Build a generalizable Claude Science tool that automates the complete therapeutic development pipeline for any disease: from understanding unmet needs and patient priorities, through data/literature mining for novel therapeutics, AI/ML validation (protein modeling, etc.), to generating scientific, regulatory, and commercial strategies. System should iteratively request human validation at decision points where confidence is insufficient.

The session aimed to build a generalizable "Claude Science" tool/skill that automates an end-to-end therapeutic development pipeline — from unmet-need and literature discovery through AI/ML validation to scientific, regulatory, and commercial strategy — with built-in checkpoints for human validation wherever confidence is low.

The agent constructed a disease-agnostic pipeline architecture (staged process, checkpoint protocol, and a program dossier schema) rather than a single-disease analysis, and packaged it as a deployable skill with an orchestration kernel and agent manifest. It tested the framework with dry runs on other conditions (SMA and IPF) and built supporting reference material such as a genetic modality tree and target-modality rubric. The final steps focused on packaging and publishing the work as a public, reusable repository, including fixing repo completeness (adding a missing .gitignore) and correcting the README's description of what the deployment helper (deploy_agent.py) actually does.

The primary deliverable is the public GitHub repository (https://github.com/ruthannepai-tech/therapeutic-program-architect) containing all 16 session artifacts — core pipeline logic (kernel.py, SKILL.md, stage_spec.md, checkpoint_protocol.md, program_dossier.schema.json), supporting templates and rubrics (scientific_plan_template.md, program_summary_template.md, target_modality_rubric.csv, genetic_modality_tree.md/csv, checkpoint_map.csv), and validation dry runs (dryrun_SMA_genetic.md, dryrun_IPF.md), along with README, LICENSE, and .gitignore for deployment. Note