NIAID Blueprint Prompt Library
High-quality prompts to help implement the NIAID Blueprint for Including Digital Objects in the NIAID Data Ecosystem and improve FAIRness of
Open source Repository Open in the app JSON README (API)
About
# NIAID Blueprint Prompt Library
High-quality, ready-to-use LLM prompts organized by the areas of the NIAID
Blueprint. Each concept file is a single prompt; each subdirectory is a category.
## Categories
* [metadata-schema](metadata-schema/index.md) - Guidance and prompts for capturing the minimal set of metadata elements recommended by the Blueprint.
* [persistent-identifiers](persistent-identifiers/index.md) - Guidance on using globally unique, persistent, machine-resolvable identifiers for digital objects and metadata.
* [apis-metadata-exposure](apis-metadata-exposure/index.md) - Exposing metadata through standard, open APIs so machines can discover and use NIAID-funded digital objects.
* [citation-outreach](citation-outreach/index.md) - Proper citation practices and establishing points of contact for training and support.
Details
- Kind
- OKF bundles
- Topic
- AI, RAG & memory
- Publisher
- go-fair-us
- Origin
- okf_github
- Category
- dados
- Version
- 0.1
- Stars
- 1
- Forks
- 1
- Last push
- 2026-08-24T16:36:08Z
- Repository state
- ativo
- Language
- Python
- Added
- 2026-09-08 22:08:51
- Updated
- 2026-09-08 22:08:51
- Origin id
go-fair-us/ai-blueprint-core:src/promptLibrary/okf-bundle/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.