eas-update-insights

name: eas-update-insightsdescription: "Check the health of published EAS Updates: crash rates, install/launch counts, unique users, payload size, and the split between embedded and OTA users per channel."risk: criticalsource: https://github.com/expo/skills/tree/main/plugins/expo/skills/eas-update-insightssource_repo: expo/skillssource_type: officialdate_added: 2026-07-01license: MITlicense_source: https://github.com/expo/skills/blob/main/LICENSE

EAS Update Insights

Query the health of published EAS Updates directly from the CLI: launches, failed launches, crash rates, unique users, payload size, the embedded-vs-OTA user split per channel, and the most popular updates per runtime version. The data is the same data that powers the update and channel detail pages on expo.dev; these commands expose it in the terminal in human and JSON form.

When to use this skill

Use this when the user wants to assess the health or adoption of a published EAS Update: crash rates, install counts, unique users, bundle size, or the split between embedded and OTA users on a channel.

Example prompts:

• "How is the latest update doing?"
• "Is the latest update healthy?"
• "Is the new release crashing more than the last one?"
• "How many users are on the latest update vs the embedded build?"
• "Which update is most popular on production right now?"
• "How big is our update bundle?"

Also fits: post-publish rollout monitoring and regression detection.

Don't use when the user needs per-user crash detail or device-level reporting; this skill only exposes aggregate EAS metrics.

Prerequisites

• A project-approved, pinned eas-cli version exposing these commands. Check eas --version and each command’s --help before use; command availability can change.
• Logged in: eas login.
• For channel:insights: run from an Expo project directory (the command resolves the project ID from app.json). update:insights only needs a login.

Commands at a glance

All of these support --json --non-interactive for programmatic parsing.

Discovering IDs

Before querying insights for an update group, you need its group ID. Use eas update:list with either --branch <name> (updates on that branch) or --all (updates across all branches). Always pass --json --non-interactive when running non-interactively; without a branch/--all flag the command will otherwise prompt for a branch selection:

# Latest group id across all branches
eas update:list --all --json --non-interactive | jq -er '.currentPage[0].group // error("No update group returned")'

# Latest group id on a specific branch
eas update:list --branch production --json --non-interactive | jq -er '.currentPage[0].group // error("No update group returned")'

The JSON response has a currentPage array with one entry per update group (both platforms of the same publish are collapsed into one entry):

{
  "currentPage": [
    {
      "branch": "production",
      "message": "\"Fix checkout crash\" (1 week ago by someone)",
      "runtimeVersion": "1.0.6",
      "group": "03d5dfcf-736c-475a-8730-af039c3f4d06",
      "platforms": "android, ios",
      "isRollBackToEmbedded": false
    }
  ]
}

Entries also carry codeSigningKey and rolloutPercentage, but only when those features are in use for the group (undefined values are omitted from the JSON output).

When called with --branch <name>, the response also includes name (the branch name) and id (the branch ID) at the top level.

eas update:insights <groupId>

Shows launches, failed launches, crash rate, unique users, launch asset count, and average payload size for a single update group, broken down per platform (iOS, Android), plus a daily breakdown of launches and failures.

Basic use

eas update:insights 03d5dfcf-736c-475a-8730-af039c3f4d06

Flags

JSON output shape

Top level: groupId, timespan (start, end, daysBack), and platforms[] with one entry per platform the group was published to. Each platform entry has updateId, totals (uniqueUsers, installs, failedInstalls, crashRatePercent), payload (launchAssetCount, averageUpdatePayloadBytes), and a daily[] time series of { date, installs, failedInstalls }.

For the complete schema and field reference, see references/update-insights-schema.md.

Fields that matter for health assessment:

• platforms[].totals.crashRatePercent, computed as failedInstalls / (installs + failedInstalls) * 100. Zero by CLI convention when installs + failedInstalls is zero; this is no exposure, not proof of health.
• platforms[].totals.installs and uniqueUsers give the adoption signal.
• platforms[].daily is a time series, useful for spotting a sudden spike in failures.

Errors

• Could not find any updates with group ID: "<id>" — group doesn't exist or you lack access.
• Update group "<id>" has no ios update (available platforms: android) — --platform ios was used but the group wasn't published for iOS.
• EAS Update insights is not supported by this version of eas-cli. Please upgrade ... — the server deprecated a field the CLI relies on. Check the project’s runtime constraints and changelog, then upgrade to a reviewed compatible CLI version; do not mutate the global environment automatically.

eas update:view <groupId> --insights

Extends the standard update:view output with the same per-platform insights, inline.

# Human-readable
eas update:view 03d5dfcf-... --insights
eas update:view 03d5dfcf-... --insights --days 30

# JSON: wrapped as { updates: [...], insights: {...} }
eas update:view 03d5dfcf-... --json --insights

Without --insights, update:view behaves exactly as before — no JSON shape change for existing consumers. The --days / --start / --end flags only apply when --insights is set; passing them alone errors.

eas channel:insights --channel <name> --runtime-version <version>

Shows, per channel, how many users are on the embedded build vs over-the-air updates and which updates are pulling the most traffic. Must be run from an Expo project directory.

Basic use

eas channel:insights --channel production --runtime-version 1.0.6

Flags

JSON output shape

Top level: channel, runtimeVersion, timespan, embeddedUpdateTotalUniqueUsers, otaTotalUniqueUsers, mostPopularUpdates[] (each with rank, groupId, message, platform, totalUniqueUsers), cumulativeMetricsAtLastTimestamp[], plus chart-shaped uniqueUsersOverTime and cumulativeMetricsOverTime objects with labels and datasets.

For the complete schema and field reference, see references/channel-insights-schema.md.

Fields that matter:

• embeddedUpdateTotalUniqueUsers is the count of users running the embedded (binary-bundled) build.
• mostPopularUpdates[] is updates ranked by totalUniqueUsers. Caveat: this is the top-N the server returns; otaTotalUniqueUsers is a sum of that list and may undercount total OTA reach if more than top-N updates are active.
• uniqueUsersOverTime and cumulativeMetricsOverTime are daily data series for charting.

Errors

• Could not find channel with the name <name> — typo or wrong account.
• "No update launches recorded" in the table / empty mostPopularUpdates in JSON — no OTA update has been launched for that channel + runtime yet. This can reflect no exposure, telemetry lag, wrong runtime/project/channel or absent OTA traffic; do not infer embedded-only service from an empty list.

Common workflows

Resolve the actual channel→branch mapping and intended group/runtime before selecting a release. A branch named production is an example, not proof it serves the production channel. Across all branches, the first returned group need not be the intended production release. Keep account/project/group/platform, UTC window and collection time with the report.

Verify the update I just published is healthy

# 1. Grab the latest publish on production
GROUP_ID=$(eas update:list --branch production --json --non-interactive \
  | jq -er '.currentPage[0].group // error("No update group returned")')

# 2. Give it some adoption time (minutes to hours), then check crash rate
eas update:insights "$GROUP_ID" --json --non-interactive \
  | jq '.platforms[] | {platform, installs: .totals.installs, crashRate: .totals.crashRatePercent}'

Compare the crashRate across platforms and against previous releases; sudden spikes or asymmetric behaviour (iOS spiking while Android is flat, or vice versa) is the signal to investigate.

Compare adoption between two channels

for channel in production staging; do
  echo "--- $channel ---"
  eas channel:insights --channel "$channel" --runtime-version 1.0.6 --json --non-interactive \
    | jq '{
        channel,
        embedded: .embeddedUpdateTotalUniqueUsers,
        ota: .otaTotalUniqueUsers,
        topUpdate: .mostPopularUpdates[0]
      }'
done

A failure-rate threshold is an investigation trigger, not a release gate by itself. Require meaningful sample size and comparable observation windows, and inspect independent crash reporting. No launch/failure telemetry or successful metric query can certify an app is healthy. Do not rollback, republish or change rollout percentage from this read-only analysis unless separately authorized.

Detect an investigation signal in a one-day window

eas update:insights "$GROUP_ID" --days 1 --json --non-interactive \
  | jq '.platforms[] | select(.totals.crashRatePercent > 1)'

Summarize group metrics for release notes

eas update:view "$GROUP_ID" --insights --days 30

Human-readable group details plus 30 days of launches/failures per platform — suitable for pasting into a changelog or incident review.

Output tips

• Pipe JSON through jq; payloads are structured for easy filtering.
• --json implies --non-interactive, but passing both is explicit and scripting-friendly.
• Dates in daily[].date are UTC ISO timestamps; the human-readable table renders them as YYYY-MM-DD (UTC).
• The CLI table labels say "Launches" / "Crashes" while JSON uses installs / failedInstalls. Same field, different display name.

Limitations

• Summed OTA users can also double-count one user across multiple active updates within the window, while the top-N list may omit updates. The sum is not a globally deduplicated reach count.
• Unique users across platforms may double-count users who run the same publish on both iOS and Android. The same caveat applies to otaTotalUniqueUsers in channel insights, which is a sum over mostPopularUpdates.
• Fresh publishes may show zeros for a short period while the metrics pipeline catches up.
• Installs are downloads, not launches: the installs / "Launches" field counts users who downloaded the manifest and launch asset. A confirmed run only registers on the user's next update check (typically up to 24h later, depending on the app's update policy). So metrics lag the real-world state slightly.
• Crashes are self-reported: failedInstalls / "Crashes" counts updates that errored during install/launch and were reported on the next update check. Crashes that don't trigger an update request (e.g. process kill before recovery) won't appear.