analytics

name	analytics
description	Pull Vibe's Aptabase telemetry into a CSV — export a date range with scripts/export_analytics.py, trim it to an exact time window, and read the event set from the source. Use when the user asks about analytics, telemetry, Aptabase, crash or failure rates, affected users, or version and OS breakdowns.

Analytics

Gets you the data. What to do with it is your call — read the question and answer it, rather than producing a fixed report.

Prerequisites

scripts/export_analytics.py calls load_dotenv(), so a .env at the repo root supplies: BASE_URL, AUTH_SECRET, AUTH_NAME, AUTH_EMAIL, APP_KEY, and optionally APTABASE_REGION (defaults to SH).

Export

Dates are UTC and day-granular: --start-date inclusive, --end-date exclusive. For a window in hours, export the days that overlap it and trim afterwards.

uv run scripts/export_analytics.py \
  --start-date YYYY-MM-DD --end-date YYYY-MM-DD \
  --output scripts/analytics_raw.csv

Also takes --format csv|parquet and --build-mode release|debug (default release — debug rows are developer machines).

Columns

Column
timestamp	UTC
event_name	see below
user_id	anonymous
app_version
os_name / os_version	e.g. Windows, macOS, Linux Mint
string_props	JSON; failures carry error_message with the full string
numeric_props	JSON

The event set

Read it from the code, never from memory — names change:

• desktop/src-tauri/src/analytics.rs
• desktop/src/lib/analytics.ts

The shapes are <action>_started → <action>_succeeded / <action>_failed, and <component>_<failure_type> for infrastructure faults. Work out which of the current names are starts, successes and failures before computing any rate.

Trimming to an exact window

uv run --with pandas python - <<'PY'
from datetime import timedelta
import pandas as pd

df = pd.read_csv("scripts/analytics_raw.csv")
df["timestamp"] = pd.to_datetime(df["timestamp"], utc=True)
df = df[df["timestamp"] >= df["timestamp"].max() - timedelta(hours=48)]
df.to_csv("scripts/analytics_window.csv", index=False)
print(len(df), df["timestamp"].min(), df["timestamp"].max())
PY

From here, inline pandas passes over the CSV answer most questions. A few things worth knowing when you interpret them: a handful of users can repeat the same input mistake and dominate an event-level error rate, so per-user counts often say more than raw ones; and a failure rate is only meaningful against its own start event, when that lifecycle is present in the data.