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Get Started Free →Search NSF awards and grants with free public API, no auth required
.claude/skills/brycewang-stanford-nsf-award-api-guide/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 31% | 0% |
| case-12 | ✓→✗ | ▼ Worse | 25% | 0% |
| case-02 | ✓→✓ | = Same ✓ | 34% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 65% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 34% | 0% |
The National Science Foundation (NSF) Award Search API provides free, unauthenticated access to a comprehensive database of NSF-funded awards spanning decades of federally funded research in the United States. This API is invaluable for researchers seeking to understand funding trends, identify potential collaborators, or find precedent awards in their field.
The API covers all NSF directorates including Computer and Information Science and Engineering (CISE), Biological Sciences (BIO), Engineering (ENG), Geosciences (GEO), Mathematical and Physical Sciences (MPS), Social, Behavioral and Economic Sciences (SBE), and Education and Human Resources (EHR). Each award record includes principal investigator information, award amounts, abstracts, and institutional details.
No API key or authentication is needed. The service returns JSON or XML and supports a wide range of query parameters for precise filtering.
No authentication is required. The NSF Award Search API is completely open and free to use.
bash# No API key needed -- just make the request curl "https://api.nsf.gov/services/v1/awards.json?keyword=quantum+computing"
GET https://api.nsf.gov/services/v1/awards.json?{params}Key Parameters:
keyword: Full-text search across award title and abstractpiFirstName, piLastName: Filter by principal investigator nameawardeeCity, awardeeStateCode: Filter by institution locationstartDateStart, startDateEnd: Date range (format: MM/DD/YYYY)fundProgramName: NSF program nameawardeeName: Institution nameoffset: Pagination offset (default 1)printFields: Comma-separated list of fields to returnExample: Search for machine learning awards at MIT:
bashcurl -s "https://api.nsf.gov/services/v1/awards.json?\ keyword=machine+learning&\ awardeeName=Massachusetts+Institute+of+Technology&\ printFields=id,title,piFirstName,piLastName,startDate,awardeeName,fundsObligatedAmt,abstractText&\ offset=1" | python3 -m json.tool
Retrieve details for a specific award by its NSF award number.
bashcurl -s "https://api.nsf.gov/services/v1/awards/2345678.json?\ printFields=id,title,piFirstName,piLastName,startDate,expDate,awardeeName,fundsObligatedAmt,abstractText" \ | python3 -m json.tool
pythonimport requests import time base_url = "https://api.nsf.gov/services/v1/awards.json" def search_nsf_awards(keyword, year_start, year_end, max_results=100): """Search NSF awards and return structured results.""" results = [] offset = 1 while len(results) < max_results: params = { "keyword": keyword, "startDateStart": f"01/01/{year_start}", "startDateEnd": f"12/31/{year_end}", "printFields": "id,title,piFirstName,piLastName,startDate,awardeeName,fundsObligatedAmt", "offset": offset } resp = requests.get(base_url, params=params) data = resp.json() awards = data.get("response", {}).get("award", []) if not awards: break results.extend(awards) offset += 25 time.sleep(0.5) return results[:max_results] awards = search_nsf_awards("large language models", 2022, 2025) total_funding = sum(int(a.get("fundsObligatedAmt", 0)) for a in awards) print(f"Found {len(awards)} awards, total funding: ${total_funding:,}") for a in awards[:5]: print(f" [{a['id']}] {a['title']} — ${int(a.get('fundsObligatedAmt', 0)):,}")
Grant Prospecting: Search for awards in your field to understand typical funding amounts, common program solicitations, and successful framing of research projects. Analyze abstracts of funded proposals for vocabulary and scope.
Collaborator Discovery: Search by keyword and explore the PI network to identify active researchers and institutions. Cross-reference with publication databases to find potential collaborators.
Funding Landscape Analysis: Track funding volumes over time for specific keywords or programs to identify emerging priorities and declining areas. Useful for strategic planning and grant writing.
Institutional Benchmarking: Compare award counts and funding totals across institutions for a given field or directorate.
offset to paginate)printFields to reduce response size and improve performance| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 9,180 | 11,936 | +30% | 1 | 1 | 0% | 1,537 | 2,016 | +31% | 0 | 0 | — |
case-02 | pass→pass | 9,987 | 4,972 | -50% | 1 | 1 | 0% | 1,560 | 2,090 | +34% | 0 | 0 | — |
case-03 | pass→pass | 6,811 | 4,751 | -30% | 1 | 1 | 0% | 1,264 | 2,086 | +65% | 0 | 0 | — |
case-04 | pass→pass | 9,082 | 3,578 | -61% | 1 | 1 | 0% | 1,367 | 1,838 | +34% | 0 | 0 | — |
case-05 | pass→pass | 7,580 | 2,839 | -63% | 1 | 1 | 0% | 1,306 | 1,865 | +43% | 0 | 0 | — |
case-06 | pass→pass | 6,504 | 3,603 | -45% | 1 | 1 | 0% | 1,266 | 2,006 | +58% | 0 | 0 | — |
case-07 | pass→pass | 3,893 | 3,777 | -3% | 1 | 1 | 0% | 662 | 1,805 | +173% | 0 | 0 | — |
case-08 | pass→pass | 10,101 | 5,337 | -47% | 1 | 1 | 0% | 1,719 | 2,042 | +19% | 0 | 0 | — |
case-09 | pass→pass | 9,838 | 4,223 | -57% | 1 | 1 | 0% | 1,525 | 1,929 | +26% | 0 | 0 | — |
case-10 | pass→pass | 6,691 | 2,186 | -67% | 1 | 1 | 0% | 1,103 | 1,725 | +56% | 0 | 0 | — |
case-11 | pass→pass | 8,836 | 5,012 | -43% | 1 | 1 | 0% | 1,411 | 1,985 | +41% | 0 | 0 | — |
case-12 | pass→fail | 14,762 | 10,160 | -31% | 1 | 1 | 0% | 2,502 | 3,130 | +25% | 0 | 0 | — |
case-13 | pass→pass | 16,505 | 8,878 | -46% | 1 | 1 | 0% | 2,225 | 2,631 | +18% | 0 | 0 | — |
case-14 | pass→pass | 8,877 | 2,268 | -74% | 1 | 1 | 0% | 1,298 | 1,815 | +40% | 0 | 0 | — |
case-15 | pass→pass | 5,431 | 3,372 | -38% | 1 | 1 | 0% | 840 | 1,993 | +137% | 0 | 0 | — |
case-16 | pass→pass | 4,008 | 2,418 | -40% | 1 | 1 | 0% | 736 | 1,733 | +135% | 0 | 0 | — |
case-17 | pass→pass | 4,915 | 2,692 | -45% | 1 | 1 | 0% | 787 | 1,672 | +112% | 0 | 0 | — |
case-18 | pass→pass | 6,421 | 3,288 | -49% | 1 | 1 | 0% | 920 | 1,762 | +92% | 0 | 0 | — |
case-19 | pass→pass | 9,622 | 6,924 | -28% | 1 | 1 | 0% | 1,536 | 2,636 | +72% | 0 | 0 | — |
case-20 | pass→pass | 15,727 | 15,979 | +2% | 1 | 1 | 0% | 2,972 | 4,086 | +37% | 0 | 0 | — |
case-21 | pass→pass | 12,243 | 17,364 | +42% | 1 | 1 | 0% | 3,000 | 5,001 | +67% | 0 | 0 | — |
case-22 | pass→pass | 13,213 | 17,032 | +29% | 1 | 1 | 0% | 2,669 | 4,063 | +52% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of -100 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.