Pi Insights

Personal usage analytics for the Pi coding agent. Scans your session history, extracts deterministic stats and LLM-powered facets, then generates a self-contained HTML report covering your workflows, friction points, and suggestions for improvement.

Built by the Observal team while developing our agent observability platform. We needed to understand how we actually use Pi across hundreds of sessions, what patterns emerge, and where we waste time or money. This extension is the result.

Install

From npm (recommended):

pi install npm:@observal/pi-insights

From source:

git clone https://github.com/BlazeUp-AI/pi-insights.git
pi install ./pi-insights

Try without installing:

pi -e npm:@observal/pi-insights

Usage

Run the command inside any Pi session:

/pi-insights

The report opens in your browser automatically.

Flags

Flag	Description
--refresh / -r	Invalidate all cached LLM facet extractions and re-run them
--no-open	Generate the report without opening it in the browser
--since <N>d	Only analyze sessions from the last N days (e.g. --since 7d)
--md	Output a Markdown report instead of opening the HTML version

Examples

# Normal run (uses caches, fast on re-runs)
/pi-insights

# Force re-extraction of all session facets
/pi-insights --refresh

# Generate without auto-opening
/pi-insights --no-open

# Only analyze the last 7 days
/pi-insights --since 7d

# Export as Markdown (for Slack, docs, etc.)
/pi-insights --md

What the Report Shows

Session stats at a glance

Tokens, cost, lines changed, commits, tool errors, parallel sessions, and more.

Context-aware suggestions with copyable prompts

Suggests features, skills, and config additions tailored to your actual workflow. References your real projects and tools.

"Stop Doing" section

Tells you what patterns are costing you time or money, with concrete alternatives.

Model spend analysis

Identifies overspend (Opus on simple tasks) and underspend (Sonnet failing on complex work), with a recommendation and estimated savings.

What Makes This Different

Most Pi insight extensions dump flat aggregates into an LLM prompt and get the same generic report every time. This one is temporal-aware:

• Week-over-week diffs: see what actually changed, not a static portrait
• Decay-weighted charts: recent sessions have more influence on friction/satisfaction/outcome charts (10-day half-life)
• Trajectory detection: are your costs/errors improving, worsening, or stable?
• Anomaly detection: spikes in cost or errors are surfaced with context
• Resolved vs ongoing friction: only surfaces problems you still have, not ones you fixed
• Context-aware suggestions: reads your existing AGENTS.md, installed skills, extensions, and packages. Will not suggest what you already have.
• Negative suggestions: tells you what to stop doing, not just what to add

How It Works

The pipeline runs in five phases:

1. Scan all Pi session log files
2. Extract stats deterministically from each session (tool counts, tokens, languages, git activity, response times)
3. LLM facet extraction per session to classify goals, outcomes, satisfaction, and friction
4. Aggregate with decay weighting, compute diffs, detect anomalies and transitions, gather user context
5. Generate insights using 8 parallel LLM prompts (with temporal and user context injected) plus a synthesis prompt, then render a self-contained HTML report

Results are cached in ~/.pi/agent/usage-data/:

Path	Contents
session-meta/<id>.json	Deterministic stats, cached permanently
facets/<id>.json	LLM-extracted facets, cached permanently (clear with --refresh)
report.html	Last generated report
report.md	Last markdown export (when using --md)

Requirements

• Pi v0.74.0 or later
• An active model configured in Pi (used for both facet extraction and insight generation)

License

AGPL-3.0-only