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 cafe3310/public-agent-skills --skill hugging-face-statgit clone --depth 1 https://github.com/cafe3310/public-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/cafe3310/public-agent-skills/hugging-face-stat)<a href="https://agentmods.dev/skills/cafe3310/public-agent-skills/hugging-face-stat"><img src="https://agentmods.dev/badge/skills/cafe3310/public-agent-skills/hugging-face-stat/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/cafe3310/public-agent-skills/hugging-face-stat"><img src="https://agentmods.dev/badge/skills/cafe3310/public-agent-skills/hugging-face-stat.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 | $0.00020 | $0.00853 |
| Opus 5 | $0.00010 | $0.00426 |
| Sonnet 5 | $0.00004 | $0.00171 |
| Haiku 4.5 | $0.00002 | $0.00085 |
Grade A, and why
hugging-face-stat 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 13d 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 What it actually says
Skill: hugging-face-stat
1. 概述 (Overview)
Hugging Face 官方页面默认主要展示“过去 30 天的下载量”。此技能通过底层的 API 扩展参数，允许大语言模型获取任意公开模型或数据集的历史总下载量 (downloadsAllTime)，以及获取 Space 的运行硬件、点赞数与状态，并能汇总查询组织下所有模型的数据。
此技能提供了一个可靠的 Bash 脚本（hf_stats.sh），自带错误处理、超时机制和数值美化。
2. 核心功能与使用方法 (Core Capabilities & Usage)
本技能提供了一个现成的 Bash 脚本 hf_stats.sh。当你需要查询 Hugging Face 数据时，必须直接调用该脚本。
脚本路径
<path_to_skill>/hf_stats.sh
场景 A: 查询模型或数据集 (Model / Dataset)
当你需要查询一个模型或数据集的详细下载数据时：
# 查询模型
<path_to_skill>/hf_stats.sh model <repo_id>
# 查询数据集
<path_to_skill>/hf_stats.sh dataset <repo_id>
返回数据包括：库 ID、作者、创建时间、最近30天下载、历史总下载量、点赞数。
场景 B: 查询空间 (Space)
当你需要了解一个 Space 的热度与运行环境时：
<path_to_skill>/hf_stats.sh space <repo_id>
返回数据包括：库 ID、运行状态、硬件规格（如 T4 medium）、SDK 类型（如 Gradio）、点赞数。
场景 C: 查询组织 (Organization)
当你需要汇总查询一个组织下所有模型的统计数据时：
<path_to_skill>/hf_stats.sh org <org_name>
返回数据包括：组织模型总数、最近30天总下载、总点赞数，并按下载量降序排列前20个热门模型。
3. 工作流 (Workflow)
- 意图识别: 当用户询问“某个模型的总下载量”、“这个模型是什么时候创建的”、“这个 Space 跑在什么硬件上”或“这个组织一共有多少个模型”时，触发此技能。
- 提取参数: 提取目标库的
repo_id和类型（model,dataset,space,org）。 - 执行工具: 使用
Bash工具运行本技能目录下的hf_stats.sh脚本。 - 解析与回复:
- 将脚本返回的格式化结果整理成自然语言。
- 强调历史总下载量，这是用户最关注且网页端较难直接获取的数据。
- 提及 Space 的硬件规格和 SDK，帮助用户评估其运行性能。
- 如果是查询 Space 的访问量，必须向用户澄清：“Hugging Face 官方未公开 Space 的实时访问量数据，目前通过点赞数和硬件规格来评估其热度和资源投入”。
4. 限制说明
- Hugging Face API 仅提供“过去30天”和“历史总和”两个快照。无法直接回溯任意日期的历史趋势折线图。
- 组织查询受 API 汇总限制，无法直接获取组织级的“历史总下载量”累加值，仅显示最近30天累加值。
- 如果查询遇到 404，提醒用户检查 ID 是否正确或仓库是否为私有。
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.
- 13d ago First seen · 66 lines · 20 tokens per session scan A 4e71e7afa995
hugging-face-stat is a skill published in the GitHub repository cafe3310/public-agent-skills (253 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 20 tokens to every session and 853 once invoked, about $0.0001 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.
Other skills, from other repositories
matplotlib
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for…
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file…
dask
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed…
lamindb
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological…
alphafold-database
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…