Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill lqf_machine_learning_expert_guidegit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote 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/foryourhealth111-pixel/vibe-skills/lqf_machine_learning_expert_guide)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/lqf_machine_learning_expert_guide"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/lqf_machine_learning_expert_guide/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/foryourhealth111-pixel/vibe-skills/lqf_machine_learning_expert_guide"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/lqf_machine_learning_expert_guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00152 | $0.07467 |
| Opus 5 | $0.00076 | $0.03734 |
| Sonnet 5 | $0.00030 | $0.01493 |
| Haiku 4.5 | $0.00015 | $0.00747 |
Grade A, and why
LQF_Machine_Learning_Expert_Guide scanned grade A with 0 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 13d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 830 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LQF Machine Learning Expert Guide
When to Use This Skill
Use this skill when:
- Building ML models (classification, regression, clustering, forecasting)
- Evaluating model performance and debugging issues
- Feature engineering and data preprocessing for ML
- Hyperparameter tuning and model optimization
- Debugging overfitting, underfitting, or poor generalization
- Choosing between traditional ML and deep learning approaches
- Establishing baselines and conducting ablation studies
- Performing error analysis and model validation
- Statistical modeling with predictive components
Not For / Boundaries
Out of Scope:
- Pure data visualization without modeling (use data visualization skills)
- Database queries without predictive modeling
- Basic descriptive statistics without ML context
- Production deployment infrastructure (use MLOps/deployment skills)
- Reinforcement learning (specialized domain)
- Time series forecasting with specialized methods (use time series skills)
Required Inputs - Ask User If Missing:
- What is the problem type? (classification, regression, clustering, etc.)
- What does your data look like? (size, number of features, target variable distribution)
- Have you established a baseline yet? (dummy predictor, simple heuristic)
Critical Discussion Protocol
This skill operates in Critical Engagement Mode - every proposal (user's or your own) undergoes systematic critique and iterative refinement.
Core Principles
- No First-Pass Acceptance: Never accept initial proposals without critique
- Minimum 3 Iteration Cycles: Propose → Critique → Refine → Repeat (3x minimum)
- Evidence-Based Critique: Every critique must cite specific ML concerns
- Tiered Information Requirements:
- HIGH-RISK decisions (model selection, data splitting, deployment): Demand complete information
- LOW-RISK exploration (EDA, feature brainstorming): Proceed with stated assumptions
Critique Intensity Levels
Level 1 - Diplomatic (for exploration/brainstorming):
- "Have you considered establishing a baseline first?"
- "It might be worth exploring simpler alternatives..."
- "One potential concern is..."
What ships with it
3 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.
- 13d ago First seen · 830 lines · 152 tokens per session scan A c37676f68c2e
LQF_Machine_Learning_Expert_Guide is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 12d ago), licensed Apache-2.0. It adds 152 tokens to every session and 7,467 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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