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:
- Call
get_teams to get team IDs and names
- Call
get_accounts to get member IDs and names
- 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
1---2name: engineering-analytics3description: 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.4---56# Engineering Analytics78Analyze 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.910## When to use1112- "How is my team performing?"13- "What's our PR cycle time?"14- "Who are the top contributors this quarter?"15- "How much AI-written code are we shipping?"16- "Show me review turnaround trends"17- "Compare team velocity month over month"18- Any question about engineering metrics, productivity, or code analytics1920## Workflow2122### Step 1: Understand the question2324Identify what the user is asking about:25- **What metric?** Map to a specific `metric_type` (see list below)26- **What scope?** Org-wide, team, or individual27- **What time range?** Default to last 30 days if not specified2829### Step 2: Resolve entities3031If the question mentions specific teams or people:321. Call `get_teams` to get team IDs and names332. Call `get_accounts` to get member IDs and names343. Use the returned IDs for filtering in subsequent calls3536### Step 3: Fetch metrics3738Choose the right tool based on the question:3940- **Trends, aggregates, comparisons** → `get_metric_overview`41 - Use `group_by` for breakdowns (by team, person, repository)42 - Use `time_granularity` for time series (`day`, `week`, `month`)43 - Use `benchmark_group` for comparisons4445- **Specific records, outliers, investigation** → `get_metric_drill_down`46 - Use `sort_by` and `sort_direction` to find extremes47 - Use `limit` to control result size4849### Step 4: Present results5051- Use tables for comparisons52- Round durations to human-readable values (e.g., "2.3 days" not "198432 seconds")53- Highlight trends (up/down from previous period)54- Include benchmark comparisons when available55- Call out notable outliers or patterns5657## Metric reference5859### Code velocity60| Metric type | What it measures |61|---|---|62| `code_output` | Weighted productivity score factoring in complexity |63| `code_output_per_engineer` | Per-person code output |64| `code_loc` | Raw lines of code changed |65| `prs` | Pull requests merged |66| `prs_per_engineer` | PRs per person |67| `pr_cycle_time` | Open to merge duration |68| `pr_merge_time` | Approval to merge duration |6970### Code review71| Metric type | What it measures |72|---|---|73| `code_reviews` | Reviews performed |74| `code_review_turnaround` | Time to first review |75| `code_review_quality` | Review thoroughness score |76| `review_cycles` | Rounds of review before merge |77| `pr_review_rate` | Percentage of PRs that received review |78| `comment_resolution` | Comment resolution details |7980### Task delivery81| Metric type | What it measures |82|---|---|83| `tasks` | Tasks completed |84| `points` | Story points delivered |85| `points_per_engineer` | Points per person |86| `task_lead_time` | Creation to completion duration |87| `task_delivery` | Delivery rate |88| `bug_tasks` | Bug tasks completed |8990### AI code91| Metric type | What it measures |92|---|---|93| `ai_code_loc` | AI-assisted lines of code |94| `ai_code_percentage` | % of code written with AI tools |95| `ai_output_percentage` | AI contribution to output score |96| `ai_efficiency_index` | Composite score combining AI output volume, usage %, cost, and code turnover |97| `output_per_ai_dollar` | ROI metric: code output per dollar of AI tool spend |98| `tool_costs` | AI tool costs |99100### Quality101| Metric type | What it measures |102|---|---|103| `bugs_introduced` | New bugs introduced |104| `bug_ratio` | Bugs as percentage of work |105| `revert_prs` | Reverted pull requests |106| `code_turnover` | Code churn rate |107| `innovation_ratio` | Feature vs maintenance work ratio |108109## Example queries110111**"How's the team doing this month?"**112→ Call `get_metric_overview` with `metric_type=code_output`, current month date range, `group_by=team`113114**"Who shipped the most PRs last quarter?"**115→ Call `get_metric_drill_down` with `metric_type=prs`, Q date range, `sort_by=value`, `sort_direction=desc`, `limit=10`116117**"What's our AI code adoption trend?"**118→ Call `get_metric_overview` with `metric_type=ai_code_percentage`, `time_granularity=week`119120**"Compare review turnaround across teams"**121→ Call `get_teams` first, then `get_metric_overview` with `metric_type=code_review_turnaround`, `group_by=team`
weave-os