This repository serves as both the central federation registry and a host for AI agent-compatible scientific protocols for the Waldron Lab.
An "AI Agent Protocol" is related to but different than an AI Skill.
- AI Skill: A specific capability given to an AI agent (e.g., how to query a specific biological database, or how to use a particular R package). Skills teach the AI how to perform specific actions.
- AI Agent Protocol: A scientific workflow, experimental plan, or analytical pipeline designed to be executed by or in collaboration with an AI agent. Protocols in this registry are designed around provenance to published methods. Protocols:
- are formal records providing citation both to primary scientific literature and to publication of protocols (#4). Atomic protocols have a single purpose with a single citation to primary literature; composite protocols may be composed of multiple atomic protocols.
- will record human reviews (#2)
- will support formal unit tests/benchmarks (#3) to verify correct execution by different AI agents and models.
Some likely use cases include:
- Constraining Coding Agents to Established Methods: Forces AI agents to adhere strictly to vetted, peer-reviewed analytical protocols rather than drifting, inventing parameters, or inventing plausible but untested methodology during automated script generation. Protocols are expected to create more uniform behavior by different AI agents and models.
- Cross-Language and Pipeline Translation: Serves as an unambiguous English-language specification for translating computational workflows across programming languages and pipeline frameworks (e.g., Nextflow ↔ Snakemake, R ↔ Python) without losing domain-specific logic or parameter integrity.
- Discrepancy Auditing (Paper vs. Code vs. Protocol): Acts as an explicit benchmark to systematically detect inconsistencies between high-level descriptions in published manuscript Methods sections, formal protocol documentation, and actual codebase implementations. Protocols should be easier for people with domain expertise to review than codebase or even Methods sections which are less structured, can be split across main manuscript and supplementary materials, and may lack necessary details for full implementation.
- Filling the Methodological Reproducibility Gap: Provides the granular operational, environment, and parameter-level details that traditional journal Methods sections often omit, facilitating computational reproducibility with less susceptbility to bitrot or dependency issues.
- A federated registry of AI agent-compatible protocols: This repository serves as a central registry for AI agent-compatible protocols, designed to allow researchers to independently create their own protocol repositories and federate them into this central registry.
This repository is dual-licensed:
- Software & Scripts (e.g., contents of the
scripts/directory): MIT License - Scientific Protocols & Documentation (e.g., contents of the
protocols/directory, unless otherwise specified in their YAML frontmatter): Creative Commons Attribution 4.0 International (CC-BY-4.0)