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---
name: opencv
description: >-
[TODO] Define the specific workflow this skill standardises, including default libraries,
quality checks, and expected deliverables.
version: 1.0.0
tags:
- data-science
- [TODO]
metadata:
skill-author: 'Marie-Lynne Block'
---
## What this skill does
[TODO] Define the specific workflow this skill standardises, including default libraries,
quality checks, and expected deliverables.
## When to use it
[TODO] List concrete user intents and trigger phrases that should activate this skill.
## Instructions
1. Clarify the objective, data assumptions, and success metrics.
2. Execute a leakage-safe and reproducible workflow for this skill domain.
3. Validate outputs with diagnostics, edge-case checks, and documented caveats.
## Output format
- A concise plan of action
- Executable code or commands
- Validation summary with assumptions and risks
## Examples
### Example 1 - baseline workflow
**Input:** User asks for help in opencv.
**Expected output:** A reproducible, validated workflow using the skill's core tools.
## Notes
- Prefer documented, stable APIs over experimental shortcuts.
- Record assumptions explicitly when data quality or labels are uncertain.
