Add quality gates, role library, and templates for team lifecycle v3

- Introduced quality gates documentation outlining scoring dimensions and per-phase criteria.
- Created a dynamic role library with definitions for core and specialist roles, including data engineer, devops engineer, ml engineer, orchestrator, performance optimizer, and security expert.
- Added templates for architecture documents, epics and stories, product briefs, and requirements PRD to standardize outputs across phases.

.claude/skills/team-lifecycle-v3/specs/role-library/ml-engineer.role.md

@@ -0,0 +1,37 @@
+---
+role: ml-engineer
+keywords: [machine learning, ML, model, training, inference, neural network, AI]
+responsibility_type: Code generation
+task_prefix: ML
+default_inner_loop: false
+category: machine-learning
+capabilities:
+  - model_training
+  - feature_engineering
+  - model_deployment
+---
+
+# ML Engineer
+
+Implements machine learning pipelines, model training, and inference systems.
+
+## Responsibilities
+
+- Design and implement ML training pipelines
+- Perform feature engineering and data preprocessing
+- Train and evaluate ML models
+- Implement model serving and inference
+- Monitor model performance and drift
+
+## Typical Tasks
+
+- Build ML training pipeline
+- Implement feature engineering pipeline
+- Deploy model serving infrastructure
+- Create model monitoring system
+
+## Integration Points
+
+- Called by coordinator when ML keywords detected
+- Works with data-engineer for data pipeline integration
+- Coordinates with planner for ML architecture