hf-model-card-research

hf-model-card-research is a skill for Claude Code, Codex from AtlasOmnia/hermes-custom-pack. It costs 35 tokens per session (2,585 once invoked), scanned A, a copy of hf-model-card-research, MIT.

A research guide for reading Hugging Face model cards, which are pages describing downloadable AI models. It extracts details such as downloads, likes, claimed benchmarks, file sizes, and author statements.

In plain words
What is it for?
It helps compare model versions, check benchmark claims, find file sizes and popularity figures, and survey models from particular authors or families.
Why use it?
It puts comparable information about model variants in one structured view and distinguishes author claims from other evidence.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit It helps compare model versions, check benchmark claims, find file sizes and popularity figures, and survey models from particular authors or families.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/atlasomnia/hermes-custom-pack/hf-model-card-research
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.

Any agent
npx skills add AtlasOmnia/hermes-custom-pack --skill hf-model-card-research
Clone the repo
git clone --depth 1 https://github.com/AtlasOmnia/hermes-custom-pack

Made for: Claude Code, Codex.

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 hf-model-card-research

README.md
[![agentmods](https://agentmods.dev/badge/skills/atlasomnia/hermes-custom-pack/hf-model-card-research/github.svg)](https://agentmods.dev/skills/atlasomnia/hermes-custom-pack/hf-model-card-research)
Your own site
<a href="https://agentmods.dev/skills/atlasomnia/hermes-custom-pack/hf-model-card-research"><img src="https://agentmods.dev/badge/skills/atlasomnia/hermes-custom-pack/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.

agentmods 80×15 button for hf-model-card-research

Your own site · 80×15
<a href="https://agentmods.dev/skills/atlasomnia/hermes-custom-pack/hf-model-card-research"><img src="https://agentmods.dev/badge/skills/atlasomnia/hermes-custom-pack/hf-model-card-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,585 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% 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 $0.00035 $0.02585
Opus 5 $0.00017 $0.01293
Sonnet 5 $0.00007 $0.00517
Haiku 4.5 $0.00003 $0.00259

Measured 11d ago against content hash e401d4224929, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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 11d 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
Origin

This is a copy

100% identical to hf-model-card-research — 0 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/hf-model-card-research/SKILL.md · 187 lines

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 count
  • likes — like count
  • pipeline_tag — model type (text-generation, image-text-to-text)
  • tags, cardData, config, gguf, safetensors, siblings (file list), createdAt/lastModified
  • spaces (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.

Read the full file on GitHub · 187 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. 11d ago First seen · 187 lines · 35 tokens per session scan A e401d4224929

Subscribe to this mod's changes

hf-model-card-research is a skill published in the GitHub repository AtlasOmnia/hermes-custom-pack (56 stars, last pushed 27d 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). It is 100% identical to hf-model-card-research, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

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.".

AtlasOmnia/donna-starter · 35 tokens

yao-geo-brand-graph

A method for building a brand entity knowledge graph from company information, websites, products, customers, people, places, evidence sources, and use cases. A knowledge graph records entities and the supported relationships between them.

yaojingang/yao-geo-skills · 67 tokens

parse-extract-input

For text processing: extract numbers, words, structured data from messy text using regex patterns, parsing utilities.

jimmc414/claude-code-plugin-marketplace · 26 tokens

gh-issues

Fetch GitHub issues, spawn sub-agents to implement fixes and open PRs, then monitor and address PR review comments. Usage: /gh-issues [owner/repo] [--label bug] [--limit 5] [--milestone v1.0] [--assignee @me] [--fork user/repo] [--watch] [--interval 5] [--reviews-only] [--cron] [--dry-run] [--model glm-5]…

SafeAI-Lab-X/ClawKeeper · 116 tokens

litellm-vertex-proxy-repair

Diagnose and repair a local LiteLLM + Vertex AI proxy on macOS, especially when http://127.0.0.1:4000/ or /v1 is down, startup hangs at Waiting for application startup, /ui/login/ says Authentication Error, Not connected to DB!, or Prisma/PostgreSQL issues need to be isolated from the API proxy by splitting lite and…

davidtoby/agent-skills · 102 tokens

oracle

Best practices for using the oracle CLI (prompt + file bundling, engines, sessions, and file attachment patterns).

SafeAI-Lab-X/ClawKeeper · 25 tokens