Hugging Face Dataset Viewer V2

Hugging Face Dataset Viewer

Overview

This public intake copy packages plugins/antigravity-awesome-skills/skills/hugging-face-dataset-viewer from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Hugging Face Dataset Viewer

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Defaults, Dataset Viewer, Querying Datasets, Creating and Uploading Datasets, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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.
• Use when the request clearly matches the imported source intent: Query Hugging Face datasets through the Dataset Viewer API for splits, rows, search, filters, and parquet links.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Optionally validate dataset availability with /is-valid.
2. Resolve config + split with /splits.
3. Preview with /first-rows.
4. Paginate content with /rows using offset and length (max 100).
5. Use /search for text matching and /filter for row predicates.
6. Retrieve parquet links via /parquet and totals/metadata via /size and /statistics.
7. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.

Imported Workflow Notes

Imported: Core workflow

1. Optionally validate dataset availability with /is-valid.
2. Resolve config + split with /splits.
3. Preview with /first-rows.
4. Paginate content with /rows using offset and length (max 100).
5. Use /search for text matching and /filter for row predicates.
6. Retrieve parquet links via /parquet and totals/metadata via /size and /statistics.

Imported: Defaults

• Base URL: https://datasets-server.huggingface.co
• Default API method: GET
• Query params should be URL-encoded.
• offset is 0-based.
• length max is usually 100 for row-like endpoints.
• Gated/private datasets require Authorization: Bearer <HF_TOKEN>.

Examples

Example 1: Ask for the upstream workflow directly

Use @hugging-face-dataset-viewer-v2 to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @hugging-face-dataset-viewer-v2 against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @hugging-face-dataset-viewer-v2 for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @hugging-face-dataset-viewer-v2 using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills/skills/hugging-face-dataset-viewer, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: Dataset Viewer

• 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:

curl "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.
• Keep filtering and searches read-only and side-effect free.

Imported: Querying Datasets

Use npx parquetlens with Hub parquet alias paths for SQL querying.

Parquet alias shape:

hf://datasets/<namespace>/<repo>@~parquet/<config>/<split>/<shard>.parquet

Derive <config>, <split>, and <shard> from Dataset Viewer /parquet:

curl -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:

npx -y -p parquetlens -p @parquetlens/sql parquetlens \
  "hf://datasets/<namespace>/<repo>@~parquet/<config>/<split>/<shard>.parquet" \
  --sql "SELECT * FROM data LIMIT 20"

SQL export

• CSV: --sql "COPY (SELECT * FROM data LIMIT 1000) TO 'export.csv' (FORMAT CSV, HEADER, DELIMITER ',')"
• JSON: --sql "COPY (SELECT * FROM data LIMIT 1000) TO 'export.json' (FORMAT JSON)"
• Parquet: --sql "COPY (SELECT * FROM data LIMIT 1000) TO 'export.parquet' (FORMAT PARQUET)"

Imported: Creating and Uploading Datasets

Use one of these flows depending on dependency constraints.

Zero local dependencies (Hub UI):

• Create dataset repo in browser: https://huggingface.co/new-dataset
• Upload parquet files in the repo "Files and versions" page.
• Verify shards appear in Dataset Viewer:

curl -s "https://datasets-server.huggingface.co/parquet?dataset=<namespace>/<repo>"

Low dependency CLI flow (npx @huggingface/hub / hfjs):

• Set auth token:

export HF_TOKEN=<your_hf_token>

• Upload parquet folder to a dataset repo (auto-creates repo if missing):

npx -y @huggingface/hub upload datasets/<namespace>/<repo> ./local/parquet-folder data

• Upload as private repo on creation:

npx -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.

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.