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Load when extracting GEO accessions, dataset metadata, and downloadable references from a scientific paper (PDF / URL / DOI / PubMed ID / raw text) for downstream omics analysis. Skip when the dataset is already in hand or when only routing a query (use `orchestrator`).
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。
SOC 職業分類に基づく
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Build reproducible healthcare imaging pipelines with Project MONAI for DICOM, NIfTI, pathology, and multidimensional imaging tasks including preprocessing, augmentation, training, sliding-window inference, evaluation, model bundles, labeling, and deployment. Use when implementing medical image classification, segmentation, registration, detection, generative, or foundation-model workflows in PyTorch.
Operate Google TxGemma prediction and chat models for therapeutic property prediction across small molecules, proteins, nucleic acids, diseases, targets, and cell lines. Use when formatting Therapeutics Data Commons tasks, choosing TxGemma model size or variant, running local or Model Garden inference, fine-tuning on private therapeutic data, or evaluating TxGemma in drug-discovery workflows.
Evaluate and operate released Profluent OpenCRISPR gene-editing systems, especially OpenCRISPR-1, for controlled research workflows using its published Cas9-like protein, compatible guide RNA designs, protocols, licensing, specificity testing, and experimental validation. Use when comparing OpenCRISPR-1 with SpCas9, planning nonclinical editing studies, or assessing use in nuclease, nickase, deactivated, base, prime, or epigenome-editing contexts.
Operate CZI TranscriptFormer cross-species generative single-cell models to produce cell embeddings, contextual gene embeddings, likelihoods, zero-shot classifiers, disease-state representations, and regulatory analyses from raw-count AnnData files. Use when selecting TF-Sapiens, TF-Exemplar, or TF-Metazoa, processing in- or out-of-distribution species, or scaling embedding extraction across GPUs.
Operate ByteDance Protenix-v2 for open biomolecular structure prediction of proteins, antibodies, nucleic acids, ligands, and complexes using JSON inputs, MSA and template features, constraints, and inference-time sampling. Use when running Protenix locally or through its server, comparing AlphaFold3-style open models, or building reproducible co-folding evaluations.
| name | literature |
| description | Load when extracting GEO accessions, dataset metadata, and downloadable references from a scientific paper (PDF / URL / DOI / PubMed ID / raw text) for downstream omics analysis. Skip when the dataset is already in hand or when only routing a query (use `orchestrator`). |
| version | 0.5.0 |
| author | OmicsClaw |
| license | MIT |
| tags | ["literature","pdf","doi","pubmed","geo","metadata"] |
| requires | ["requests"] |
The user provides a scientific paper reference (PDF path, URL, DOI, PubMed ID, or raw text excerpt) and wants OmicsClaw to extract GEO accessions, dataset metadata, and (optionally) download referenced GEO datasets — so a downstream analysis skill can be invoked on real data.
--input-type defaults to auto (sniffs from input shape).
--no-download skips the GEO download step (metadata only).
For dispatching a NL query to an analysis skill use orchestrator.
For scaffolding a new skill from a paper use omics-skill-builder.
| Input | Format | Required |
|---|---|---|
| Reference | --input <URL|DOI|PubMed|PDF path|text> | yes (unless --demo) |
| Input type | --input-type {auto,url,doi,pubmed,file,text} (default auto) | no |
| Skip download | --no-download (extract metadata only) | no |
| Data dir | --data-dir <path> (default data/) | no |
| Output | Path | Notes |
|---|---|---|
| Extracted metadata | output_dir/extracted_metadata.json | written at literature_parse.py:80 |
| Report | output_dir/report.md | written at literature_parse.py:193 |
| Result envelope | output_dir/result.json | written at literature_parse.py:147 |
| Downloaded GEO data | <data-dir>/<GSE...>/... | only when GEO accessions found AND --no-download not set |
--input (or --demo); raise parser.error('the following arguments are required: --input (unless --demo is used)') at literature_parse.py:38 when missing.--input-type auto or honour the explicit value.parse_input (skills/literature/core/parser.py); fetch / parse content.extract_metadata (skills/literature/core/extractor.py) → identify GEO accessions, dataset metadata, study type.--no-download: call download_geo_dataset (skills/literature/core/downloader.py) → save to --data-dir.extracted_metadata.json (literature_parse.py:80) + report.md (:193) + result.json (:147).--input REQUIRED unless --demo — uses parser.error (exit 2). literature_parse.py:38 calls parser.error('the following arguments are required: --input (unless --demo is used)'). Different from most file-pipeline skills which raise ValueError.--input-type auto heuristics are positional, not URL-aware. core/parser.py:35-55 checks the bare-DOI regex ^10\.\d{4,}/\S+ first; URLs always hit the startswith("http") branch and resolve to url, even when they wrap a DOI (https://doi.org/10.1038/...). For PDF / file paths use --input-type file explicitly — Path.exists() has to succeed for auto-detection to pick file.download_geo_dataset issues HTTP requests to GEO FTP. Air-gapped runs must pass --no-download or the run will hang / time out.pypdf / similar. If the PDF parser dependency is missing, the run errors out — verify skills/literature/requirements.txt is satisfied.extracted_metadata.json is at output_dir/ ROOT, not tables/. This skill does NOT follow the tables/<file>.csv convention used by analysis skills.literature_parse.py:64 calls sys.exit(1) on internal parse failure (distinct from the parser.error exit-2 path for missing args).# Demo (built-in local text)
python omicsclaw.py run literature --demo --output /tmp/lit_demo
# DOI
python omicsclaw.py run literature \
--input "10.1038/s41586-021-03689-7" --output results/
# PDF (use --input-type file)
python omicsclaw.py run literature \
--input my_paper.pdf --input-type file --output results/
# URL, metadata-only (no GEO download)
python omicsclaw.py run literature \
--input "https://www.nature.com/articles/..." \
--output results/ --no-download
references/parameters.md — every CLI flag, input-type heuristicsreferences/methodology.md — GEO accession rules, parser fallbacksreferences/output_contract.md — extracted_metadata.json schemaorchestrator (downstream — routes the resulting dataset to an analysis skill), omics-skill-builder (parallel — scaffold a new skill from a paper)