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Get Started Free →Query Hugging Face datasets through the Dataset Viewer API for splits, rows, search, filters, and parquet links.
.claude/skills/lingxling-hugging-face-dataset-viewer/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-23 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -4% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 13% | 0% |
| case-24 | ✗→✓ | ▲ Improved | 61% | 0% |
| case-19 | ✗→✓ | ▲ Improved | -17% | 0% |
Use this skill when you need read-only exploration of a Hugging Face dataset through the Dataset Viewer API.
Use this skill to execute read-only Dataset Viewer API calls for dataset exploration and extraction.
/is-valid.config + split with /splits./first-rows./rows using offset and length (max 100)./search for text matching and /filter for row predicates./parquet and totals/metadata via /size and /statistics.https://datasets-server.huggingface.coGEToffset is 0-based.length max is usually 100 for row-like endpoints.Authorization: Bearer <HF_TOKEN>.Validate dataset: /is-valid?dataset=<namespace/repo>List subsets and splits: /splits?dataset=<namespace/repo>Preview first rows: /first-rows?dataset=<namespace/repo>&config=<config>&split=<split>Paginate rows: /rows?dataset=<namespace/repo>&config=<config>&split=<split>&offset=<int>&length=<int>Search text: /search?dataset=<namespace/repo>&config=<config>&split=<split>&query=<text>&offset=<int>&length=<int>Filter with predicates: /filter?dataset=<namespace/repo>&config=<config>&split=<split>&where=<predicate>&orderby=<sort>&offset=<int>&length=<int>List parquet shards: /parquet?dataset=<namespace/repo>Get size totals: /size?dataset=<namespace/repo>Get column statistics: /statistics?dataset=<namespace/repo>&config=<config>&split=<split>Get Croissant metadata (if available): /croissant?dataset=<namespace/repo>Pagination pattern:
bashcurl "https://datasets-server.huggingface.co/rows?dataset=stanfordnlp/imdb&config=plain_text&split=train&offset=0&length=100" curl "https://datasets-server.huggingface.co/rows?dataset=stanfordnlp/imdb&config=plain_text&split=train&offset=100&length=100"
When pagination is partial, use response fields such as num_rows_total, num_rows_per_page, and partial to drive continuation logic.
Search/filter notes:
/search matches string columns (full-text style behavior is internal to the API)./filter requires predicate syntax in where and optional sort in orderby.Use npx parquetlens with Hub parquet alias paths for SQL querying.
Parquet alias shape:
texthf://datasets/<namespace>/<repo>@~parquet/<config>/<split>/<shard>.parquet
Derive <config>, <split>, and <shard> from Dataset Viewer /parquet:
bashcurl -s "https://datasets-server.huggingface.co/parquet?dataset=cfahlgren1/hub-stats" \ | jq -r '.parquet_files[] | "hf://datasets/\(.dataset)@~parquet/\(.config)/\(.split)/\(.filename)"'
Run SQL query:
bashnpx -y -p parquetlens -p @parquetlens/sql parquetlens \ "hf://datasets/<namespace>/<repo>@~parquet/<config>/<split>/<shard>.parquet" \ --sql "SELECT * FROM data LIMIT 20"
--sql "COPY (SELECT * FROM data LIMIT 1000) TO 'export.csv' (FORMAT CSV, HEADER, DELIMITER ',')"--sql "COPY (SELECT * FROM data LIMIT 1000) TO 'export.json' (FORMAT JSON)"--sql "COPY (SELECT * FROM data LIMIT 1000) TO 'export.parquet' (FORMAT PARQUET)"Use one of these flows depending on dependency constraints.
Zero local dependencies (Hub UI):
https://huggingface.co/new-datasetbashcurl -s "https://datasets-server.huggingface.co/parquet?dataset=<namespace>/<repo>"
Low dependency CLI flow (npx @huggingface/hub / hfjs):
bashexport HF_TOKEN=<your_hf_token>
bashnpx -y @huggingface/hub upload datasets/<namespace>/<repo> ./local/parquet-folder data
bashnpx -y @huggingface/hub upload datasets/<namespace>/<repo> ./local/parquet-folder data --private
After upload, call /parquet to discover <config>/<split>/<shard> values for querying with @~parquet.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-23 | fail→pass | 13,407 | 8,295 | -38% | 1 | 1 | 0% | 1,776 | 2,458 | +38% | 0 | 0 | — |
case-01 | fail→fail | 17,183 | 28,083 | +63% | 1 | 1 | 0% | 2,252 | 1,741 | -23% | 0 | 0 | — |
case-02 | fail→pass | 30,845 | 8,300 | -73% | 1 | 1 | 0% | 3,065 | 2,954 | -4% | 0 | 0 | — |
case-03 | fail→pass | 10,292 | 6,227 | -39% | 1 | 1 | 0% | 2,055 | 2,321 | +13% | 0 | 0 | — |
case-04 | pass→pass | 11,936 | 16,363 | +37% | 1 | 1 | 0% | 2,434 | 3,455 | +42% | 0 | 0 | — |
case-05 | pass→pass | 10,603 | 10,198 | -4% | 1 | 1 | 0% | 1,982 | 3,322 | +68% | 0 | 0 | — |
case-06 | pass→pass | 11,454 | 10,826 | -5% | 1 | 1 | 0% | 1,802 | 3,090 | +71% | 0 | 0 | — |
case-07 | pass→pass | 11,721 | 8,529 | -27% | 1 | 1 | 0% | 2,231 | 2,989 | +34% | 0 | 0 | — |
case-08 | pass→pass | 6,679 | 3,684 | -45% | 1 | 1 | 0% | 969 | 1,839 | +90% | 0 | 0 | — |
case-09 | pass→pass | 7,888 | 4,977 | -37% | 1 | 1 | 0% | 1,591 | 2,010 | +26% | 0 | 0 | — |
case-10 | pass→pass | 4,571 | 3,107 | -32% | 1 | 1 | 0% | 791 | 1,775 | +124% | 0 | 0 | — |
case-11 | pass→pass | 10,213 | 4,367 | -57% | 1 | 1 | 0% | 1,585 | 2,094 | +32% | 0 | 0 | — |
case-12 | pass→pass | 13,103 | 10,837 | -17% | 1 | 1 | 0% | 1,907 | 2,278 | +19% | 0 | 0 | — |
case-13 | pass→pass | 10,307 | 5,234 | -49% | 1 | 1 | 0% | 2,119 | 2,378 | +12% | 0 | 0 | — |
case-24 | fail→pass | 8,208 | 4,099 | -50% | 1 | 1 | 0% | 1,175 | 1,887 | +61% | 0 | 0 | — |
case-14 | pass→pass | 5,226 | 4,518 | -14% | 1 | 1 | 0% | 898 | 1,826 | +103% | 0 | 0 | — |
case-15 | pass→pass | 7,841 | 3,927 | -50% | 1 | 1 | 0% | 1,138 | 1,841 | +62% | 0 | 0 | — |
case-16 | pass→pass | 4,495 | 4,378 | -3% | 1 | 1 | 0% | 798 | 1,857 | +133% | 0 | 0 | — |
case-17 | pass→pass | 9,862 | 4,273 | -57% | 1 | 1 | 0% | 1,696 | 1,810 | +7% | 0 | 0 | — |
case-18 | pass→pass | 7,079 | 4,508 | -36% | 1 | 1 | 0% | 1,319 | 1,901 | +44% | 0 | 0 | — |
case-19 | fail→pass | 20,568 | 8,369 | -59% | 1 | 1 | 0% | 3,143 | 2,623 | -17% | 0 | 0 | — |
case-20 | fail→pass | 14,197 | 3,805 | -73% | 1 | 1 | 0% | 2,467 | 2,082 | -16% | 0 | 0 | — |
case-21 | fail→pass | 10,202 | 3,587 | -65% | 1 | 1 | 0% | 1,395 | 1,968 | +41% | 0 | 0 | — |
case-22 | fail→pass | 6,786 | 4,866 | -28% | 1 | 1 | 0% | 1,380 | 1,920 | +39% | 0 | 0 | — |
case-25 | fail→pass | 6,678 | 3,068 | -54% | 1 | 1 | 0% | 896 | 1,903 | +112% | 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. 25 cases were attempted, and 24 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +36 percentage points is the difference between those two pass rates over the 24 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.