Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/PracticalSwan/agent-skillsnpx agentmods add skills/practicalswan/agent-skills/huggingface-datasetsWrote 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.
[](https://agentmods.dev/skills/practicalswan/agent-skills/huggingface-datasets)<a href="https://agentmods.dev/skills/practicalswan/agent-skills/huggingface-datasets"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/huggingface-datasets/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/practicalswan/agent-skills/huggingface-datasets"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/huggingface-datasets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 111 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
- medium Rogue Agent · line 66 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium MCP Rug Pull · line 74 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 85 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 91 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.01695 |
| Opus 5 | $0.00022 | $0.00847 |
| Sonnet 5 | $0.00009 | $0.00339 |
| Haiku 4.5 | $0.00004 | $0.00169 |
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 3d 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" How it starts
The opening of the file, as written. The whole thing — 159 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
- Optionally validate dataset availability with
/is-valid. - Resolve
config+splitwith/splits. - Preview with
/first-rows. - Paginate content with
/rowsusingoffsetandlength(max 100). - Use
/searchfor text matching and/filterfor row predicates. - Retrieve parquet links via
/parquetand totals/metadata via/sizeand/statistics.
Defaults
- Base URL:
https://datasets-server.huggingface.co - Default API method:
GET - Query params should be URL-encoded.
offsetis 0-based.lengthmax is usually100for 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"
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago Changed 30abf5124225
- 4d ago Changed c1ffc8d9ed37
- 7d ago First seen · 159 lines · 43 tokens per session scan A 8f7c702a7d46
huggingface-datasets is a skill published in the GitHub repository PracticalSwan/agent-skills (14 stars, last pushed 3d ago), licensed MIT. It adds 43 tokens to every session and 1,695 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
huggingface-hub
HuggingFace hf CLI: search/download/upload models, datasets.
hugging-face-datasets
Create and manage datasets on Hugging Face Hub. Supports initializing repos, defining configs/system prompts, streaming row updates, and SQL-based dataset querying/transformation. Designed to work alongside HF MCP server for comprehensive dataset workflows.
huggingface-hub
Hugging Face Hub — model discovery, download, inference, and upload.
huggingface-hub
Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.
huggingface-hub
HuggingFace hf CLI: search/download/upload models, datasets.
huggingface-hub
Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces and buckets.