pi-extension-stats

Token and cost analytics for Pi session history.

What it does

• Parses local Pi session .jsonl files for the current workspace.
• Aggregates usage by UTC day.
• Displays compact daily token bars and cost bars with totals.
• Shows input/output/cache breakdown, estimated initial prompt input (PI: X tok) with source split-up, cache hit rate, estimated cache savings, cost burn rate, and top model usage.
• Highlights highest-cost day, projected 30-day cost, most expensive sessions, and model cost efficiency.

Install

pi install npm:@firstpick/pi-extension-stats

Configuration

No required configuration.

Commands

• /stats [days|all] — show token usage dashboard (default: last 14 days).
• /stats tokens — show current context token breakdown by source/type.
• /stats-pi — show export-backed estimated initial prompt input token breakdown. It creates a temporary Pi HTML export, decodes its embedded session data, then counts Pi's system prompt text, active provider-level tool schemas, framing overhead, and optional historical calibration (falling back to live context data if export is unavailable).
• /stats-pi detailed — add a concise detail view of the exported initial prompt snapshot: active tool schemas, available-tool prompt entries, skills, context files, metadata, and estimate components.
• /calibrate — start an isolated calibration session with a fixed probe prompt, then update /stats-pi and the footer PI: X tok estimate from the first assistant response usage. /calibrate current reuses the current branch if it already has a suitable first-turn usage sample.
• /stats-last [days|all] — show non-zero daily usage graph.
• /stats-most-expense [days|all] — show most expensive sessions.
• /stats-model-compare [days|all] — show model token/cost comparison.
• /stats-cost-trend [days|all] — show cost trend and projections.
• /stats-cache [days|all] — show cache efficiency and token mix.

Prompt input estimate

/stats-pi and the PI: ~X tok value in /stats estimate the full initial model input, not just raw prompt text. /stats-pi prefers Pi's own HTML export data for the exact exported system prompt and active tool definitions; it falls back to live context data when a temporary export cannot be produced, so it can still be run before any LLM prompt in a fresh session.

The token calculation is intentionally provider-agnostic:

promptTextTokens = weighted text estimate of the system prompt (from exported session data when available)
toolSchemaTokens = weighted text estimate of active tool definitions JSON (from exported session data when available)
framingTokens = conservative message/request framing allowance
baseEstimate = promptTextTokens + toolSchemaTokens + framingTokens
estimatedInitialInput = baseEstimate × historicalCalibrationMultiplier

The historical multiplier is learned opportunistically from future sessions by comparing the pre-call estimate with the provider-reported first assistant usage.input + usage.cacheRead + usage.cacheWrite after subtracting the first user prompt estimate. /calibrate performs the same calculation on demand by opening an isolated session and sending a fixed probe prompt; /calibrate current can reuse the current branch once its first assistant response has usage data. Without samples, /stats-pi reports an uncalibrated estimate and a conservative range. Provider-reported usage in Pi session JSONL remains the authoritative post-call value.

Tools

None.

Example view

/stats 7
Token usage — last 7 days

May 06  in 18k  out 4k   $0.11  ████
May 07  in 42k  out 9k   $0.29  █████████
May 08  in 12k  out 2k   $0.06  ██

Total: 72k input, 15k output, $0.46
Cache hit rate: 38%

Use it to understand which days, sessions, and models are driving token volume and cost.