local repo with python -m pip install -e ./sciatlas; otherwise use python -m pip install "git+https://github.com/zjunlp/SciAtlas.git#subdirectory=sciatlas".
  3. If SCIATLAS_API_KEY is missing, send the user to http://sciatlas.openkg.cn/register.
  4. Walk the user through the browser registration: enter email, wait for the code, ask for the code if you are controlling the form, then ask them to paste the returned sciatlas_xxx token.
  5. Configure the shell:
$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>"
  1. Use a tiny search-papers --top-k 1 only if you must verify the token before the real run.

Search Plan

Run only search-papers. Prefer two passes when the topic is broad:

Precision pass:

sciatlas search-papers --retrieval-mode hybrid --query "<topic>" --domain "<field>" --time-range "<years>" --keyword "high:<core concept>" --top-k 8 --top-keywords 0 --max-titles 0 --max-refs 0 --bias-exploration low --ranking-profile precision --report-max-items 8

Coverage pass, only if the first result is too narrow:

sciatlas search-papers --retrieval-mode hybrid --query "<topic plus neighboring concepts>" --keyword "high:<core concept>" --keyword "middle:<neighbor concept>" --top-k 10 --top-keywords 0 --max-titles 0 --max-refs 0 --bias-related high --bias-exploration middle --ranking-profile balanced --report-max-items 10

If the user provides seed papers, put exact titles in --title "high:<title>" or --reference "middle:<title>", still using search-papers.

Reading Artifacts

For each run, read:

  1. summary.txt for success/failure and result count.
  2. report.md for ranked papers and generated descriptions.
  3. request.json to record the exact query and anchors.
  4. response.json to recover missing metadata and abstracts.

Create a working evidence ledger:

  • Paper: title, year, venue/source if present.
  • Problem: what question it addresses.
  • Method: model, algorithm, system, dataset, or study design.
  • Evidence: abstract phrase or report sentence that supports inclusion.
  • Role in review: foundation, method, evaluation, application, critique, or recent extension.

Review Construction Method

Do not summarize papers one by one only. Build a literature review:

  1. Define the scope in 2-3 sentences: topic, period, and what counts as relevant.
  2. Cluster papers by research theme. Use paper content, not only keywords.
  3. Order clusters logically: foundations -> methods -> applications/evaluations -> limitations -> recent directions.
  4. For each cluster, explain the shared research question and cite representative papers from the ledger.
  5. Compare papers inside a cluster: what changed in method, data, assumptions, or evaluation.
  6. Identify consensus, disagreement, and gaps. A gap must be tied to missing evaluation, weak assumptions, limited dataset, deployment challenge, or unconnected neighboring literature.
  7. End with a reading path: first 3 papers to read and why.

Deliverable

Return:

  • The exact search-papers command(s), with token omitted.
  • Evidence table of 6-10 papers.
  • Thematic literature review with headings.
  • Timeline or development notes when years are available.
  • Gaps and next search queries.
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