Subdirectories
Bundle OKF 0.2 · 9 conceitos · go-fair-us/ai-blueprint-core
Open source Repository Open in the app JSON README (API)
About
# Subdirectories
* [overview](overview/index.md) - Background, scope, and document status for the NIAID Blueprint
* [audience](audience/index.md) - Guidance for data generators and repository owners
* [implementation](implementation/index.md) - Collaborative development and Discovery Portal integration
* [metadata-schema](metadata-schema/index.md) - Minimal metadata schema — motivation, requirements, and impact
* [persistent-identifiers](persistent-identifiers/index.md) - Persistent identifiers — motivation, requirements, and impact
* [api-specification](api-specification/index.md) - Minimal API specifications — motivation, requirements, and impact
* [citation](citation/index.md) - Citation requirements — motivation, requirements, and impact
* [outreach-training](outreach-training/index.md) - Outreach and training — motivation, requirements, and impact
* [appendix](appendix/index.md) - Supplemental tables, worked examples, and reference material
Details
- Kind
- OKF bundles
- Topic
- Developer tools
- Publisher
- go-fair-us
- Origin
- okf_github
- Category
- dados
- Version
- 0.2
- Stars
- 1
- Forks
- 1
- Last push
- 2026-08-24T16:36:08Z
- Repository state
- ativo
- Language
- Python
- Added
- 2026-09-08 16:07:08
- Updated
- 2026-09-08 16:07:08
- Origin id
go-fair-us/ai-blueprint-core:okf/bundles/niaid_blueprint/index.md
README
# ai-blueprint-core AI agent tools help NIAID-funded data repositories apply the [NIAID Blueprint for Digital Objects](https://datascience.niaid.nih.gov/resources). The Blueprint is a FAIR data program from NIAID/ODSET. It defines minimal metadata schemas, persistent identifiers (PIDs), API standards, and citation practices for research data repositories. This project supplies LLM-driven agents, guided by structured prompt personas. The agents help repository owners and staff assess and apply Blueprint requirements in five areas: 1. **Metadata schema**: schema.org-based metadata elements for digital objects 2. **Persistent identifiers**: DOIs, ORCIDs, RORs, RRIDs, and ontology terms 3. **APIs and machine access**: JSON-LD endpoints, OpenAPI documentation, structured data 4. **Citation guidance**: PID-based citation examples in standard formats 5. **Outreach and training**: Contact Points, training materials, Portal onboarding ## Flipped Interaction Pattern This is not an agent. It is a pattern that reverses normal chat mode. The language model starts from a prompt and waits for user input to continue the conversation. An example lives in the `prompts` directory. That example is long and verbose. A shorter, clearer version would help. | Prompt | Purpose | |--------|---------| | `fairAssessmentInterview.md` | Runs a structured 6-phase interview to assess a repository Blueprint alignment and produces a gap report with ranked recommendations | Paste the document into your prompt. Modern models start the interview and end with a summary of the responses. The exchange can get long. If you want to stop, tell the model: "stop the interview and give me the summary now". ## Usage This repository has no executable code that you must run. The tooling converts PDF to Markdown. The converted results already live in the `docs` directory. ## Requirements - Python 3.13+ - [`uv`](https://docs.astral.sh/uv/) for environment and dependency management ## Setup ```bash # Clone and install git clone <repo-url> cd ai-blueprint-core uv sync ``` ## Reference The authoritative Blueprint specification is in `docs/BluePrint/NIAID_Blueprint_v2_26Sep2025_forExternal.md`. The tools `docling` and `marker-pdf` converted that file from the PDF. To pass this to a model, use the GitHub raw link: https://raw.githubusercontent.com/go-fair-us/ai-blueprint-core/refs/heads/master/docs/BluePrint/NIAID_Blueprint_v2_26Sep2025_forExternal.md ## Dependencies - [`docling`](https://github.com/docling-project/docling): structured document parsing and extraction - [`marker-pdf`](https://github.com/VikParuchuri/marker): PDF-to-Markdown conversion Both are ML-based libraries. Expect a large `.venv` and model downloads on the first run.