Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Manage open science projects and preprints via the OSF REST API
.claude/skills/brycewang-stanford-osf-api/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 57% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 66% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-03 | ✓→✓ | = Same ✓ | 38% | 0% |
| case-04 | ✓→✓ | = Same ✓ | 25% | 0% |
The Open Science Framework by the Center for Open Science provides infrastructure for the entire research lifecycle — project management, file storage, preprint hosting, and registrations. The API enables search, project creation, file management, and preprint discovery across OSF Preprints, PsyArXiv, SocArXiv, and 25+ community preprint servers. Free, no auth for read access.
https://api.osf.io/v2bash# Search across all OSF content curl "https://api.osf.io/v2/search/?q=replication+crisis&page[size]=20" # Search preprints curl "https://api.osf.io/v2/preprints/?filter[q]=machine+learning&page[size]=20" # Filter by preprint provider curl "https://api.osf.io/v2/preprints/?filter[provider]=psyarxiv&filter[q]=cognitive+bias" # Search registrations (pre-registered studies) curl "https://api.osf.io/v2/registrations/?filter[q]=randomized+controlled+trial"
bash# Get public projects curl "https://api.osf.io/v2/nodes/?filter[public]=true&filter[q]=neuroimaging" # Get project details curl "https://api.osf.io/v2/nodes/{node_id}/" # Get project files curl "https://api.osf.io/v2/nodes/{node_id}/files/" # Get project contributors curl "https://api.osf.io/v2/nodes/{node_id}/contributors/"
| Provider | Filter | Disciplines | |----------|--------|-------------| | OSF Preprints | osf | Multidisciplinary | | PsyArXiv | psyarxiv | Psychology | | SocArXiv | socarxiv | Social sciences | | EarthArXiv | eartharxiv | Earth sciences | | BioHackrXiv | biohackrxiv | Bioinformatics | | engrXiv | engrxiv | Engineering | | MedArXiv | medarxiv | Medical sciences | | NutriXiv | nutrixiv | Nutrition |
| Parameter | Description | Example | |-----------|-------------|---------| | filter[q] | Text search | filter[q]=open+data | | filter[provider] | Preprint server | filter[provider]=psyarxiv | | filter[subjects] | Subject filter | Subject taxonomy ID | | filter[date_created] | Date filter | filter[date_created][gte]=2024-01-01 | | page[size] | Results per page (max 100) | page[size]=50 | | page | Page number | page=2 |
json{ "data": [ { "id": "abc12", "type": "preprints", "attributes": { "title": "Replication of the Ego Depletion Effect", "description": "We attempted to replicate...", "date_created": "2024-06-15T10:00:00Z", "date_published": "2024-06-16T08:00:00Z", "doi": "10.31234/osf.io/abc12", "is_published": true, "subjects": [["Social and Behavioral Sciences", "Psychology"]], "tags": ["replication", "ego depletion"] }, "relationships": { "contributors": {"links": {"related": {"href": "..."}}}, "primary_file": {"links": {"related": {"href": "..."}}} } } ] }
pythonimport requests BASE_URL = "https://api.osf.io/v2" def search_preprints(query: str, provider: str = None, page_size: int = 20) -> list: """Search OSF preprints across providers.""" params = { "filter[q]": query, "page[size]": page_size, } if provider: params["filter[provider]"] = provider resp = requests.get(f"{BASE_URL}/preprints/", params=params) resp.raise_for_status() data = resp.json() results = [] for item in data.get("data", []): attrs = item.get("attributes", {}) results.append({ "id": item.get("id"), "title": attrs.get("title"), "description": (attrs.get("description") or "")[:300], "doi": attrs.get("doi"), "date": attrs.get("date_published", "")[:10], "tags": attrs.get("tags", []), "url": f"https://osf.io/{item.get('id')}/", }) return results def search_registrations(query: str, page_size: int = 20) -> list: """Search pre-registered studies on OSF.""" params = { "filter[q]": query, "page[size]": page_size, } resp = requests.get(f"{BASE_URL}/registrations/", params=params) resp.raise_for_status() data = resp.json() results = [] for item in data.get("data", []): attrs = item.get("attributes", {}) results.append({ "id": item.get("id"), "title": attrs.get("title"), "description": (attrs.get("description") or "")[:300], "date_registered": attrs.get("date_registered", "")[:10], "registration_schema": attrs.get("registration_supplement"), }) return results def get_project_files(node_id: str) -> list: """List files in an OSF project.""" resp = requests.get(f"{BASE_URL}/nodes/{node_id}/files/") resp.raise_for_status() data = resp.json() providers = [] for item in data.get("data", []): attrs = item.get("attributes", {}) providers.append({ "provider": attrs.get("provider"), "name": attrs.get("name"), }) return providers # Example: search psychology preprints preprints = search_preprints("cognitive load", provider="psyarxiv") for p in preprints[:5]: print(f"[{p['date']}] {p['title']}") print(f" DOI: {p['doi']}") # Example: find pre-registered clinical trials regs = search_registrations("randomized placebo") for r in regs[:5]: print(f"[{r['date_registered']}] {r['title']}")
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-03 | pass→pass | 11,493 | 5,688 | -51% | 1 | 1 | 0% | 2,128 | 2,941 | +38% | 0 | 0 | — |
case-01 | fail→pass | 12,193 | 15,652 | +28% | 1 | 1 | 0% | 2,158 | 3,392 | +57% | 0 | 0 | — |
case-02 | fail→pass | 14,950 | 7,785 | -48% | 1 | 1 | 0% | 1,993 | 3,311 | +66% | 0 | 0 | — |
case-04 | pass→pass | 12,276 | 3,820 | -69% | 1 | 1 | 0% | 2,043 | 2,555 | +25% | 0 | 0 | — |
case-05 | fail→pass | 12,933 | 7,249 | -44% | 1 | 1 | 0% | 2,177 | 3,037 | +40% | 0 | 0 | — |
case-06 | pass→pass | 6,510 | 2,718 | -58% | 1 | 1 | 0% | 1,176 | 2,380 | +102% | 0 | 0 | — |
case-07 | pass→pass | 10,781 | 7,027 | -35% | 1 | 1 | 0% | 1,826 | 3,116 | +71% | 0 | 0 | — |
case-08 | pass→pass | 10,800 | 6,611 | -39% | 1 | 1 | 0% | 1,863 | 3,142 | +69% | 0 | 0 | — |
case-09 | pass→pass | 7,338 | 5,295 | -28% | 1 | 1 | 0% | 1,296 | 2,838 | +119% | 0 | 0 | — |
case-10 | pass→pass | 7,357 | 2,672 | -64% | 1 | 1 | 0% | 1,210 | 2,317 | +91% | 0 | 0 | — |
case-11 | pass→pass | 9,341 | 5,619 | -40% | 1 | 1 | 0% | 1,597 | 2,884 | +81% | 0 | 0 | — |
case-12 | pass→pass | 9,662 | 4,507 | -53% | 1 | 1 | 0% | 1,559 | 2,493 | +60% | 0 | 0 | — |
case-13 | pass→pass | 3,386 | 2,198 | -35% | 1 | 1 | 0% | 521 | 2,214 | +325% | 0 | 0 | — |
case-14 | pass→pass | 9,352 | 7,826 | -16% | 1 | 1 | 0% | 1,693 | 3,235 | +91% | 0 | 0 | — |
case-15 | pass→pass | 5,068 | 4,421 | -13% | 1 | 1 | 0% | 885 | 2,608 | +195% | 0 | 0 | — |
case-16 | pass→pass | 8,784 | 6,664 | -24% | 1 | 1 | 0% | 1,528 | 2,996 | +96% | 0 | 0 | — |
case-17 | pass→pass | 9,636 | 9,807 | +2% | 1 | 1 | 0% | 1,629 | 3,561 | +119% | 0 | 0 | — |
case-18 | pass→pass | 14,541 | 2,932 | -80% | 1 | 1 | 0% | 2,556 | 2,364 | -8% | 0 | 0 | — |
case-19 | pass→pass | 7,630 | 4,507 | -41% | 1 | 1 | 0% | 1,297 | 2,620 | +102% | 0 | 0 | — |
case-20 | pass→pass | 2,293 | 1,197 | -48% | 1 | 1 | 0% | 347 | 1,967 | +467% | 0 | 0 | — |
case-21 | pass→pass | 7,530 | 11,322 | +50% | 1 | 1 | 0% | 1,486 | 4,065 | +174% | 0 | 0 | — |
case-22 | pass→pass | 9,184 | 9,369 | +2% | 1 | 1 | 0% | 1,695 | 3,675 | +117% | 0 | 0 | — |
case-23 | pass→pass | 12,492 | 10,668 | -15% | 1 | 1 | 0% | 2,380 | 4,138 | +74% | 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 +13 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.
Other measured skills in the registry, with their headline benchmark lift.