claude-agents-coordination

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.

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.

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.

Data quality validation rules, quality metrics, and acceptance criteria for Somali dialect classifier datasets. Covers duplicate detection, language filtering, quality scoring, and validation protocols. Auto-invokes when discussing data quality, validation, cleaning, or quality guardrails for this project.

ETL workflow patterns, data pipeline architecture, and ingestion strategies for Somali dialect classifier. Covers source integration, transformation logic, staging patterns, and load strategies. Auto-invokes when discussing data pipelines, ETL, ingestion workflows, or data processing architecture.

WCAG AA accessibility checklist and verification protocols for Somali dialect classifier dashboard. Covers keyboard navigation, screen readers, color contrast, ARIA labels, and accessibility testing procedures.

Reusable React/JavaScript patterns for Somali dialect classifier dashboard. Covers Chart.js integration, data card components, filter patterns, responsive layouts, and dashboard-specific UI patterns. Auto-invokes when building dashboard components, charts, data visualizations, or dashboard UI.

Low-resource NLP techniques specific to Somali language processing. Covers data scarcity strategies, cross-lingual transfer, morphological analysis, data augmentation for Somali, semi-supervised learning, and evaluation considerations for low-resource contexts. Auto-invokes when working on Somali NLP, low-resource language challenges, dialect classification, or language-specific modeling decisions.

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.

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.