session-trends

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Analyze trends across session metrics. Computes windowed aggregates, deltas, and compares against MEMORY.md findings. Use periodically for progress tracking.

Code & Development 543 stars 38 forks Updated yesterday MIT

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Skill Content

# 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 ```bash python3 .claude/skills/session-scan/references/compute-metrics.py \ --trends .claude/s...

Details

Author
oliver-kriska
Repository
oliver-kriska/claude-elixir-phoenix
Created
7 months ago
Last Updated
yesterday
Language
Python
License
MIT

Integrates with

Model Context Protocol · AI

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claude-elixir-phoenix

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