LLMs-Universal-Life-Science-and-Clinical-Skills- computational-software-development
Full-stack computational software development for biomedical and life science applications. Use when building or refactoring research software, LLM-enabled analysis platforms, scientific web apps, data pipelines, RAG systems, evaluation harnesses, or package-quality Python services for translational research.
install
source · Clone the upstream repo
git clone https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- "$T" && mkdir -p ~/.claude/skills && cp -r "$T/Skills/User_Collections/Babu/computational-software-development" ~/.claude/skills/mdbabumiamssm-llms-universal-life-science-and-clinical-skills-computational-soft && rm -rf "$T"
manifest:
Skills/User_Collections/Babu/computational-software-development/SKILL.mdsource content
Computational Software Development
Build production-oriented biomedical software with a bias toward reproducibility, clear interfaces, and testable workflows.
Workflow
- Clarify the product boundary: analysis script, library, API, web app, pipeline, or multi-agent system.
- Inspect the local repository before proposing architecture changes; preserve existing conventions unless they are actively harmful.
- Choose the simplest stack that fits the job: plain Python package first, then API or UI layers only when they solve a real need.
- Separate scientific logic, orchestration, and presentation so domain code remains testable outside the UI.
- Add validation around files, schemas, model outputs, and external API calls; fail loudly when assumptions break.
- Prefer deterministic scripts for repeated scientific tasks and reserve LLM calls for synthesis, extraction, ranking, or planning.
- Deliver runnable code plus concise usage notes, tests, and environment assumptions.
Engineering Priorities
- Keep domain logic in importable modules rather than notebooks or route handlers.
- Treat data contracts explicitly: define expected columns, shapes, units, genome builds, and identifier namespaces.
- For LLM features, specify provider fallback rules, timeout budgets, retry policy, and citation or provenance requirements.
- For biomedical applications, avoid hidden PHI flows and minimize access to sensitive fields.
- Prefer small composable services over one large agent script.
Common Deliverables
- Python packages with
layout, CLI entry points, and tests.src/ - Flask or FastAPI backends for analysis services or RAG endpoints.
- Data pipelines for omics preprocessing, QC, and result packaging.
- Multi-LLM orchestration with structured outputs, adjudication, and evaluation.
- Research dashboards that expose results without burying the computational workflow.
References
- Read
for architecture selection and delivery checklists.references/architecture-patterns.md - Read
when the request involves RAG, citations, or multi-provider systems.references/biomedical-llm-patterns.md