AI Engineering Hub is a learning and project repository covering large language models, retrieval-augmented generation, AI agents, and related applications. Beginners, practitioners, and researchers use its tutorials and projects to learn AI engineering and build working systems. The catalogue entries are examples of the skills, plugins, and agent resources included with it.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/patchy631/ai-engineering-hubnpx agentmods add skills/patchy631/ai-engineering-hub/hugging-face-evaluationWrote 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.
[](https://agentmods.dev/skills/patchy631/ai-engineering-hub/hugging-face-evaluation)<a href="https://agentmods.dev/skills/patchy631/ai-engineering-hub/hugging-face-evaluation"><img src="https://agentmods.dev/badge/skills/patchy631/ai-engineering-hub/hugging-face-evaluation/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.
<a href="https://agentmods.dev/skills/patchy631/ai-engineering-hub/hugging-face-evaluation"><img src="https://agentmods.dev/badge/skills/patchy631/ai-engineering-hub/hugging-face-evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk fail
- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 191 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 192 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 193 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 602 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 142 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00055 | $0.05934 |
| Opus 5.5 | $0.00022 | $0.02374 |
| Sonnet 5.5 | $0.00011 | $0.01187 |
| Haiku 4.5 | $0.00006 | $0.00593 |
Grade A, and why
hugging-face-evaluation 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run([ Copies of this mod
7 near-identical copies found in the catalogue:
- hugging-face-evaluation-manager — 98% identical, 1,290 lines differ
- hugging-face-evaluation-manager — 98% identical, 1,290 lines differ
- hugging-face-evaluation — 97% identical, 1,296 lines differ
- hugging-face-evaluation — 97% identical, 1,296 lines differ
- hugging-face-evaluation — 92% identical, 1,302 lines differ
- hugging-face-evaluation — 89% identical, 1,307 lines differ
- hugging-face-evaluation — 89% identical, 1,307 lines differ
How it starts
The opening of the file, as written. The whole thing — 646 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
This skill provides tools to add structured evaluation results to Hugging Face model cards. It supports multiple methods for adding evaluation data:
- Extracting existing evaluation tables from README content
- Importing benchmark scores from Artificial Analysis
- Running custom model evaluations with vLLM or accelerate backends (lighteval/inspect-ai)
Integration with HF Ecosystem
- Model Cards: Updates model-index metadata for leaderboard integration
- Artificial Analysis: Direct API integration for benchmark imports
- Papers with Code: Compatible with their model-index specification
- Jobs: Run evaluations directly on Hugging Face Jobs with
uvintegration - vLLM: Efficient GPU inference for custom model evaluation
- lighteval: HuggingFace's evaluation library with vLLM/accelerate backends
- inspect-ai: UK AI Safety Institute's evaluation framework
Version
1.3.0
Dependencies
Core Dependencies
- huggingface_hub>=0.26.0
- markdown-it-py>=3.0.0
- python-dotenv>=1.2.1
- pyyaml>=6.0.3
- requests>=2.32.5
- re (built-in)
Inference Provider Evaluation
- inspect-ai>=0.3.0
- inspect-evals
- openai
vLLM Custom Model Evaluation (GPU required)
- lighteval[accelerate,vllm]>=0.6.0
- vllm>=0.4.0
- torch>=2.0.0
- transformers>=4.40.0
- accelerate>=0.30.0
Note: vLLM dependencies are installed automatically via PEP 723 script headers when using uv run.
IMPORTANT: Using This Skill
⚠️ CRITICAL: Check for Existing PRs Before Creating New Ones
Before creating ANY pull request with --create-pr, you MUST check for existing open PRs:
uv run scripts/evaluation_manager.py get-prs --repo-id "username/model-name"
If open PRs exist:
- DO NOT create a new PR - this creates duplicate work for maintainers
- Warn the user that open PRs already exist
- Show the user the existing PR URLs so they can review them
- Only proceed if the user explicitly confirms they want to create another PR
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/.env.example 318 B
- examples/artificial_analysis_to_hub.py 4.0 KB runs code
- examples/example_readme_tables.md 3.2 KB
- examples/metric_mapping.json 974 B
- examples/USAGE_EXAMPLES.md 9.6 KB
- requirements.txt 485 B
- scripts/evaluation_manager.py 49 KB runs code
- scripts/inspect_eval_uv.py 3.0 KB runs code
- scripts/inspect_vllm_uv.py 9.5 KB runs code
- scripts/lighteval_vllm_uv.py 9.5 KB runs code
- scripts/run_eval_job.py 2.5 KB runs code
- scripts/run_vllm_eval_job.py 9.6 KB runs code
- scripts/test_extraction.py 5.5 KB runs code
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.
- 1mo ago First seen · 646 lines · 55 tokens per session scan A 80ccaa485486
hugging-face-evaluation is a skill published in the GitHub repository patchy631/ai-engineering-hub (38,394 stars, last pushed 8d ago), licensed MIT. It adds 55 tokens to every session and 5,934 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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