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Eas Update Insights

OrganizationPopular
expo
eas-update-insights

EAS service (paid). Check the health of published EAS Update: crash rates, install/launch counts, unique users, payload size, and the split between embedded and OTA users per channel. Use when the user asks how an update is performing, whether a rollout is healthy, how many users are on the embedded build vs OTA, or wants to gate CI on update health.

Overview

Publisherexpo
Repositoryskills
Skill nameeas-update-insights
Stars
2.5K
Forks
146
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by expo on GitHub. Read the source before you install it.

Installation

Install the Eas Update Insights AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/expo/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/expo/skills/eas-update-insights .claude/skills/eas-update-insights
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Eas Update Insights in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Eas Update Insights on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Eas Update Insights is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

EAS Update Insights

EAS service - costs apply. Insights cover updates published through EAS Update, a paid Expo Application Services product with free-tier limits. Update delivery and the data behind these commands count against your plan's EAS Update usage. Review https://expo.dev/pricing.

Query the health of published EAS Update 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

  • eas-cli installed (npm install -g eas-cli).
  • 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

CommandPurpose
eas update:listDiscover recent update groups, their group IDs, and branch names
eas update:insights <groupId>Per-platform launches, failed launches, crash rate, unique users, payload size, daily breakdown
eas update:view <groupId> --insightsUpdate group details + the same metrics appended
eas channel:insights --channel <name> --runtime-version <version>Embedded/OTA user counts, most popular updates, cumulative metrics for a channel + runtime

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:

bash
# Latest group id across all branches
eas update:list --all --json --non-interactive | jq -r '.currentPage[0].group'

# Latest group id on a specific branch
eas update:list --branch production --json --non-interactive | jq -r '.currentPage[0].group'

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

json
{
  "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

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

Flags

FlagDescription
--days <N>Look back N days. Default: 7. Mutually exclusive with --start/--end.
--start <iso-date> / --end <iso-date>Explicit time range, e.g. --start 2026-04-01 --end 2026-04-15.
--platform <ios|android>Filter to a single platform. Omit to see all platforms in the group.
--jsonMachine-readable output. Implies --non-interactive.
--non-interactiveRequired when scripting.

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 when there are no installs.
  • 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. Run npm install -g eas-cli@latest.

eas update:view <groupId> --insights

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

bash
# 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

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

Flags

FlagDescription
--channel <name>Required. The channel name (e.g. production, staging).
--runtime-version <version>Required. Match exactly what was published. Check runtimeVersion values in update:list.
--days <N>Look back N days. Default: 7.
--start / --endExplicit time range, like update:insights.
--json / --non-interactiveMachine-readable output.

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. Usually means the channel is still serving the embedded build only.

Common workflows

Verify the update I just published is healthy

bash
# 1. Grab the latest publish on production
GROUP_ID=$(eas update:list --branch production --json --non-interactive \
  | jq -r '.currentPage[0].group')

# 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

bash
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

Detect a rollout regression in the last 24 hours

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

Summarize group metrics for release notes

bash
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

  • 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.

Submitting Feedback

If you encounter errors, misleading or outdated information in this skill, report it so Expo can improve:

bash
npx --yes submit-expo-feedback@latest --category skills --subject "eas-update-insights" "<actionable feedback>"

Only submit when you have something specific and actionable to report. Include as much relevant context as possible. If an AI agent repeatedly failed or the user had to take over an Expo task, load the expo-skill-feedback skill and follow its eval-candidate flow instead of reusing the command above.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Eas Update Insights AI skill do?

EAS service (paid). Check the health of published EAS Update: crash rates, install/launch counts, unique users, payload size, and the split between embedded and OTA users per channel. Use when the user asks how an update is performing, whether a rollout is healthy, how many users are on the embedded build vs OTA, or wants to gate CI on update health.

Why use Eas Update Insights on TypingMind?

Because you install it once and use it with any model. Eas Update Insights is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Eas Update Insights in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/expo/skills/tree/main/plugins/expo/skills/eas-update-insights. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Eas Update Insights?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Eas Update Insights?

As many as you like. As long as a model supports skills, you can use Eas Update Insights with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Eas Update Insights AI skill free?

Yes. It is published on GitHub by expo under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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