detecting-data-anomalies
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pinned to #34429a8updated 3 months ago
Ask your AI client: “install skills/detecting-data-anomalies”.
Requires the metahub MCP server installed in your client. Set up MCP.
mh install skills/detecting-data-anomaliesmetahub onboarded this repo on the author's behalf.
If you own github.com/foryourhealth111-pixel/Vibe-Skills on GitHub, claim the listing to take over publishing. Your claim preserves the existing eval history and badges; only the curator label is replaced with verified-publisher on your next publish.
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Last commit
3 months ago
Latest release
published
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About this skill
Pulled from SKILL.md at publish time.
Allowed tools
- Read
- Bash(python:*)
- Grep
- Glob
Automated checks the publisher passed at publish time — structure, docs, safety, and whether the artifact behaves as claimed.d5ae560· last month
Documentation
6 passed3 warningsREADME has usage / example sectionswarn
README has no Usage / Example / Quick start / Installation heading at any level
Add a `# Usage`, `## Quick start`, or similar section so end users can copy/paste a working invocation. Code blocks alone work too if there are several.
Homepage or repository declaredwarn
No homepage or repository declared.
Add a "homepage" or "repository" field to SKILL.md.
Documentation shows usagewarn
Documentation has no code examples.
Add at least one fenced example showing a real invocation.
Description quality
31 words · 237 chars — "Investigate outliers, rare events, spikes, and suspicious records in datasets. U…"
README is present and substantial
24,689 chars · 1 code block
Tags / topics declared
19 total — agent-framework, agent-skills, agentic-coding, ai-agents, ai-scientist, ai-skills (+13)
Homepage / docs URL declared
no homepage declared (registry will use the repo URL) — info-only, not blocking
Description is substantive
Description is 31 words.
Documentation present and substantive
Documentation present (SKILL.md, 189 words).
Release history
1- releasecurrent34429a8warn3 months ago
Contents
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
Reviews
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Related
Verification Before Completion
Evidence before assertions, always
Writing Plans
Turn specs into phased implementation plans
Test-Driven Development
Red → green → refactor discipline for any feature or bugfix
mh install skills/detecting-data-anomalies