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Use when deeply analyzing a single paper and producing structured notes on claims, methods, figures, evaluation, strengths, limitations, and related work.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
SOC 職業分類に基づく
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/jluo41/Tools --skill paper-analyzerコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SKILL.md を表示中
PostFn specialist -- designs/reviews the response-formatting function in an Endpoint_Set. One of 5 inference Fn-types. Called by /haipipe-end when intent references PostFn, response formatting, post-processing, or `post`.
Run any Stage 6 endpoint work: routes intent to the right specialist across Fn-type, artifact-verb, develop-target, and deploy-target axes. Use for designing inference Fns, packaging Endpoint_Sets, training a build, local inference tests, or deploying anywhere. Trigger: endpoint, deploy, develop, train, package, inference Fn, MetaFn, TrigFn, PostFn, Src2InputFn, Input2SrcFn, /haipipe-end.
Src2InputFn specialist -- designs/reviews the record->wire-payload function in an Endpoint_Set (serializes a ProcessedDF record into JSON the model can ingest). Platform-specific: one impl per deploy platform (SageMaker flat JSON vs Databricks dataframe_records); --platform picks (default sagemaker). Called by /haipipe-end when intent references Src2InputFn, record-to-payload serialization, or `src2input`.
| name | paper-analyzer |
| description | Use when deeply analyzing a single paper and producing structured notes on claims, methods, figures, evaluation, strengths, limitations, and related work. |
| metadata | {"version":"0.1.0","last_updated":"2026-05-31","summary":"Use when deeply analyzing a single paper and producing structured notes on claims, methods, figures, evaluation, strengths, limitations, and related work."} |
Perform deep analysis of a specific paper, generating structured notes that cover claims, methodology, experiment evaluation, strengths and limitations, and links to adjacent work.
Accept input: arXiv ID (e.g., "2402.12345"), full ID ("arXiv:2402.12345"), paper title, or file path.
curl -L "https://arxiv.org/pdf/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].pdf
curl -L "https://arxiv.org/e-print/[PAPER_ID]" -o /tmp/paper_analysis/[PAPER_ID].tar.gz
curl -s "https://arxiv.org/abs/[PAPER_ID]" > /tmp/paper_analysis/arxiv_page.html
Analyze: abstract, methodology, experiments, results, contributions, limitations, future work, related papers.
python scripts/generate_note.py --paper-id "$PAPER_ID" --title "$TITLE" --authors "$AUTHORS" --domain "$DOMAIN"
python scripts/update_graph.py --paper-id "$PAPER_ID" --title "$TITLE" --domain "$DOMAIN" --score $SCORE
scripts/generate_note.py — Generate structured note templatescripts/update_graph.py — Update paper relationship graphThe generated note includes: core info, abstract (EN/CN), research background, method overview with architecture figures, experiment results with tables, deep analysis, related paper comparison, tech roadmap positioning, future work, and comprehensive evaluation (0-10 scoring).
Based on evil-read-arxiv — an automated paper reading workflow. MIT License.