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Session Trends Skill for Claude

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

2k tokens
context cost
the whole folder, loaded on every use
2
files
instructions only
0
copies elsewhere
how many repositories repackaged it
514
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/oliver-kriska/claude-elixir-phoenix --skill session-trends

What comes with it

4 520 bytes besides the instruction
references/trend-queries.md

The instruction itself

18 sections, as written by the author

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.

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
| Metric                  | 7 days | 30 days | All time |
|-------------------------|--------|---------|----------|
| Sessions                | 12     | 45      | 165      |
| Avg friction            | 0.28   | 0.24    | 0.22     |
| Max friction            | 0.72   | 0.72    | 0.89     |
| Avg opportunity         | 0.35   | 0.30    | 0.28     |
| Tier 2 eligible         | 40%    | 33%     | 30%      |
| Plugin adoption         | 12%    | 10%     | 8%       |
Fingerprint Distribution
| Type          | 7d  | 30d | All  |
|---------------|-----|-----|------|
| bug-fix       | 4   | 15  | 52   |
| feature       | 3   | 12  | 48   |
| exploration   | 2   | 8   | 30   |
| maintenance   | 1   | 5   | 18   |
| review        | 1   | 3   | 10   |
| refactoring   | 1   | 2   | 7    |
MEMORY.md Comparison (if --compare)

Compare measured values against MEMORY.md claims:

| MEMORY.md Claim              | Measured    | Match? |
|------------------------------|-------------|--------|
| Plugin adoption: 8-12%       | 10.2%       | Yes    |
| Minimal friction in 40+ of 74| 68% smooth  | Yes    |

Write computed trends to .claude/session-metrics/trends.json.

Step 6: Suggest Actions

Based on trends:

  • If friction is increasing: "Friction trending up — run /session-deep-dive --from-scan to investigate"
  • If plugin adoption is growing: "Plugin adoption growing — check which commands drive value"
  • If many Tier 2 eligible: "{N} sessions need deep analysis"

Output Files

| File | Purpose |

|------|---------|

| .claude/session-metrics/trends.json | Computed trend data |

Common Queries

See references/trend-queries.md for interpreting specific trend patterns.

Iron Laws

  • ALWAYS use Python for computation — no manual aggregation
  • NEVER modify metrics.jsonl — read-only for trends
  • ALWAYS show window comparison — single numbers lack context

Acknowledgements

The HTML report layout (preformatted text + ASCII bar charts via /)

and per-model + threshold-bucket breakdown (>=80%, >=90%, >=100%,

compaction_rate) were borrowed from

badlogic / earendil-works/pi session-context-stats.mjs.

Our pipeline's qualitative metrics (friction, fingerprint, plugin

opportunity, skill effectiveness) are additive on top.

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How to use it

Copy the folder

Take oliver-kriska/session-trends from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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