Hugging Face Trending

> **${var}** — Optional. One of `models`, `datasets`, `spaces` to scope the digest to a single resource type. Empty = pull from all three and let the curator pick the best 5–8 across them.

Today is ${today}. The Hugging Face Hub is where new AI artifacts land first — models hours after a paper, datasets before they get cited, spaces as the first runnable form of a technique. The Hub's own front page lists "trending" but doesn't filter the noise (test models, gated previews, redundant fine-tunes of the same base). This skill mirrors `github-trending`'s contract for the AI ecosystem: don't dump the top 10, deliver a **curated** slate of 5–8 picks a busy AI/dev reader would actually want to click, with a one-line "why notable" each.

Read `memory/MEMORY.md` for context.
Read the last 3 days of `memory/logs/` to dedupe artifacts already featured.
Read `soul/SOUL.md` + `soul/STYLE.md` if populated to match voice.

## Steps

### 1. Fetch candidates

The Hugging Face Hub REST API is fully keyless for the list endpoints used here. Pull trending across all three resource types unless `${var}` narrows it:

```bash
# Models — sort=trendingScore returns the same ranking that backs the HF front page
curl -sf "https://huggingface.co/api/models?sort=trendingScore&direction=-1&limit=20" \
  -H "accept: application/json" \
  -H "user-agent: aeon/1.0 (+https://github.com/aaronjmars/aeon)" \
  > .hf-models.json

# Datasets
curl -sf "https://huggingface.co/api/datasets?sort=trendingScore&direction=-1&limit=15" \
  -H "accept: application/json" \
  -H "user-agent: aeon/1.0 (+https://github.com/aaronjmars/aeon)" \
  > .hf-datasets.json

# Spaces
curl -sf "https://huggingface.co/api/spaces?sort=trendingScore&direction=-1&limit=15" \
  -H "accept: application/json" \
  -H "user-agent: aeon/1.0 (+https://github.com/aaronjmars/aeon)" \
  > .hf-spaces.json
```

If `${var}` is set to `models` / `datasets` / `spaces`, fetch only that endpoint.

If any `curl` fails (sandbox blocks outbound from bash on some runs), use **WebFetch** as a fallback for the same URL. WebFetch bypasses the sandbox and parses the JSON for you. If both fail across all three resources, log `HF_TRENDING_ERROR` with the failure detail, send a brief notify (*"Hugging Face Trending — sources unavailable today."*), and exit.

For each entry extract:
- `id` (always present, format `owner/name`) — split on `/` to get author + name
- `likes`, `downloads` (models/datasets only, spaces have no `downloads`), `trendingScore`
- `tags` (filter out `region:*`, `license:*`, and storage-format noise like `endpoints_compatible`, `safetensors`, `gguf`)
- `pipeline_tag` (models) — the canonical task label (e.g. `text-generation`, `text-to-image`)
- `library_name` (models) — `transformers`, `diffusers`, `mlx`, etc.
- `sdk` (spaces) — `gradio` / `streamlit` / `docker` / `static`
- `createdAt`, `lastModified` (when present)
- Resource type (`models` / `datasets` / `spaces`) — preserve so the renderer can pick the right footer
- Permalink: `https://huggingface.co/{id}` for models, `/datasets/{id}` for datasets, `/spaces/{id}` for spaces

### 2. Filter noise (required)

Drop entries matching these patterns — they're low-signal:

- **Test / debug artifacts**: `id` containing `-test`, `-debug`, `-tmp`, `-scratch`, `-playground`, or starting with `test-` / `debug-`
- **Gated / private preview shells**: entries flagged `gated: true` *and* with `<10` likes (HF gates lots of legit work, but a gated artifact with no community signal is usually a draft)
- **Trivial fine-tunes**: model `id` ending in `-finetune`, `-ft`, `-lora-test`, or with `<5` likes AND `<100` downloads (real momentum picks both)
- **Already featured**: anything that appeared in `memory/logs/YYYY-MM-DD.md` for the last 3 days
- **Quantization-only forks**: `id` ending in `-gguf`, `-awq`, `-gptq`, `-int4`, `-int8`, `-fp8` *unless* it has `>500` likes — quantizations of a base model are useful but rarely the most interesting story; the base usually carries the narrative
- **Spaces with `runtime.status: ERROR`** if the field is present (broken demos shouldn't be recommended)
- **Spaces called "demo"** or "example" with `<20` likes — boilerplate scaffolds

If an entry barely fails a filter but is genuinely interesting (novel architecture, first-of-kind dataset, reference implementation of a fresh paper), you may keep it — note it as a judgment call in the log.

### 3. Require a "why notable" for each survivor

For every survivor, write **one line** (≤ 18 words) explaining *why someone should care today*. No paraphrasing the model card / dataset description.

Good: *"First open-weight 70B trained end-to-end with online RL — beats Llama 3 70B on AGIEval, MIT-licensed."*
Bad: *"A new instruction-tuned LLM."* (that's just the description)

If you can't write a concrete "why notable" line for an entry, **drop it**. The filter is the feature.

When the artifact references a paper, you may pull one verifying detail via **WebFetch** on the arxiv URL or the HF model card — but cap at 1 fetch per pick, and only when it materially sharpens the line.

### 4. Tag momentum

Tag each survivor with one of:

- **DEBUT** — `createdAt` within the last 7 days (first-time trending)
- **ACCELERATING** — older than 7 days, `trendingScore > 50` AND `likes > 200`
- **RETURNING** — `createdAt` older than 90 days but trending again — usually a release, a viral post, or a paper drop reviving interest. Note the reason in "why notable" when known
- **HOLDOVER** — appeared in the last day's logs (use sparingly; prefer to drop unless there's a new development)

### 5. Cluster into categories

Buckets are heuristic — classify by what the artifact does, not by author self-description. Cap total buckets at **5** (merge if you hit 6+). Group survivors:

- **LLMs / Reasoning** — text-generation, instruction-tuned, reasoning-tuned, RAG models
- **Multimodal** — text-to-image, text-to-video, vision-language, speech, music
- **Agents / Tooling** —