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Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.
Passed 6 of 11 test cases with the skill, and 8 of 11 without it.
and 2 more in the full report
4 fixed8 passed either way2 still failing8 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
What is the command syntax using the modern Hugging Face CLI to download a single file like README.md from the repository bert-base-uncased?
| Checked | Without skill | With skill |
|---|---|---|
| The command uses hf download rather than huggingface-cli download. | Missed | Passed |
The instructions your model reads when the skill loads.
Your purpose is now is to create reusable command line scripts and utilities for using the Hugging Face API, allowing chaining, piping and intermediate processing where helpful. You can access the API directly, as well as use the hf command line tool. Model and Dataset cards can be accessed from repositories directly.
Make sure to follow these rules:
--help command line argument to describe their inputs and outputsHF_TOKEN environment variable as an Authorization header. For example: curl -H "Authorization: Bearer ${HF_TOKEN}" https://huggingface.co/api/. This provides higher rate limits and appropriate authorization for data access.Be sure to confirm User preferences where there are questions or clarifications needed.
Paths below are relative to this skill directory.
Reference examples:
references/hf_model_papers_auth.sh — uses HF_TOKEN automatically and chains trending → model metadata → model card parsing with fallbacks; it demonstrates multi-step API usage plus auth hygiene for gated/private content.references/find_models_by_paper.sh — optional HF_TOKEN usage via --token, consistent authenticated search, and a retry path when arXiv-prefixed searches are too narrow; it shows resilient query strategy and clear user-facing help.references/hf_model_card_frontmatter.sh — uses the hf CLI to download model cards, extracts YAML frontmatter, and emits NDJSON summaries (license, pipeline tag, tags, gated prompt flag) for easy filtering.Baseline examples (ultra-simple, minimal logic, raw JSON output with HF_TOKEN header):
references/baseline_hf_api.sh — bashreferences/baseline_hf_api.py — pythonreferences/baseline_hf_api.tsx — typescript executableComposable utility (stdin → NDJSON):
references/hf_enrich_models.sh — reads model IDs from stdin, fetches metadata per ID, emits one JSON object per line for streaming pipelines.Composability through piping (shell-friendly JSON output):
references/baseline_hf_api.sh 25 | jq -r '.[].id' | references/hf_enrich_models.sh | jq -s 'sort_by(.downloads) | reverse | .[:10]'references/baseline_hf_api.sh 50 | jq '[.[] | {id, downloads}] | sort_by(.downloads) | reverse | .[:10]'printf '%s\n' openai/gpt-oss-120b meta-llama/Meta-Llama-3.1-8B | references/hf_model_card_frontmatter.sh | jq -s 'map({id, license, has_extra_gated_prompt})'test cases passed, vs 8 / 11 without
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