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what is research-grant-finder-agent fr?

quazi-07/research-grant-finder-agent — explained in plain English

Analysis updated 2026-05-18

26PythonAudience · researcherComplexity · 3/5Setup · moderate

tl;dr

An AI agent workflow that searches NIH and federal grant sources, ranks funding opportunities by relevance to data science and AI research, and shows results in a dashboard.

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what do people make with this?

VIBE 1

Search NIH Guide and Grants.gov for funding opportunities relevant to AI or data science research.

VIBE 2

Rank funding announcements by relevance even when they use different wording than your search terms.

VIBE 3

Review prioritized grant opportunities through an interactive Streamlit dashboard.

VIBE 4

Export ranked funding opportunities to Excel or a digest report for team review.

what's the stack?

PythonStreamlitSQLite

how it stacks up fr

quazi-07/research-grant-finder-agent95ge/cfquantaevella/sky-pc-mcp-companion
Stars262626
LanguagePythonPythonPython
Setup difficultymoderatemoderatemoderate
Complexity3/53/53/5
Audienceresearcherdevelopervibe coder

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · moderate time til it works · 30min

Requires Python 3.10 or later plus access to the NIH Guide, Grants.gov, and NIH RePORTER data sources.

in plain english

Research Grant Finder Agent is a Python tool that helps researchers find NIH funding opportunities worth applying to, instead of manually searching databases and reading long announcements one by one. Rather than doing a simple keyword search, it organizes the process into a workflow of several specialized AI agents, each handling one part of the job, from collecting opportunities to analyzing and ranking them by relevance. The idea behind the project is that a researcher searching for terms like data science may miss funding announcements that use different wording, such as predictive modeling, omics, or medical imaging, even though those opportunities are closely related to their work. The tool tries to catch those near misses by evaluating opportunities on several signals rather than one exact phrase match. Separate agents handle different sources and tasks: one pulls announcements from the NIH Guide, another searches Grants.gov, and a third gathers related project context from NIH RePORTER. A parser agent converts all of these different formats into one common structure, duplicates are filtered out, and a relevance agent scores each opportunity using computational keyword signals, whether relevant terms appear in the title itself, similarity to known AI and data-science grants, whether the source is NIH or HHS, upcoming deadlines, and how complete the listing's information is. Opportunities are then grouped into high, medium, and watchlist priority tiers. Results are stored in a SQLite database and can be reviewed through a Streamlit dashboard, exported to Excel, or summarized in a digest report. There is also a feedback agent meant to capture researcher input so relevance scoring can be tuned over time. The project describes itself as a research prototype built with Python 3.10 or later.

prompts (copy fr)

prompt 1
Explain how the ML Relevance Agent scores NIH funding opportunities in this project.
prompt 2
Help me set up the Research Grant Finder Agent and run its Streamlit dashboard.
prompt 3
Walk me through how the Grant Parser Agent normalizes records from different funding sources.
prompt 4
Show me how the high, medium, and watchlist priority tiers are decided.

Frequently asked questions

what is research-grant-finder-agent fr?

An AI agent workflow that searches NIH and federal grant sources, ranks funding opportunities by relevance to data science and AI research, and shows results in a dashboard.

What language is research-grant-finder-agent written in?

Mainly Python. The stack also includes Python, Streamlit, SQLite.

How hard is research-grant-finder-agent to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is research-grant-finder-agent for?

Mainly researcher.

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