engineering-analytics

name	engineering-analytics
description	Analyze engineering team metrics using Weave. Use when asked about team productivity, code velocity, PR cycle time, review turnaround, AI code adoption, DORA metrics, or any engineering performance question.

Engineering Analytics

Analyze engineering team performance using Weave's MCP tools. This skill handles questions about code output, PR cycle time, review quality, task delivery, AI code adoption, and more.

When to use

• "How is my team performing?"
• "What's our PR cycle time?"
• "Who are the top contributors this quarter?"
• "How much AI-written code are we shipping?"
• "Show me review turnaround trends"
• "Compare team velocity month over month"
• Any question about engineering metrics, productivity, or code analytics

Workflow

Step 1: Understand the question

Identify what the user is asking about:

• What metric? Map to a specific metric_type (see list below)
• What scope? Org-wide, team, or individual
• What time range? Default to last 30 days if not specified

Step 2: Resolve entities

If the question mentions specific teams or people:

1. Call get_teams to get team IDs and names
2. Call get_accounts to get member IDs and names
3. Use the returned IDs for filtering in subsequent calls

Step 3: Fetch metrics

Choose the right tool based on the question:

• Trends, aggregates, comparisons → get_metric_overview

  • Use group_by for breakdowns (by team, person, repository)
  • Use time_granularity for time series (day, week, month)
  • Use benchmark_group for comparisons

• Specific records, outliers, investigation → get_metric_drill_down

  • Use sort_by and sort_direction to find extremes
  • Use limit to control result size

Step 4: Present results

• Use tables for comparisons
• Round durations to human-readable values (e.g., "2.3 days" not "198432 seconds")
• Highlight trends (up/down from previous period)
• Include benchmark comparisons when available
• Call out notable outliers or patterns

Metric reference

Code velocity

Metric type	What it measures
code_output	Weighted productivity score factoring in complexity
code_output_per_engineer	Per-person code output
code_loc	Raw lines of code changed
prs	Pull requests merged
prs_per_engineer	PRs per person
pr_cycle_time	Open to merge duration
pr_merge_time	Approval to merge duration

Code review

Metric type	What it measures
code_reviews	Reviews performed
code_review_turnaround	Time to first review
code_review_quality	Review thoroughness score
review_cycles	Rounds of review before merge
pr_review_rate	Percentage of PRs that received review
comment_resolution	Comment resolution details

Task delivery

Metric type	What it measures
tasks	Tasks completed
points	Story points delivered
points_per_engineer	Points per person
task_lead_time	Creation to completion duration
task_delivery	Delivery rate
bug_tasks	Bug tasks completed

AI code

Metric type	What it measures
ai_code_loc	AI-assisted lines of code
ai_code_percentage	% of code written with AI tools
ai_output_percentage	AI contribution to output score
ai_efficiency_index	Composite score combining AI output volume, usage %, cost, and code turnover
output_per_ai_dollar	ROI metric: code output per dollar of AI tool spend
tool_costs	AI tool costs

Quality

Metric type	What it measures
bugs_introduced	New bugs introduced
bug_ratio	Bugs as percentage of work
revert_prs	Reverted pull requests
code_turnover	Code churn rate
innovation_ratio	Feature vs maintenance work ratio

Example queries

"How's the team doing this month?" → Call get_metric_overview with metric_type=code_output, current month date range, group_by=team

"Who shipped the most PRs last quarter?" → Call get_metric_drill_down with metric_type=prs, Q date range, sort_by=value, sort_direction=desc, limit=10

"What's our AI code adoption trend?" → Call get_metric_overview with metric_type=ai_code_percentage, time_granularity=week

"Compare review turnaround across teams" → Call get_teams first, then get_metric_overview with metric_type=code_review_turnaround, group_by=team