scientific-skills/Evidence Insights/literature-close-read/SKILL.md
Produce a structured close-reading report from a paper's full PDF-to-Markdown text (with `## Page XX` pagination and image references) when you need to systematically extract background, research questions, methods, results, limitations, and reproducible experimental details.
npx skillsauth add aipoch/medical-research-skills literature-close-readInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
## Page XX and image references such as .references/guide.mdassets/deep_reading_template.mdpdf-extract (version: not specified) — used only when the source is PDF and must be converted to Markdown first.# 1) (Optional) Convert PDF to Markdown if you only have a PDF
# Note: exact command/options depend on your local pdf-extract installation.
pdf-extract paper.pdf > paper.md
# 2) Run the close-reading process (manual or via your orchestration tool):
# Input: paper.md (full text converted from PDF, may include `## Page XX` and images)
# Guidance: references/guide.md
# Template: assets/deep_reading_template.md
# 3) Save the final report as UTF-8 Markdown under outputs/
mkdir -p outputs
# Example output file name:
# outputs/paper_close_reading.md
Minimal expected I/O contract:
.md file containing the full paper text (PDF-to-Markdown), optionally with:
## Page 01.md report saved to outputs/, formatted according to assets/deep_reading_template.md.Input reading rules
## Page XX) may be used for navigation and citation, but should not alter meaning.Extraction and summarization rules
Quality constraints
Files used
references/guide.mdassets/deep_reading_template.mdoutputs/ (create if missing)literature_close_read_result.md unless the skill documentation defines a better convention.Run this minimal verification path before full execution when possible:
No local script validation step is required for this skill.
Expected output format:
Result file: literature_close_read_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any
tools
Generates complete conventional oncology bulk-transcriptome biomarker and hub-gene research designs from a user-provided cancer type and study direction. Always use this skill whenever a user wants to design, plan, or build a tumor bioinformatics study centered on differential expression, prognostic filtering or risk modeling, PPI-based hub-gene prioritization, diagnostic/prognostic evaluation, clinical association, immune infiltration context, methylation context, and optional tissue or cell validation. Covers five study patterns (signature-first prognostic workflow, hub-gene-first biomarker workflow, hybrid signature-to-hub workflow, immune-context biomarker workflow, translational validation workflow) and always outputs four workload configs (Lite / Standard / Advanced / Publication+) with recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path...
development
Generates complete conventional non-oncology bioinformatics research designs from a user-provided disease context, process-related gene family or biological theme, and validation direction. Use when a study centers on multi-dataset bulk transcriptome integration, DEG analysis, process-gene intersection, enrichment analysis, GSEA, PPI hub-gene prioritization, TF/miRNA regulatory networks, ROC-based biomarker evaluation, and immune infiltration analysis. Covers five study patterns (process-DEG discovery, enrichment/GSEA interpretation, hub-gene prioritization, regulatory-network and immune interpretation, multi-layer public validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.
tools
Plans confounder control, variable adjustment logic, and bias mitigation strategies at the protocol stage for clinical, epidemiologic, translational, observational, and biomarker studies. Always use this skill when a user needs to identify major confounders, decide which variables should or should not be adjusted for, compare matching/stratification/weighting approaches, anticipate selection or measurement bias, or pressure-test a study design before execution. Focus on bias sensing, causal structure awareness, variable-role classification, and critical design review rather than generic statistical advice.
testing
Generates complete comparative network-toxicology research designs from a user-provided exposure pair, shared toxic phenotype, and validation direction. Use when a study centers on two related exposures under one outcome and needs target collection, shared-vs-specific target decomposition, enrichment, PPI hub prioritization, docking, optional transcriptomic cross-checks, and conservative mechanistic synthesis. Covers five study patterns and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.