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 explaining-machine-learning-modelsgit 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/explaining-machine-learning-models)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/explaining-machine-learning-models"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/explaining-machine-learning-models/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/explaining-machine-learning-models"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/explaining-machine-learning-models.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 | $0.00049 | $0.00312 |
| Opus 5 | $0.00024 | $0.00156 |
| Sonnet 5 | $0.00010 | $0.00062 |
| Haiku 4.5 | $0.00005 | $0.00031 |
Grade A, and why
explaining-machine-learning-models 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 9d 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.
What it actually says
Model Explainability Tool
Positioning
Treat this skill as an explicit/manual helper for interpretability work.
When to Use
Use this skill when:
- Understand why a machine learning model made a specific prediction.
- Identify the most important features influencing a model's output.
- Debug model performance issues by identifying unexpected feature interactions.
- Communicate model insights to non-technical stakeholders.
- Ensure fairness and transparency in model predictions.
Not For / Boundaries
- Model training and hyperparameter search: use
scikit-learn - Benchmark comparison and threshold selection: use
evaluating-machine-learning-models - Leakage or prediction-time audits: use
ml-data-leakage-guard
Typical Outputs
- Feature importance or attribution summaries
- Local explanation workflow for a concrete prediction
- Notes on caveats, instability, or misleading explanations
Related Skills
shapfor SHAP-specific workflowsevaluating-machine-learning-modelswhen the question is whether the model is good enough
What ships with it
9 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.
- assets/example_explanation.json 3.5 KB
- assets/explanation_template.html 3.8 KB
- assets/README.md 362 B
- assets/visualization_styles.css 2.5 KB
- references/README.md 551 B
- scripts/data_preprocessing.py 2.8 KB runs code
- scripts/explain_model.py 2.9 KB runs code
- scripts/feature_importance.py 2.8 KB runs code
- scripts/README.md 407 B
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.
- 9d ago First seen · 42 lines · 49 tokens per session scan A 39cf26cd01ae
explaining-machine-learning-models 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 49 tokens to every session and 312 once invoked, about $0.0002 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 2026-09-03.
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