Session Insights

Surface patterns from learnings and session history.

Trigger

Use when asking "show stats", "how am I doing", "analytics", "insights", "heatmap", or "correction rate".

Data Sources

Gather data from these locations before computing metrics:

# Session history and learnings
cat .claude/LEARNED.md 2>/dev/null || cat CLAUDE.md | grep -A999 "LEARNED"
cat .claude/learning-log.md 2>/dev/null

# Session activity
git log --oneline --since="today" --author="$(git config user.name)"
git diff --stat

A correction is any instance where the user redirected, fixed, or overrode agent output during a session. Count

[LEARN]

entries and explicit correction markers in session history.

What It Shows

Session Summary

Session Insights
  Duration: 47 min
  Edits: 23 files modified
  Corrections: 2 self-corrections applied
  Learnings: 3 new patterns captured
  Context: 62% used (safe)

Learning Analytics

Learning Insights (42 total)

Top categories:
  Testing     12 learnings (29%)
  Navigation   8 learnings (19%)
  Git          7 learnings (17%)
  Quality      6 learnings (14%)

Most applied:
  #12 [Testing] Run tests before commit — 15 times
  #8  [Navigation] Confirm path for common names — 11 times

Stale learnings (never applied):
  #15 [Editing] Prefer named exports — 0 times (45 days old)

Correction Heatmap

Correction Heatmap

By category (all time):
  ████████████ Testing      34 corrections
  ████████     Navigation   22 corrections
  ██████       Git          18 corrections
  ████         Quality      12 corrections

Hot learnings (most corrected, least learned):
  - [Testing] Mock external deps — corrected 8x, learned 0x
    → Consider: /learn-rule to capture this permanently

Cold learnings (learned but never applied):
  - [Editing] Use named exports — learned 45 days ago, applied 0x
    → Consider removing if no longer relevant

Productivity Metrics

Productivity (last 10 sessions)
  Avg session: 35 min
  Avg edits/session: 18
  Correction rate: 12% (improving)
  Learning capture: 2.1 per session

Guardrails

• Use actual data from project memory and session history.
• Surface actionable suggestions, not just numbers.
• Flag recurring corrections that should become permanent rules.
• Identify stale learnings that may no longer be relevant.

Output

Formatted analytics report with:

• Current session stats
• Category breakdown
• Most/least applied learnings
• Correction trends with suggestions
• Productivity metrics over time