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Get Started Free →Access NBER working papers and economic research datasets
.claude/skills/brycewang-stanford-nber-working-papers-api/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 97% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 16% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 29% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 63% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 6% | 0% |
The National Bureau of Economic Research (NBER) is the leading U.S. economics research organization, publishing 1,200+ working papers annually by top economists. NBER papers are among the most cited in economics. The website provides structured access to working papers, researcher profiles, and macroeconomic datasets. Free metadata access; some full text requires subscription.
bash# Latest working papers feed curl "https://www.nber.org/papers.rss" # Papers by program curl "https://www.nber.org/programs/ef/papers.rss" # Economic Fluctuations curl "https://www.nber.org/programs/ls/papers.rss" # Labor Studies curl "https://www.nber.org/programs/io/papers.rss" # Industrial Organization
bash# Search via NBER website (HTML scraping needed) curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/?page=1&perPage=20&q=inflation+expectations" # Get specific paper metadata curl "https://www.nber.org/api/v1/working_page_listing/contentType/working_paper/?page=1&perPage=1&q=w28104"
bash# Macroeconomic history data # Available at: https://data.nber.org/ # Business cycle dates curl "https://data.nber.org/data/cycles/business_cycle_dates.json" # CPS labor data extracts # https://data.nber.org/cps/
| Code | Program | Focus | |------|---------|-------| | ef | Economic Fluctuations and Growth | Macro, business cycles | | ls | Labor Studies | Employment, wages | | io | Industrial Organization | Markets, competition | | pe | Public Economics | Taxation, spending | | he | Health Economics | Healthcare markets | | de | Development Economics | Developing countries | | if | International Finance | Exchange rates, capital flows | | it | International Trade | Trade policy | | me | Monetary Economics | Central banking | | cf | Corporate Finance | Firm finance | | ap | Asset Pricing | Financial markets | | ed | Education | Education economics | | ag | Aging | Demographics | | ch | Children | Child welfare | | le | Law and Economics | Legal institutions | | env | Environment and Energy | Environmental policy | | pol | Political Economy | Political institutions |
pythonimport requests from xml.etree import ElementTree def get_latest_papers(program: str = None, count: int = 20) -> list: """Get latest NBER working papers via RSS.""" if program: url = f"https://www.nber.org/programs/{program}/papers.rss" else: url = "https://www.nber.org/papers.rss" resp = requests.get(url, timeout=30) resp.raise_for_status() root = ElementTree.fromstring(resp.content) papers = [] for item in root.findall(".//item")[:count]: papers.append({ "title": item.findtext("title", ""), "link": item.findtext("link", ""), "description": item.findtext("description", "")[:300], "pub_date": item.findtext("pubDate", ""), }) return papers def search_papers(query: str, page: int = 1, per_page: int = 20) -> list: """Search NBER working papers.""" resp = requests.get( "https://www.nber.org/api/v1/working_page_listing/" "contentType/working_paper/", params={"q": query, "page": page, "perPage": per_page}, timeout=30, ) resp.raise_for_status() data = resp.json() results = [] for item in data.get("results", []): results.append({ "title": item.get("title"), "authors": item.get("authors", ""), "number": item.get("wp_number", ""), "date": item.get("date", ""), "url": f"https://www.nber.org/papers/{item.get('wp_number', '')}", "abstract": item.get("description", "")[:300], "program": item.get("programs", []), }) return results def get_business_cycle_dates() -> list: """Get NBER official business cycle dates.""" resp = requests.get( "https://data.nber.org/data/cycles/business_cycle_dates.json", timeout=30, ) resp.raise_for_status() return resp.json() # Example: latest macro working papers papers = get_latest_papers(program="ef", count=5) for p in papers: print(f"{p['title']}") print(f" {p['link']}") # Example: search for AI economics papers results = search_papers("artificial intelligence labor market") for r in results: print(f"[{r['number']}] {r['title']}") print(f" Authors: {r['authors']}") # Example: recession dates cycles = get_business_cycle_dates() for c in cycles[-3:]: print(f"Peak: {c.get('peak')} → Trough: {c.get('trough')}")
| Dataset | Description | |---------|-------------| | Business Cycle Dates | Official US recession start/end dates | | CPS Extracts | Current Population Survey labor data | | Macrohistory Database | 150 years of macro indicators | | Patent Data | Patent citation and classification | | Trade Data | Bilateral trade statistics |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 7,307 | 7,007 | -4% | 1 | 1 | 0% | 1,494 | 2,938 | +97% | 0 | 0 | — |
case-02 | fail→pass | 7,768 | 2,363 | -70% | 1 | 1 | 0% | 1,751 | 2,026 | +16% | 0 | 0 | — |
case-03 | fail→pass | 6,882 | 1,959 | -72% | 1 | 1 | 0% | 1,524 | 1,960 | +29% | 0 | 0 | — |
case-04 | fail→fail | 7,837 | 1,848 | -76% | 1 | 1 | 0% | 1,685 | 1,875 | +11% | 0 | 0 | — |
case-05 | fail→pass | 6,076 | 1,978 | -67% | 1 | 1 | 0% | 1,178 | 1,916 | +63% | 0 | 0 | — |
case-06 | fail→pass | 10,006 | 2,910 | -71% | 1 | 1 | 0% | 2,029 | 2,157 | +6% | 0 | 0 | — |
case-12 | pass→pass | 2,035 | 1,144 | -44% | 1 | 1 | 0% | 360 | 1,648 | +358% | 0 | 0 | — |
case-07 | fail→pass | 7,559 | 1,764 | -77% | 1 | 1 | 0% | 1,534 | 1,903 | +24% | 0 | 0 | — |
case-08 | pass→pass | 7,192 | 2,446 | -66% | 1 | 1 | 0% | 1,418 | 2,016 | +42% | 0 | 0 | — |
case-09 | pass→pass | 2,670 | 4,077 | +53% | 1 | 1 | 0% | 547 | 2,411 | +341% | 0 | 0 | — |
case-10 | fail→pass | 10,470 | 2,091 | -80% | 1 | 1 | 0% | 1,975 | 1,999 | +1% | 0 | 0 | — |
case-11 | pass→pass | 6,410 | 6,088 | -5% | 1 | 1 | 0% | 1,285 | 2,782 | +116% | 0 | 0 | — |
case-13 | fail→pass | 6,249 | 1,182 | -81% | 1 | 1 | 0% | 1,322 | 1,648 | +25% | 0 | 0 | — |
case-14 | fail→pass | 3,568 | 1,168 | -67% | 1 | 1 | 0% | 580 | 1,689 | +191% | 0 | 0 | — |
case-15 | pass→pass | 4,615 | 1,068 | -77% | 1 | 1 | 0% | 776 | 1,696 | +119% | 0 | 0 | — |
case-16 | pass→pass | 3,827 | 1,218 | -68% | 1 | 1 | 0% | 664 | 1,661 | +150% | 0 | 0 | — |
case-22 | pass→pass | 8,109 | 5,166 | -36% | 1 | 1 | 0% | 1,516 | 2,469 | +63% | 0 | 0 | — |
case-17 | pass→pass | 4,184 | 1,151 | -72% | 1 | 1 | 0% | 746 | 1,723 | +131% | 0 | 0 | — |
case-18 | pass→pass | 8,410 | 4,642 | -45% | 1 | 1 | 0% | 1,843 | 2,641 | +43% | 0 | 0 | — |
case-19 | fail→pass | 10,804 | 5,942 | -45% | 1 | 1 | 0% | 2,384 | 3,021 | +27% | 0 | 0 | — |
case-20 | fail→pass | 9,610 | 3,491 | -64% | 1 | 1 | 0% | 1,957 | 2,316 | +18% | 0 | 0 | — |
case-21 | pass→pass | 9,035 | 13,747 | +52% | 1 | 1 | 0% | 947 | 3,009 | +218% | 0 | 0 | — |
case-23 | pass→pass | 10,320 | 7,037 | -32% | 1 | 1 | 0% | 2,343 | 3,119 | +33% | 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. 23 cases were attempted. The headline lift of +48 percentage points is the difference between those two pass rates over the 23 comparable cases.
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.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 8/7/2026 | +57% |
Other measured skills in the registry, with their headline benchmark lift.