notion-hf-curator
End-to-end Notion-to-Hugging Face dataset and model curation workflow for SCBE repositories (export, QA, comparison, GitHub Actions, publishing).
Best use case
notion-hf-curator is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
End-to-end Notion-to-Hugging Face dataset and model curation workflow for SCBE repositories (export, QA, comparison, GitHub Actions, publishing).
Teams using notion-hf-curator should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/notion-hf-curator/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How notion-hf-curator Compares
| Feature / Agent | notion-hf-curator | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
End-to-end Notion-to-Hugging Face dataset and model curation workflow for SCBE repositories (export, QA, comparison, GitHub Actions, publishing).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Notion-to-HF Curator Use when a user asks to turn Notion content into an AI-ready dataset and sync to Hugging Face. ## Prereqs - `NOTION_TOKEN` or `NOTION_API_KEY` (Notion integration token). - `HF_TOKEN` and `HF` CLI installed (for publishing/validation). - In a clone of `SCBE-AETHERMOORE`, run from repository root. ## Pipeline workflow 1. Export Notion pages to dataset JSONL. - `python scripts/notion_to_dataset.py --category all --output training-data/` - Output files include `training-data/notion_export_<cat>_<yyyymmdd>.jsonl` and `training-data/metadata.json`. 2. Push dataset to Hugging Face. - `python scripts/push_to_hf.py --data-path training-data/notion_export_all_$(Get-Date -Format yyyyMMdd).jsonl --repo-id <owner>/scbe-aethermoore-training-data --token $env:HF_TOKEN` - For repository-level updates, use `HF_TOKEN` or pass `hf upload` directly. 3. Run pipeline health checks. - `python scripts/notion_pipeline_gap_review.py --output artifacts/notion_pipeline_gap_review.json --summary-path artifacts/notion_pipeline_gap_review.md` - `python scripts/compare_notion_to_codebase.py --notion-jsonl training-data/notion_raw_clean.jsonl` 4. Trigger GitHub Actions for repeatable runs. - `gh workflow run notion-to-dataset` - `gh workflow run cloud-kernel-data-pipeline -f ship_targets=hf,github` 5. Optional browser validation (Playwright). - Open target HF dataset/model pages and verify cards/metadata before/after publish. ## Notes - If dataset export records are 0, check Notion token scope and `scripts/sync-config.json` entries. - Use `artifacts/notion_pipeline_gap_review.json` and `.md` for the highest-priority remediation list. - Use `training/NOTION_CODEBASE_COMPARISON.md` to compare Notion coverage against repository code/docs.
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