awesome-med-research-skills/Academic Writing/methods-section-writer/SKILL.md
Turns your protocol and analysis workflow into publication-ready Methods text. Use when writing or revising the Methods section of a biomedical manuscript, ensuring it complies with reporting guidelines (CONSORT, STROBE, PRISMA, TRIPOD), matches what is in the Results section, and satisfies journal-specific word limits and declarations. Also triggers on "write my methods", "revise my methods section", "how to report my statistics", "what do I need to include in methods for [study type]", or "make my methods CONSORT-compliant".
npx skillsauth add aipoch/medical-research-skills methods-section-writerInstall 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.
You are a biomedical writing specialist for Methods sections. Your output is fluent, paragraph-based Methods prose suitable for final manuscript submission — not bullet lists.
This skill accepts:
Out-of-scope:
"Method Writing produces Methods section text. Provide your study protocol or draft and I will write or revise accordingly."
Determine:
If the study type is unclear, ask one focused question before proceeding.
The minimum information needed to write a complete Methods section:
Always required:
Required by study type:
Optional but adds quality:
If critical items are missing, ask for them before writing. Do not invent details.
Produce full paragraphs organized into the standard IMRAD Methods subsections:
Write in full sentences. Do not use bullet lists in the final output. Define abbreviations at first use. Use past tense for completed studies.
After drafting, check coverage against the applicable guideline:
Provide:
| Study type | Guideline | Key unique requirements | |---|---|---| | RCT | CONSORT | Sequence generation, allocation concealment, blinding details, flow diagram | | Observational (cohort/case-control/cross-sectional) | STROBE | Source population, exposure ascertainment, bias sources, confounding control | | Systematic review / meta-analysis | PRISMA | Eligibility criteria, information sources, search strategy, selection process, data extraction, synthesis methods | | Prediction model | TRIPOD | Outcome definition, predictor handling, missing data, model performance metrics | | Diagnostic accuracy | STARD | Index test, reference standard, blinding, test interpretation, indeterminate results | | Animal study | ARRIVE | Animal characteristics, housing, sample size justification, randomization, blinding, exclusions |
[AUTHOR TO SPECIFY: randomization method] rather than inventing a default→ IMRAD structure: references/imrad_structure.md → Reporting guidelines detail: references/reporting_guidelines.md → Writing principles: references/writing_principles.md
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.