huggingface-datasets
Apply hugging face datasets in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation.
Skill by sandbaseai
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/sandbaseai-huggingface-datasets
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: data, ml, marketing, productivity
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/huggingface-datasets
What's inside
- Skill name
huggingface-datasets- Description
- Apply hugging face datasets in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses hugging face datasets or its strengths fit the task.
- License
- Apache-2.0
- Instructions
- 43 lines in SKILL.md · view on GitHub
Files (2)
- SKILL.md
- sandbase-api-map.md · references/
SKILL.md (first lines)
# Hugging Face Datasets Use this Skill to produce a bounded, verifiable Hugging Face Datasets outcome. Preserve the user's chosen stack, source material, and authorization boundaries. Read [the SandBase API map](references/sandbase-api-map.md) only when the task genuinely needs an external data source or generative model. ## Workflow 1. Inspect the available files, runtime, versions, inputs, and existing conventions before deciding what to change. 2. Restate the requested outcome, constraints, acceptance checks, and any assumption that could change the result. 3. Produce the smallest complete implementation, analysis, or artifact that satisfies those checks. 4. Verify the real output with appropriate tests, previews, calculations, or source comparison; do not infer success from file creation alone. 5. Return the deliverable, evidence of validation, material assumptions, and unresolved limitations. ## Quality gates - Inspect shapes, types, units, missing values, sampling, target leakage, and train/test boundaries before modeling or transformation. - Pin or record relevant library versions, random seeds, parameters, and environment assumptions for reproducibility. - Validate against a baseline or independent calculation and report diagnostics, uncertainty, failure modes, and resource use. ## Focus checks - Verify dataset card, license, configuration, split, feature schema, revision, streaming and cache behavior, and avoid assuming viewer samples represent the full dataset. ## SandBase boundary Keep the core Hugging Face Datasets work local. Use SandBase only for an explicitly requested external dataset or model inference step that is not part of the local analysis. 1. Call `sandbase_discover` with a short capability query. 2. Call `sandbase_inspect` for viable candidates and compare the live schema, coverage, limits, output, execution mode, and price. 3. Prefer a dedicated tool or API the user already has. Send only the minimum necessary data. 4. Before any paid call, show the endpoint, important arguments, current unit price, call count, and total estimate or uncertainty, then obtain confirmation. 5. Use `sandbase_account` before an approved multi-call batch and call `sandbase_run` only with current schema-defined arguments. 6. Poll asynchronous work with `sandbase_run_get` using the same run ID; never resubmit merely because it is pending. 7. Use `sandbase_runs` only to recover status or reconcile observed cost. If SandBase is unavailable, continue with local work and authorized sources when possible. Do not silently switch providers, fabricate external results, or claim a generation or retrieval succeeded. ## Handoff Provide the completed artifact or findings, concise reproduction steps, checks actually run, source or asset provenance, SandBase endpoint and run IDs when used, observed cost when available, and any follow-up that still requires user action.
From sandbaseai/sandbase-skills, Apache-2.0.
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Signals
- Stars
- 201
- Forks
- 21
- Open issues
- 1
- Last commit (this folder)
- Sep 8, 2026 (1 month ago)
- Last release
- Aug 20, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/sandbaseai/sandbase-skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
huggingface-datasets skill. Agent Mart. https://agentmart.dev/skills/sandbaseai-huggingface-datasets (last updated 2026-10-24)