Automated summary

Queries Hugging Face's three trending surfaces — models, datasets, spaces — and returns a filtered shortlist where every entry carries a "why notable" justification.

Strengths

Screens out noise: empty repos, forks with no README delta, and aggregator authors with more than 5 trending entries in 24h.
Demotes same-week quantizations into a separate tail section instead of letting them crowd the main ranking.
Requires a concrete reason per entry (architecture, size step, context window, author, license) and explicitly says when there is none.
Flags licenses inline (Apache 2.0, MIT, OpenRAIL-M, custom non-commercial) directly in the output.

Limitations

Entirely dependent on Hugging Face's "trending" sort, whose ranking method is undocumented and may change.
No fact-checking of model cards: authors' claims are passed through as-is.
Thresholds (5 entries in 24h, same-week quantization) are hardcoded and not configurable in the skill.

When to use it

Use it for a daily, compact and justified digest of the models, datasets and demos actually shifting on Hugging Face.

When not to use it

Don't use it for a deep technical evaluation of a specific model (benchmarks, real quality, inference cost), which this skill does not perform.

Security analysis

Safe

Quality score86/100

The skill is a read-only aggregator over Hugging Face's public trending APIs. All three curl calls are unauthenticated GETs to huggingface.co with no secret material, no piping into a shell, no filesystem or environment mutation. There is no meaningful execution risk beyond ordinary network egress.

Findings

•Uses curl to public Hugging Face API endpoints (GET only, no auth headers or tokens)
•No file writes, no destructive commands, no credential access, no obfuscation
•Pipes output only to stdout for inspection; no curl|sh style execution

How this analysis is made

Examples

Daily HF scan

What's trending on Hugging Face today? Give me the notable models, datasets and spaces with a why-notable line and license for each.

Builder-tier signal

Show me the new Hugging Face spaces from today — I care more about working demos than model cards.

Dataset discovery

Scan Hugging Face for the best new datasets this week, filter out empty or license-unclear ones, and tell me why each one matters.

name: aeon-huggingface-trending description: | Trending Hugging Face models, datasets, and spaces — filtered by license sanity, dedup vs same-week quantizations, with a "why notable" line per pick (architecture shift, size step, license change, notable author). Surfaces what's actually shifting rather than just popular. Triggers: "trending on HF", "what models are hot", "huggingface trending", "new spaces today", "best new datasets".

aeon-huggingface-trending

Name: HuggingFace Trending Filter
Author: Skills Guide Bot

Daily filtered scan over HF's three trending surfaces — models, datasets, spaces — cluster-ranked rather than raw-download ranked.

Endpoints

curl -s "https://huggingface.co/api/models?sort=trending&direction=-1&limit=30"
curl -s "https://huggingface.co/api/datasets?sort=trending&direction=-1&limit=30"
curl -s "https://huggingface.co/api/spaces?sort=trending&direction=-1&limit=30"

Filters

Empty repos, no commits, no model card → drop.
Same-week quantization of an already-trending model → demoted to "Quantizations" tail.
Fork without README delta vs upstream → drop.
Authors with > 5 trending entries in 24h → demoted (typically aggregators).
License unclear or missing → flagged inline, not dropped.

"Why notable" line

Per surfaced entry, a one-sentence tag: new architecture / size step / context-window jump / notable author affiliation / license change. If no concrete reason exists, the entry says "no clear why — popular but unremarkable" rather than inventing one.

Output

Three sections — Models, Datasets, Spaces — each with the surviving picks, "why notable", license, and download/view count. Tail section for quantizations.

Rules

"Why notable" is a hard requirement. No reason → no surface.
License flags appear inline (Apache 2.0, MIT, OpenRAIL-M, custom-no-commercial). Operators trading on model output care.
Spaces section often beats Models for builder-tier signal — a working demo is stronger validation than a card claim.

From the file to a first result

The download contains exactly the content displayed on this page. This copy may differ from the latest version in the source repository.

View the source and supporting files

This download contains SKILL.md only. Any scripts, references, templates and dependencies are not included. Get the required files from the source, preserving their directory structure, before continuing.

1. Download the file

Download SKILL.md

From the root of a test project, save the file as .skill-downloads/huggingface-trending-filter-mrwakfxh/SKILL.md, or use the command below. This folder is for inspecting the skill before installation.

Command for macOS/Linux or WSL: requires a POSIX shell and curl. On Windows without WSL, use the download button and create the folder manually.

mkdir -p '.skill-downloads/huggingface-trending-filter-mrwakfxh' && curl --fail --show-error --location 'https://skills-guides.com/api/skills/huggingface-trending-filter-mrwakfxh/download' --output '.skill-downloads/huggingface-trending-filter-mrwakfxh/SKILL.md.part' && mv '.skill-downloads/huggingface-trending-filter-mrwakfxh/SKILL.md.part' '.skill-downloads/huggingface-trending-filter-mrwakfxh/SKILL.md'

2. Check prerequisites

Read SKILL.md before using it. If it references scripts or external services, inspect them and prepare the required access in your test environment.

No recognized prerequisites section in this file. This does not mean it is self-contained: libraries, tools and access requirements may be mentioned elsewhere in the content or source repository.

The platforms on this page are catalog indications. The paths below follow tool documentation; they do not prove this skill has been run in those tools. No minimum tool version is confirmed for this listing.

3. Load the skill

After inspection, copy the complete folder into your project using one of the commands below, then open a new tool session. The command stops if a folder with the same name already exists: compare the files before replacing it.

Claude Code

test -f '.skill-downloads/huggingface-trending-filter-mrwakfxh/SKILL.md' && test -s '.skill-downloads/huggingface-trending-filter-mrwakfxh/SKILL.md' && mkdir -p '.claude/skills' && test ! -e '.claude/skills/aeon-huggingface-trending' && cp -R '.skill-downloads/huggingface-trending-filter-mrwakfxh' '.claude/skills/aeon-huggingface-trending'

Cursor

test -f '.skill-downloads/huggingface-trending-filter-mrwakfxh/SKILL.md' && test -s '.skill-downloads/huggingface-trending-filter-mrwakfxh/SKILL.md' && mkdir -p '.cursor/skills' && test ! -e '.cursor/skills/aeon-huggingface-trending' && cp -R '.skill-downloads/huggingface-trending-filter-mrwakfxh' '.cursor/skills/aeon-huggingface-trending'

Codex

test -f '.skill-downloads/huggingface-trending-filter-mrwakfxh/SKILL.md' && test -s '.skill-downloads/huggingface-trending-filter-mrwakfxh/SKILL.md' && mkdir -p '.agents/skills' && test ! -e '.agents/skills/aeon-huggingface-trending' && cp -R '.skill-downloads/huggingface-trending-filter-mrwakfxh' '.agents/skills/aeon-huggingface-trending'

OpenSkills

OpenSkills must already be installed. The local install/read procedure is checked with OpenSkills 1.5.0, which requires Node.js ≥ 20.6.0. Catalog slugs are not resolved automatically.

Official OpenSkills installation

openskills install './.skill-downloads/huggingface-trending-filter-mrwakfxh'

openskills read 'huggingface-trending-filter-mrwakfxh'

Reading should display the installed file content and its base directory. It does not run the skill or install its dependencies.

For an agent that reads AGENTS.md, then sync the index. This command changes AGENTS.md: review the diff before using it.

openskills sync

Manual method or another tool: attach SKILL.md to a conversation, or open it in an agent that can read files. Include any required supporting resources. No universal folder is recognized by every tool.

4. Try a first task

Use a short, synthetic input: an API endpoint, a small document or an interface brief. In a new conversation, attach the file or give the agent its path, then adapt this request:

Read .skill-downloads/huggingface-trending-filter-mrwakfxh/SKILL.md and apply its instructions to this example: [describe a test input]. Identify any missing files, dependencies or access. Produce the result requested by the skill without inventing data or running unreviewed scripts.

Expected result: output matching the skill instructions, or a precise list of missing prerequisites. A read confirmation alone does not validate a first use. If an external resource is unavailable, that path remains untested.

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