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
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add patchy631/ai-engineering-hub --skill hugging-face-jobsgit clone --depth 1 https://github.com/patchy631/ai-engineering-hubWrote 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-jobs)<a href="https://agentmods.dev/skills/patchy631/ai-engineering-hub/hugging-face-jobs"><img src="https://agentmods.dev/badge/skills/patchy631/ai-engineering-hub/hugging-face-jobs/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-jobs"><img src="https://agentmods.dev/badge/skills/patchy631/ai-engineering-hub/hugging-face-jobs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 7 findings, up to medium
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 →
- medium Rogue Agent · line 31 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Excessive Agency · line 423 Skill grants unrestricted tool access without appropriate constraints. An agent with unfettered tool access can perform arbitrary actions including file modification, network requests, and code execution.Fix: Restrict tool access to only the tools required for the skill's stated purpose. Use an explicit allowlist rather than granting blanket access.
- medium Data Exfiltration · line 511 Data is uploaded to cloud storage (S3 / GCS / Azure Blob). This may be a legitimate backup or exfiltration to an external bucket. Manual review is recommended.Fix: Verify the destination bucket is trusted and owned by you. Never upload credentials, secrets, or workspace contents to external or unverified cloud storage.
- medium Data Exfiltration · line 525 Data is uploaded to cloud storage (S3 / GCS / Azure Blob). This may be a legitimate backup or exfiltration to an external bucket. Manual review is recommended.Fix: Verify the destination bucket is trusted and owned by you. Never upload credentials, secrets, or workspace contents to external or unverified cloud storage.
- medium Data Exfiltration · line 554 Data is uploaded to cloud storage (S3 / GCS / Azure Blob). This may be a legitimate backup or exfiltration to an external bucket. Manual review is recommended.Fix: Verify the destination bucket is trusted and owned by you. Never upload credentials, secrets, or workspace contents to external or unverified cloud storage.
- medium Data Exfiltration · line 533 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 533 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00103 | $0.08087 |
| Opus 5 | $0.00051 | $0.04043 |
| Sonnet 5 | $0.00021 | $0.01617 |
| Haiku 4.5 | $0.00010 | $0.00809 |
Grade C, and why
hugging-face-jobs scanned grade C with 3 findings 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 10d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
requests.post("https://your-api.com/results", json=results) Unrestricted tool accessmediumExcessive agency
A wildcard tool grant or "run any command" leaves no least-privilege boundary at all.
**Benefits:** Full Docker control, use pre-built images, run any command Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
requests.post("https://your-api.com/results", json=results) Copies of this mod
2 near-identical copies found in the catalogue:
- hugging-face-jobs — 100% identical, 2,072 lines differ
- huggingface-jobs — 86% identical, 2,078 lines differ
How it starts
The opening of the file, as written. The whole thing — 1,037 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Running Workloads on Hugging Face Jobs
Overview
Run any workload on fully managed Hugging Face infrastructure. No local setup required—jobs run on cloud CPUs, GPUs, or TPUs and can persist results to the Hugging Face Hub.
Common use cases:
- Data Processing - Transform, filter, or analyze large datasets
- Batch Inference - Run inference on thousands of samples
- Experiments & Benchmarks - Reproducible ML experiments
- Model Training - Fine-tune models (see
model-trainerskill for TRL-specific training) - Synthetic Data Generation - Generate datasets using LLMs
- Development & Testing - Test code without local GPU setup
- Scheduled Jobs - Automate recurring tasks
For model training specifically: See the model-trainer skill for TRL-based training workflows.
When to Use This Skill
Use this skill when users want to:
- Run Python workloads on cloud infrastructure
- Execute jobs without local GPU/TPU setup
- Process data at scale
- Run batch inference or experiments
- Schedule recurring tasks
- Use GPUs/TPUs for any workload
- Persist results to the Hugging Face Hub
Key Directives
When assisting with jobs:
-
ALWAYS use
hf_jobs()MCP tool - Submit jobs usinghf_jobs("uv", {...})orhf_jobs("run", {...}). Thescriptparameter accepts Python code directly. Do NOT save to local files unless the user explicitly requests it. Pass the script content as a string tohf_jobs(). -
Always handle authentication - Jobs that interact with the Hub require
HF_TOKENvia secrets. See Token Usage section below. -
Provide job details after submission - After submitting, provide job ID, monitoring URL, estimated time, and note that the user can request status checks later.
-
Set appropriate timeouts - Default 30min may be insufficient for long-running tasks.
Prerequisites Checklist
Before starting any job, verify:
✅ Account & Authentication
- Hugging Face Account with Pro, Team, or Enterprise plan (Jobs require paid plan)
- Authenticated login: Check with
hf_whoami() - HF_TOKEN for Hub Access ⚠️ CRITICAL - Required for any Hub operations (push models/datasets, download private repos, etc.)
- Token must have appropriate permissions (read for downloads, write for uploads)
What ships with it
8 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.
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.
- 10d ago First seen · 1,037 lines · 103 tokens per session scan C 027f40972832
hugging-face-jobs is a skill published in the GitHub repository patchy631/ai-engineering-hub (37,448 stars, last pushed 14d ago), licensed MIT. It adds 103 tokens to every session and 8,087 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 3 findings (sends data to an external url, unrestricted tool access, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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