score-session

name	score-session
description	Score the current AI coding session for orchestration quality across 5 metrics

Score Session

Analyze the current conversation and score it for AI orchestration quality.

Trigger

Use when you want to evaluate how well this coding session used AI orchestration patterns — tool diversity, decision-making, error recovery, and compound learning.

Workflow

1. Gather the current session context — tools invoked, decisions made, errors encountered, skills used
2. Use the score_session MCP tool to score the session content
3. Present the score breakdown with actionable feedback

Input

The full session transcript or a summary of the current conversation. If the user provides a specific session export file, use score_session_file instead.

Output

• Overall score (0-100) with quality tier (Exceptional/Excellent/Good/Fair/Poor)
• Per-metric breakdown:
  • Skill Diversity (20%) — range of tools and skills invoked
  • Decision Depth (25%) — explicit tradeoffs and reasoning
  • Error Recovery (20%) — how errors were handled
  • Compound Learning (20%) — cross-step insights
  • Orchestration Mastery (15%) — agent coordination patterns
• Actionable suggestions for improving the score

Constraints

• Score reflects orchestration quality, not code correctness
• Sessions with no tool use or decisions will score low — that's expected for simple tasks
• Scores are deterministic for the same input