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 AtlasOmnia/donna-starter --skill hf-model-card-researchgit clone --depth 1 https://github.com/AtlasOmnia/donna-starterWrote 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/atlasomnia/donna-starter/hf-model-card-research)<a href="https://agentmods.dev/skills/atlasomnia/donna-starter/hf-model-card-research"><img src="https://agentmods.dev/badge/skills/atlasomnia/donna-starter/hf-model-card-research/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/atlasomnia/donna-starter/hf-model-card-research"><img src="https://agentmods.dev/badge/skills/atlasomnia/donna-starter/hf-model-card-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00035 | $0.02585 |
| Opus 5 | $0.00017 | $0.01293 |
| Sonnet 5 | $0.00007 | $0.00517 |
| Haiku 4.5 | $0.00003 | $0.00259 |
Grade A, and why
hf-model-card-research 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 12d 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.
**Pitfall — delegated subagents hit terminal guardrails on HF pulls.** Three dispatched subagents (each on different HF research tasks, all variants) hit `same_tool_failure_halt` after 4 repeated terminal retries on fail Copies of this mod
1 near-identical copy found in the catalogue:
- hf-model-card-research — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
HF Model Card Research
Extract structured metadata — downloads, likes, benchmark claims, file sizes, author statements — from HuggingFace model cards. Used when the user asks you to "check these models on HF", "pull benchmarks for these variants", or "compare what authors claim."
When to Use
- User provides a list of authors and model families (e.g. "pull metadata for these Qwen3.6 variants")
- You need to compare benchmark claims across multiple fine-tunes of the same base model
- You need up-to-date download counts, likes, or file sizes for model variants
- Verifying what a model card claims vs what other sources say
- Surveying the ecosystem around a new base model release
Workflow
1. Discover model repo names
Search HF for each author's models using site:huggingface.co queries:
site:huggingface.co <author> <model-family> <variant-keyword>
Try variant-specific keywords: the author's handle, the model name, key terms like "uncensored", "abliterated", "Opus", "NVFP4", "MTP", etc.
For community quantizers (mradermacher, unsloth, byteshape), search with: site:huggingface.co <author> Qwen3.6-27B.
If search results are sparse, try the author's HF profile page directly: https://huggingface.co/<author>/models — use web_extract on that.
2. Pull structured stats via HF API
The HF API endpoint returns the metadata you need. Pull JSON, don't scrape the web view for stats:
https://huggingface.co/api/models/{owner}/{repo}
Returns JSON with:
downloads— download countlikes— like countpipeline_tag— model type (text-generation, image-text-to-text)tags,cardData,config,gguf,safetensors,siblings(file list),createdAt/lastModifiedspaces(linked HF Spaces)model-index(evaluation results when present)
Pitfall — delegated subagents hit terminal guardrails on HF pulls. Three dispatched subagents (each on different HF research tasks, all variants) hit same_tool_failure_halt after 4 repeated terminal retries on failed HF calls. The failure mode is predictable: subagents loop on the same failing terminal (curl to HF API) because they think "retry the same call until it works." The per-turn tool guardrail halts them after 4 non-progressing attempts with zero data produced. This happened across three separate delegations in the same session.
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.
- 12d ago First seen · 187 lines · 35 tokens per session scan A e401d4224929
hf-model-card-research is a skill published in the GitHub repository AtlasOmnia/donna-starter (111 stars, last pushed 12d ago), licensed MIT. It adds 35 tokens to every session and 2,585 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 2026-08-30.
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