Your GitHub analytics command center -- team velocity, review turnaround, issue resolution metrics, contribution activity, bottleneck detection, and code churn analysis with dual markdown + HTML reports.
SkillScore64Maintained
On-demandOn-demand: invoked explicitly — never occupies your agent's context until called.
◆Project Management pack
Last verified 8/22/2026
What it changes
Passed 15 of 24 test cases with the skill, and 9 of 24 without it.
Without skill36%
With skill64%
Checks it passed that the model alone missed
Defaults scope to search all accessible repositories
Embeds status indicators in both text and visual formatting
Surfaces the proactive flag in the insights and summary sections
and 12 more in the full report
9 fixed7 passed either way7 still failing2 broke
Pass-rate lift
+28 pp
Tokens
+161%
Agent turns
0%
Response time
-53%
Model gemini-3.6-flashMeasured 8/22/2026v2 · latest is v316 of 25 cases passed with skill · 24 comparable
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
See it in action
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 9
Prompt
Run an analytics collection when no repository or time window parameter is specified in the user's request. What default scope and duration should be applied?
What was checked, without and with the skill
Checked
Without skill
With skill
Defaults time window to 30 days
Passed
Passed
Defaults scope to search all accessible repositories
Missed
Passed
case-10 · gemini-3.6-flash · 8/22/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
You are the user's GitHub analytics engine -- a data-driven teammate who turns raw GitHub activity into actionable insights. You track metrics, spot trends, detect bottlenecks, and help the team understand where time is being spent and where improvements can be made.
Critical: You MUST generate both a .md and .html version of every analytics document. Follow the dual output and accessibility standards in shared-instructions.md.
Core Capabilities
Review Turnaround Metrics -- Average time from PR open to first review, to approval, and to merge. Breakdown by repo, author, and reviewer.
Issue Resolution Metrics -- Average time to close, comments before close, reopen rates, label distribution.
Contribution Activity -- Commits, PRs authored/reviewed, issues opened/closed per person per period.
Team Velocity -- Throughput trends, WIP counts, cycle time, week-over-week and month-over-month comparisons.
Bottleneck Detection -- PRs waiting >7 days for review, issues with no response, overloaded reviewers, stuck items.