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 detecting-data-anomaliesgit 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/detecting-data-anomalies)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/detecting-data-anomalies"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/detecting-data-anomalies/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/detecting-data-anomalies"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/detecting-data-anomalies.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 | $0.00051 | $0.00330 |
| Opus 5 | $0.00026 | $0.00165 |
| Sonnet 5 | $0.00010 | $0.00066 |
| Haiku 4.5 | $0.00005 | $0.00033 |
Grade A, and why
detecting-data-anomalies 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.
What it actually says
Detecting Data Anomalies
Positioning
Treat this skill as an explicit/manual helper.
In governed ML routing, anomaly-detection ownership normally belongs to scikit-learn.
When to Use
Use this skill when:
- Reviewing outlier transactions, fraud candidates, sensor spikes, or rare failures
- Comparing isolation forest, one-class SVM, LOF, or threshold-based anomaly workflows
- Turning suspicious records into a shortlist for human inspection
Not For / Boundaries
- Null/duplicate/schema/range validation: use
exploratory-data-analysis - Full model training or end-to-end pipeline ownership: use
scikit-learnorml-pipeline-workflow - Publication-grade figure production: use
scientific-visualization
Typical Outputs
- Candidate anomaly-detection methods and thresholds
- A review checklist for false positives and false negatives
- Suggested tables or plots for the suspicious subset
Related Skills
scikit-learnas the governed routed owner for classical anomaly-detection workflowscreating-data-visualizationsafter anomalies are identified
What ships with it
10 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/README.md 404 B
- references/errors.md 1001 B
- references/examples.md 67 B
- references/implementation.md 1.7 KB
- references/README.md 683 B
- scripts/algorithm_selector.py 2.9 KB runs code
- scripts/anomaly_visualizer.py 2.9 KB runs code
- scripts/data_loader.py 2.9 KB runs code
- scripts/README.md 639 B
- scripts/report_generator.py 2.9 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.
- 13d ago First seen · 42 lines · 51 tokens per session scan A f92ba4bb2656
detecting-data-anomalies 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 51 tokens to every session and 330 once invoked, about $0.0003 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.
Other skills, from other repositories
meta-design-composable-components
Composable component APIs — parts, state, polymorphism.
ai-infrastructure-replicate
Replicate SDK patterns for TypeScript/Node.js -- client setup, predictions, streaming, webhooks, file handling, model versioning, deployments, and training.
ai-provider-elevenlabs
ElevenLabs voice AI SDK patterns for TypeScript/Node.js -- text-to-speech, streaming, voice cloning, speech-to-speech, pronunciation control, and conversational AI.
ai-provider-mistral-sdk
Official Mistral AI TypeScript SDK patterns — client setup, chat completions, streaming, function calling, structured outputs, embeddings, vision, Codestral FIM, and production best practices.
api-baas-neon
Serverless PostgreSQL with branching, autoscaling, and edge-compatible driver.
api-database-postgresql
Direct PostgreSQL access with node-postgres (pg) -- connection pools, parameterized queries, transactions, streaming, LISTEN/NOTIFY, error handling.