Session Trends

Analyze trends from the metrics ledger. Computes windowed aggregates, fingerprint distributions, and compares against MEMORY.md baselines.

Requirements

Requires .claude/session-metrics/metrics.jsonl from /session-scan.

Usage

/session-trends                          # All windows (7d, 30d, all)
/session-trends --window 30d             # Specific window only
/session-trends --project enaia          # Filter by project
/session-trends --compare MEMORY.md      # Compare against memory baseline
/session-trends --html out.html          # Write HTML report with ASCII bars

For pure context-window stats (max prompt tokens, ctx %, compaction rate) across raw Claude Code JSONL files, see the --scan-jsonl mode of compute-metrics.py (inspired by badlogic / earendil-works/pi).

Pipeline

Step 1: Parse Arguments

Extract from $ARGUMENTS:

• --window WINDOW: Time window, 7d, 30d, or all (default: show all three)
• --project NAME: Filter metrics by project name
• --compare PATH: Path to MEMORY.md for baseline comparison (default: auto-detect from .claude/ project memory)

Step 2: Read Metrics Ledger

Read .claude/session-metrics/metrics.jsonl.

If empty or missing:

No metrics found. Run /session-scan first.

If --project specified, filter entries by project field.

Step 3: Compute Trends via Python

python3 .claude/skills/session-scan/references/compute-metrics.py \
  --trends .claude/session-metrics/metrics.jsonl \
  --memory {MEMORY_PATH}

Capture the JSON output.

Step 4: Display Trend Report

Format the JSON output as a readable report:

Overview

Total sessions: {N} ({backfilled} backfilled from v1)
Date range: {earliest} to {latest}

Window Comparison