scientific-skills/Protocol Design/research-grants/SKILL.md
Write competitive research proposals for NSF, NIH, DOE, DARPA, and Taiwan's NSTC when you need agency-compliant narratives, budgets, and review-criteria alignment for a specific solicitation/FOA/BAA.
npx skillsauth add aipoch/medical-research-skills research-grantsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when you need to produce or revise a grant application that must meet strict agency rules and reviewer expectations, for example:
Agency-aware structure and compliance
Review-criteria-driven writing
Budget-to-scope alignment
Milestones, timeline, and management planning
Mandatory visual communication workflow
scientific-schematics skill to generate publication-quality figures.Reference-driven drafting
references/nsf_guidelines.mdreferences/nih_guidelines.mdreferences/doe_guidelines.mdreferences/darpa_guidelines.mdreferences/nstc_guidelines.mdreferences/specific_aims_guide.mdreferences/broader_impacts.mdreferences/budget_preparation.mdreferences/review_criteria.mdreferences/timeline_planning.mdreferences/team_building.mdreferences/resubmission_strategies.mdscripts/compliance_checker.py (format checks)scripts/budget_calculator.py (budget math support)scripts/deadline_tracker.py (planning support)scripts/generate_schematic.py (diagram generation wrapper; used with scientific-schematics)Note: Exact third-party Python package requirements are not specified in the source document. If you maintain this skill repository, add a
requirements.txt(with pinned versions) and list them here.
The example below is a complete, runnable workflow that (1) generates required visuals, (2) drafts core sections, and (3) performs basic compliance checks using the included scripts.
# Conceptual framework / workflow diagram
python scripts/generate_schematic.py \
"Conceptual workflow for a 3-aim biomedical project: Aim 1 data collection -> Aim 2 model development -> Aim 3 validation; include feedback loop and key deliverables" \
-o figures/workflow.png
# Timeline / milestones diagram (recommended)
python scripts/generate_schematic.py \
"Gantt chart for a 3-year project with quarterly milestones; include go/no-go at end of Year 1 and deliverables per aim" \
-o figures/timeline.png
Create proposal.md:
# Project Title
Mechanistic and Translational Study of X to Enable Y
## NIH Specific Aims (1 page target)
**Knowledge gap:** ...
**Long-term goal:** ...
**Objective:** ...
**Central hypothesis:** ...
**Aim 1 (verb-led):** ...
- Rationale:
- Approach (high level):
- Expected outcomes:
**Aim 2:** ...
**Aim 3:** ...
**Impact:** If successful, this work will ...
## Research Strategy (12 pages target for R01)
### Significance
- Problem and barrier to progress:
- Why now / why this team:
- Expected impact on health/biology:
### Innovation
- Conceptual innovation:
- Methodological innovation:
- Why current approaches are insufficient:
### Approach
#### Overview and rationale
#### Aim 1 Methods
- Design:
- Data:
- Analysis:
- Pitfalls and alternatives:
#### Aim 2 Methods
...
#### Aim 3 Methods
...
### Rigor and Reproducibility (as applicable)
- Controls, replicates, blinding/randomization:
- Power/statistics:
- Data management and sharing:
python scripts/compliance_checker.py proposal.md
Create budget_justification.md:
# Budget Justification (Draft)
## Personnel
- PI (X% effort): ...
- Postdoc (100%): ...
- Graduate student (50%): ...
## Equipment
- Item: purpose, necessity, and timing
## Travel
- Conference dissemination
- Collaboration meetings
## Materials and Supplies
- Consumables / software licenses
## Other Direct Costs
- Publication fees / participant incentives / consultants
## Subawards (if any)
- Scope and deliverables per partner
## Indirect Costs (F&A)
- Rate and base per institutional policy
NSF
NIH
DOE
DARPA
NSTC (Taiwan)
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.