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.
npx skills add PracticalSwan/agent-skills --skill huggingface-tool-buildergit clone --depth 1 https://github.com/PracticalSwan/agent-skillsWrote 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-tool-builder)<a href="https://agentmods.dev/skills/practicalswan/agent-skills/huggingface-tool-builder"><img src="https://agentmods.dev/badge/skills/practicalswan/agent-skills/huggingface-tool-builder.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00067 | $0.01878 |
| Opus 5 | $0.00034 | $0.00939 |
| Sonnet 5 | $0.00013 | $0.00376 |
| Haiku 4.5 | $0.00007 | $0.00188 |
Grade A, and why
huggingface-tool-builder 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 yesterday.
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.
- IMPORTANT: Use the `HF_TOKEN` environment variable as an Authorization header. For example: `curl -H "Authorization: Bearer ${HF_TOKEN}" https://huggingface.co/api/`. This provides higher rate limits and appropriate au This is a copy
88% identical to hugging-face-tool-builder — 281 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.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hugging Face API Tool Builder
Your purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the hf command line tool. Model and Dataset cards can be accessed from repositories directly.
Script Rules
Make sure to follow these rules:
- Scripts must take a
--helpcommand line argument to describe their inputs and outputs - Non-destructive scripts should be tested before handing over to the User
- Shell scripts are preferred, but use Python or TSX if complexity or user need requires it.
- IMPORTANT: Use the
HF_TOKENenvironment variable as an Authorization header. For example:curl -H "Authorization: Bearer ${HF_TOKEN}" https://huggingface.co/api/. This provides higher rate limits and appropriate authorization for data access. - Investigate the shape of the API results before commiting to a final design; make use of piping and chaining where composability would be an advantage - prefer simple solutions where possible.
- Share usage examples once complete.
Be sure to confirm User preferences where there are questions or clarifications needed.
Sample Scripts
Paths below are relative to this skill directory.
Reference examples:
references/hf_model_papers_auth.sh— usesHF_TOKENautomatically and chains trending → model metadata → model card parsing with fallbacks; it demonstrates multi-step API usage plus auth hygiene for gated/private content.references/find_models_by_paper.sh— optionalHF_TOKENusage via--token, consistent authenticated search, and a retry path when arXiv-prefixed searches are too narrow; it shows resilient query strategy and clear user-facing help.references/hf_model_card_frontmatter.sh— uses thehfCLI to download model cards, extracts YAML frontmatter, and emits NDJSON summaries (license, pipeline tag, tags, gated prompt flag) for easy filtering.
What ships with it
9 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.
- CHANGELOG.md 2.7 KB
- LICENSE.txt 11 KB
- references/baseline_hf_api.py 1.3 KB runs code
- references/baseline_hf_api.sh 707 B runs code
- references/baseline_hf_api.tsx 1.2 KB
- references/find_models_by_paper.sh 7.5 KB runs code
- references/hf_enrich_models.sh 2.2 KB runs code
- references/hf_model_card_frontmatter.sh 4.4 KB runs code
- references/hf_model_papers_auth.sh 4.8 KB runs code
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.
- yesterday Changed 57e949e49580
- 4d ago First seen · 172 lines · 67 tokens per session scan A d8da396603ad
huggingface-tool-builder is a skill published in the GitHub repository PracticalSwan/agent-skills (13 stars, last pushed 2d ago), licensed MIT. It adds 67 tokens to every session and 1,878 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 88% identical to hugging-face-tool-builder, differing in 281 lines, and is treated as a copy.
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