claude-user/skills/ux-writing/SKILL.md
Voice, tone, and content guidelines for data/ML dashboards. Covers microcopy, error messages, success states, and data presentation language. Auto-invokes on copy, messaging, content, labels, error messages keywords.
npx skillsauth add ilyasibrahim/claude-agents-coordination ux-writingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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| Context | Tone | Example | |---------|------|---------| | Executive | Strategic, narrative | "ML models require quality data for production performance" | | Analyst | Precise, data-rich | "Retention: discovery → silver dataset" | | Error | Calm, solution-focused | "Unable to load metrics. Check connection and refresh." | | Success | Confirming, brief | "Pipeline completed successfully." |
Structure: Problem + Solution
Structure: Explanation + Action
Be specific: "Loading dashboard..." not "Loading..."
Describe: "Bar chart showing training data distribution" Not: "Image of chart"
Specific: "View dataset documentation" Not: "Click here"
| Use | Avoid | |-----|-------| | Dataset | Data set | | Pipeline | ETL process | | Real-time | Realtime | | Training data | Train data |
Version: 2.0.0
development
Unified design system for data/ML dashboards. Quick reference for brand vs data color decisions, component patterns, typography, spacing. Auto-invokes on styling, CSS, design, colors, UI, visualization keywords. Tiered loading - core always, philosophy/implementation on-demand.
development
Coordination protocol for main Claude Code agent. Explicit user invocation required ("mobilize agents", "coordinate", "check registry"). Provides agent orchestration, registry management, and handoff protocols. Subagents never access this - main agent provides context in task prompts.
development
Model evaluation metrics, testing protocols, and performance assessment for Somali dialect classification. Covers accuracy, F1-score, confusion matrix analysis, per-dialect performance, and evaluation best practices for multi-class classification tasks.
testing
MLOps best practices for model versioning, experiment tracking, deployment, monitoring, and retraining workflows. Covers reproducibility, CI/CD for ML, model registry, and production ML system design.