agentmods.dev

huggingface-datasets

huggingface-datasets is a skill for Claude Code, Codex from waybarrios/opencode-power-pack. It costs 43 tokens per session (1,151 once invoked), scanned A, a copy of huggingface-datasets, MIT.

A workflow for exploring and extracting data through the Hugging Face Dataset Viewer API. The API provides metadata, sample rows, searches, filters, downloadable Parquet files, and dataset statistics without requiring the dataset to be loaded locally.

In plain words
What is it for?
Use it to validate a dataset, find its subsets and splits, preview or search rows, apply filters, retrieve Parquet links, and check totals or statistics. Private or gated datasets require a Hugging Face access token.
Why use it?
It helps inspect a dataset's available configurations, splits, rows, and size before using it. It also supports paginated retrieval so large results can be examined in smaller requests.

Skill for Claude CodeCodex

Part of the opencode-power-pack plugin — 54 skills shipped together

not rated 490 +1 3d ago A scan 43 tokens copy · 98% MIT
View the source file waybarrios/opencode-power-pack skills.waybarrios.com
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/waybarrios/opencode-power-pack/huggingface-datasets
Any agent
npx skills add waybarrios/opencode-power-pack --skill huggingface-datasets
Clone the repo
git clone --depth 1 https://github.com/waybarrios/opencode-power-pack

Made for: Claude Code, Codex.

Or install opencode-power-pack, the plugin that ships this one along with the rest of its 54 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for huggingface-datasets

README.md
[![agentmods](https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/huggingface-datasets.svg)](https://agentmods.dev/skills/waybarrios/opencode-power-pack/huggingface-datasets)
Your own site
<a href="https://agentmods.dev/skills/waybarrios/opencode-power-pack/huggingface-datasets"><img src="https://agentmods.dev/badge/skills/waybarrios/opencode-power-pack/huggingface-datasets.svg" alt="Measured on agentmods" height="20"></a>
Per session 43 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,151 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin 98% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00043 $0.01151
Opus 5 $0.00022 $0.00575
Sonnet 5 $0.00009 $0.00230
Haiku 4.5 $0.00004 $0.00115

Measured 5d ago against content hash fde3aae0abfa, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

huggingface-datasets scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl "https://datasets-server.huggingface.co/rows?dataset=stanfordnlp/imdb&config=plain_text&split=train&offset=0&length=100"
Origin

This is a copy

98% identical to huggingface-datasets — 215 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/huggingface-datasets/SKILL.md · 109 lines

How it starts

The opening of the file, as written. The whole thing — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Hugging Face Dataset Viewer

Use this skill to execute read-only Dataset Viewer API calls for dataset exploration and extraction.

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.

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>.

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"

Read the full file on GitHub · 109 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 109 lines · 43 tokens per session scan A fde3aae0abfa

Subscribe to this mod's changes

huggingface-datasets is a skill published in the GitHub repository waybarrios/opencode-power-pack (490 stars, last pushed 3d ago), licensed MIT. It adds 43 tokens to every session and 1,151 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 98% identical to huggingface-datasets, differing in 215 lines, and is treated as a copy.

Backend and APIsCode reviewData and AIResearchai-agentsanthropicclaude-codedeveloper-toolsllm-tools
Related

Other skills, from other repositories

llm-wiki

The foundational knowledge distillation pattern for building and maintaining an AI-powered Obsidian wiki. Based on Andrej Karpathy's LLM Wiki architecture. Use this skill whenever the user wants to understand the wiki pattern, set up a new knowledge base, or needs guidance on the three-layer architecture (raw sources…

Ar9av/obsidian-wiki · 113 tokens

wiki-export

Export the Obsidian wiki's knowledge graph to structured formats for use in external tools. Use this skill when the user says "export wiki", "export graph", "export to JSON", "export to Gephi", "export to Neo4j", "export to Postgres", "export to SQL", "graphml", "visualize wiki", "knowledge graph export", "export to…

Ar9av/obsidian-wiki · 172 tokens

llm-as-judge-evaluation

Evaluate LLM outputs using frontier models as judges. Use for pairwise model comparison, quality scoring with custom rubrics, and automated evaluation pipelines. Covers position bias mitigation, statistical significance, and generating preference data for DPO/RLHF.

synthetic-sciences/openscience · 56 tokens

transformers

This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning…

synthetic-sciences/openscience · 63 tokens

generate-image

Generate or edit images using AI models (FLUX, Gemini). Use for general-purpose image generation including photos, illustrations, artwork, visual assets, concept art, and any image that isn't a technical diagram or schematic. For flowcharts, circuits, pathways, and technical diagrams, use the scientific-schematics…

synthetic-sciences/openscience · 67 tokens

gemini-webhooks

Receive and verify Google Gemini API webhooks. Use when setting up Gemini webhook handlers for batch jobs, video generation, or Interactions API function-calling LROs, debugging signature verification, or handling events like batch.succeeded, batch.failed, video.generated, or interaction.completed.

hookdeck/webhook-skills · 61 tokens