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Analysis Period: 2026-04-09 (all sessions from today)
Completion Rate: 8.0% (4/50 overall; 100% for Copilot coding agents)
Average Duration: 1.17 min (all sessions); 10.24 min (Copilot agents only)
Distinct Branches: 3
Experimental Strategy: None (standard analysis)
Key Metrics
Metric
Value
Trend
Total Sessions
50
→
Successful Completions
4 (8.0%)
↑
Failed Sessions
6 (12.0%)
→
Action Required (review bots)
27 (54.0%)
→
Skipped
12 (24.0%)
→
Cancelled
1 (2.0%)
→
Avg Session Duration
1.17 min
↑
Copilot Coding Agents
2
→
Copilot Success Rate
100% (2/2)
↑
Avg Copilot Duration
10.24 min
↑
Context Issues
0
→
📈 Session Trends Analysis
Completion Patterns
The 10-day trend shows high volatility in success rates: a strong peak on Mar 31 (46% overall) followed by a collapse on Apr 1–2, then gradual recovery. Apr 9 shows a modest uptick to 8% overall, but the Copilot-specific rate remains at 100% — back-to-back perfect days (Apr 8 and Apr 9) suggest the current active task (fix-discussion-label-limit) is well-scoped and the agent is performing effectively.
Duration & Efficiency
Copilot agent duration spikes on Apr 3 (15.78 min avg) and today (10.24 min avg) correlate with successful task completion — deeper, longer sessions produce better outcomes. The Apr 7 collapse to 0.19 min average matched a 0% success day. Average all-session duration is typically low (0.2–1.2 min) because review trigger bots (Archie, Scout, Q, /cloclo) dominate session counts and complete nearly instantly.
Branch Analysis
View Per-Branch Breakdown
copilot/fix-discussion-label-limit (23 sessions — active Copilot work)
Abandonment risk: MEDIUM — awaiting human PR review/approval
Success Factors ✅
Focused task scope: fix-discussion-label-limit is a targeted bug fix. Copilot completed both agent sessions (100%). Contrast with broader multi-file refactor branches that stall.
Adequate agent time budget: Both successful Copilot sessions today ran 6.5m and 14.0m. The 10-day pattern shows duration > 5min strongly correlates with success.
Success rate correlation: long sessions (>5m avg) → success; short sessions (<1m avg) → failure
Iterative PR comment addressing: Copilot is being driven by specific PR review comments, which provide precise, actionable context. This is the most reliable trigger pattern observed across all 10 days.
Failure Signals ⚠️
CI pipeline failures co-existing with Copilot success: On fix-discussion-label-limit, Copilot succeeded twice but CI still fails. This suggests Copilot is making progress on application logic but a separate test infrastructure issue may be unresolved.
Failure rate: 2/4 CI runs today (50%)
Pattern: Design Decision Gate + Test Quality Sentinel both failing — likely the same root issue
Branch inactivity / review-bot-only traffic: Two branches (update-claude-code-and-gemini-cli, improve-workflow-documentation) have zero Copilot agent engagement but high review bot activity.
Failure rate: 100% for branches never receiving a Copilot agent session
Signal: Branches with >10 sessions and 0 Copilot agents are awaiting human action
Low overall completion rate (8%): Review bots (action_required) and skips inflate the denominator. The "true" Copilot task completion rate is 100% — but the organizational health rate (branches making progress) is low.
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Executive Summary
Key Metrics
📈 Session Trends Analysis
Completion Patterns
The 10-day trend shows high volatility in success rates: a strong peak on Mar 31 (46% overall) followed by a collapse on Apr 1–2, then gradual recovery. Apr 9 shows a modest uptick to 8% overall, but the Copilot-specific rate remains at 100% — back-to-back perfect days (Apr 8 and Apr 9) suggest the current active task (fix-discussion-label-limit) is well-scoped and the agent is performing effectively.
Duration & Efficiency
Copilot agent duration spikes on Apr 3 (15.78 min avg) and today (10.24 min avg) correlate with successful task completion — deeper, longer sessions produce better outcomes. The Apr 7 collapse to 0.19 min average matched a 0% success day. Average all-session duration is typically low (0.2–1.2 min) because review trigger bots (Archie, Scout, Q, /cloclo) dominate session counts and complete nearly instantly.
Branch Analysis
View Per-Branch Breakdown
copilot/fix-discussion-label-limit (23 sessions — active Copilot work)
copilot/update-claude-code-and-gemini-cli (15 sessions — awaiting human)
action_requiredcopilot/improve-workflow-documentation (12 sessions — awaiting human)
action_requiredSuccess Factors ✅
Focused task scope:
fix-discussion-label-limitis a targeted bug fix. Copilot completed both agent sessions (100%). Contrast with broader multi-file refactor branches that stall.Adequate agent time budget: Both successful Copilot sessions today ran 6.5m and 14.0m. The 10-day pattern shows duration > 5min strongly correlates with success.
Iterative PR comment addressing: Copilot is being driven by specific PR review comments, which provide precise, actionable context. This is the most reliable trigger pattern observed across all 10 days.
Failure Signals⚠️
CI pipeline failures co-existing with Copilot success: On
fix-discussion-label-limit, Copilot succeeded twice but CI still fails. This suggests Copilot is making progress on application logic but a separate test infrastructure issue may be unresolved.Branch inactivity / review-bot-only traffic: Two branches (
update-claude-code-and-gemini-cli,improve-workflow-documentation) have zero Copilot agent engagement but high review bot activity.Low overall completion rate (8%): Review bots (
action_required) and skips inflate the denominator. The "true" Copilot task completion rate is 100% — but the organizational health rate (branches making progress) is low.Prompt Quality Analysis 📝
High-Quality Prompt Characteristics
Example High-Quality Prompt (inferred from session name):
Next Steps
fix-discussion-label-limit— Copilot succeeding but CI failing suggests a test infrastructure issueupdate-claude-code-and-gemini-cliandimprove-workflow-documentationfor human action or closureAnalysis generated automatically on 2026-04-09
Run ID: §24188032597
Workflow: Copilot Session Insights
References:
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