ai-engineering-insights

name	ai-engineering-insights
description	AI-assisted engineering impact analysis — productivity metrics and code quality insights

AI Engineering Insights Skill

Inspired by: DX Q1 AI-Assisted Engineering Impact Report

Purpose: Track engagement, performance, and tool quality for AI engineering tools — the metrics engineering leaders use to justify spend and pick winning vendors.

What's Unique About the DX Report

The DX report defines three metric pillars that local-llm-server now mirrors:

1. Engagement — DAU/WAU, sessions per user, tool diversity. Adoption is a leading indicator of value.
2. Performance — cycle-time delta (AI vs control), defect rate, throughput. Outcome metrics that show whether AI is actually moving the needle.
3. Tool Quality — per-tool acceptance rate, latency, token efficiency. Helps choose between vendors objectively.

Module: agents/ai_insights.py

from agents.ai_insights import (
    EngagementMetrics, PerformanceAnalytics, AIToolMetrics,
    UsageEvent, ToolKind, build_report,
)

eng = EngagementMetrics()
eng.record(UsageEvent("alice", "claude_code", ToolKind.AGENT, datetime.now(), accepted=True))
eng.weekly_active_users()  # → 1

Integration Points

• Telemetry pipeline — emit UsageEvent from agent loops, completion endpoints, chat handlers.
• Dashboard — surface build_report(...) output in admin GUI.
• Vendor reviews — use AIToolMetrics.tool_ranking() to compare tools objectively.

Key Design Choices

• Plain dataclasses, no external deps — drops cleanly into any service.
• statistics.median — resistant to outliers (a single 100-hour PR doesn't skew cycle-time delta).
• Session detection by gap — matches DX's definition of "engagement session".

References

• DX Report: https://getdx.com/report/ai-assisted-engineering-Q1-impact-report/
• Quick-Note Issue: #264