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Run E2e Ci

Organization
LedgerHQ
run-e2e-ci

Trigger the on-demand E2E / Coin Tester CI workflows (Desktop E2E, Mobile E2E, Coin Tester) on a branch and post a PR comment. Use when asked to "run e2e", "trigger e2e on CI", or "run coin testers" for a PR/branch.

Overview

PublisherLedgerHQ
Repositoryledger-live
Skill namerun-e2e-ci
Stars
622
Forks
491
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Run E2e Ci 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.
    https://github.com/LedgerHQ/ledger-live/tree/develop/.agents/skills/run-e2e-ci
  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/LedgerHQ/ledger-live.git /tmp/ledger-live
mkdir -p .claude/skills
cp -r /tmp/ledger-live/.agents/skills/run-e2e-ci .claude/skills/run-e2e-ci
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Run E2e Ci 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 Run E2e Ci 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 Run E2e Ci 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.

Run E2E / Coin Tester on CI

When: for changes that impact the e2e apps (desktop/mobile/coin logic), once the PR is getting ready for review. Desktop & Mobile E2E only run on a schedule + manual dispatch, so trigger them deliberately. Coin Tester already auto-runs on PRs for affected coins — only dispatch it to force a chain or re-run.

Scope it down first — filtered is the default, full suite is the exception

Always run the smallest subset that covers the change. The full suite is slow, expensive, and competes for shared on-chain accounts, so run it only for genuinely cross-cutting changes — and when you do, say why in the PR comment. Derive the scope from the diff:

What the diff touchesHow to scope
One coin module (libs/coin-modules/coin-solana)test_filter=@solana + Coin Tester chain=solana
A coin family (**/families/evm)test_filter=@family-evm (or @generic-coin-framework)
A specific spec / flow (swap, subAccount, newSendFlow)test_filter with the tag or spec path
Everything one team ownsteam=<slug> — ANDs with test_filter, Smoke and the device
Platform-specific (Android manifest, iOS plist)narrow Mobile tests_type (Android Only / iOS Only); skip the other surfaces
Desktop-only or Mobile-only changerun only that surface; skip the others + Coin Tester
Shared / cross-cutting (build tooling, TS upgrade, currency core)full suite — and add a one-line rationale

Filter tags: @bitcoin, @family-evm, @solana, @generic-coin-framework, @smoke, … See the test-filter guide for the grammar and the mobile (whole spec files by path/@-tag) vs desktop (test titles) matching difference.

team vs test_filter. test_filter is an OR over its patterns; the team dropdown is an AND that narrows the run to one team's specs. Combine them: team=bst + test_filter=@bitcoin runs only the bitcoin specs BST owns. Slugs: bst, buy-and-sell, coin-integration, earn, engagement, swap, wallet-xp (all is the default and changes nothing). List them with their spec counts:

bash
node e2e/tooling/filter/resolve.mjs --list-teams --check-dir e2e/desktop/tests --runner playwright
node e2e/tooling/filter/resolve.mjs --list-teams --check-dir e2e/mobile/specs  --runner detox

Selection is per spec file, so a spec two teams share runs for both. bst and coin-integration each own about half the suite — pair those with a coin tag or you have not narrowed much. Do not put @team-<slug> in test_filter: it is rejected, with a message pointing at the dropdown.

Dispatch

These are workflow_dispatch workflows — no slash-command triggers them. Dispatch with gh, which must run outside the sandbox (dangerouslyDisableSandbox: true).

gh workflow run --ref <branch> is all you need: the workflows checkout inputs.ref || github.sha, so the optional "Specify branch" (ref) field can stay blank — it falls back to the commit --ref points to. Run-names echo the branch, so use gh run list to confirm.

bash
BR=<branch>            # PR head branch
TAGS=<derived-filter>  # e.g. "@solana" — see the table above. Drop -f test_filter ONLY for a justified full suite.
CHAIN=<derived-chain>  # e.g. "solana" or "evm,solana" — derive from the diff; omit -f chain to auto-detect.

# Filtered runs (preferred). Narrow tests_type when the change is platform-specific.
gh workflow run test-ui-e2e-only-desktop.yml --ref "$BR" -f test_filter="$TAGS"
gh workflow run test-mobile-e2e-reusable.yml --ref "$BR" -f test_filter="$TAGS" -f tests_type="iOS & Android" -f speculos_device=nanoX

# Scope to one team (optional, ANDs with the filter above). Omit -f team for the default "all".
gh workflow run test-ui-e2e-only-desktop.yml --ref "$BR" -f team=swap -f test_filter="$TAGS"
gh workflow run test-mobile-e2e-reusable.yml --ref "$BR" -f team=swap -f test_filter="$TAGS" -f tests_type="iOS & Android" -f speculos_device=nanoX

gh workflow run test-coin-tester.yml --ref "$BR" -f chain="$CHAIN"   # or omit -f chain to auto-detect affected coins

gh run list --branch "$BR" --limit 6   # grab the run IDs

Mobile requires tests_type (Android Only|iOS Only|iOS & Android) and speculos_device (nanoS|nanoSP|nanoX|stax|flex|nanoGen5). Desktop defaults to Speculos nanoSP. Coin Tester auto-detects affected coins (or -f chain="evm,solana").

team is optional on both and defaults to all. Some combinations are refused outright, failing the run in the first minute rather than running the wrong thing:

  • invert_filter with team, or with smoke_tests. --grep-invert is applied to the whole pattern, so anything the resolver folds into it is inverted too: with a team you get every other team's specs, and with Smoke you get the whole suite minus the smoke tests. Invert only ever applies to test_filter, so use it alone.
  • @team-<slug> typed into test_filter. Use the dropdown; the error says so.

-f team= only exists on branches that already carry the input — a branch cut before it landed will reject the flag.

Verify the filter took effect: open the run's Summary → Workflow Context. Desktop prints Filtered pattern; Mobile prints both Filtered pattern (your raw input) and Resolved filtered pattern (what the runner selects with). Both print a Team line.

A filter that matches 0 specs is wasted, and the failure mode depends on whether a team is set:

  • No team — resolve.mjs warns "filter has no matches"; on Mobile the test jobs are then skipped and the run still reports success, on Desktop it fails ("No tests executed").
  • With a team — unknown teams and teams that own nothing fail in the first minute (look for Unknown E2E team / E2E selection is empty). On Mobile, an empty team ∩ filter is also a hard error (E2E selection is empty). On Desktop, the resolver can only warn when the team ∩ filter looks empty; the run may still proceed and fail later with "No tests executed".

Fix the filter and re-dispatch.

Post the run on the PR

Fill the Scope column with the actual filter used (never a hardcoded "full suite"), and add a rationale line stating what you scoped to and why the other surfaces were skipped. Post with gh pr comment <pr> --body ... (--edit-last to update):

markdown
## 🧪 Triggered test workflows

Manually dispatched on `<branch>` (commit <short-sha>, `git rev-parse --short HEAD`), scoped to <scope>:

| Workflow | Scope | Run |
|---|---|---|
| [Desktop] E2E Only | `<@tag>` filter + `team=<slug>`, Speculos nanoSP | [run <id>](https://github.com/LedgerHQ/ledger-live/actions/runs/<id>) |
| [Mobile] E2E Only | `<@tag>` filter + `team=<slug>`, <tests_type>, nanoX | [run <id>](https://github.com/LedgerHQ/ledger-live/actions/runs/<id>) |
| [Coin] Test Coin modules | `chain=<...>` (or affected coins) | [run <id>](https://github.com/LedgerHQ/ledger-live/actions/runs/<id>) |

Scope rationale: <which surfaces changed, what you filtered to, and why the rest were skipped>.

Only write "full suite" when you deliberately ran everything, and keep the rationale line explaining why the change is cross-cutting enough to need it.

Frequently asked questions

What does the Run E2e Ci AI skill do?

Trigger the on-demand E2E / Coin Tester CI workflows (Desktop E2E, Mobile E2E, Coin Tester) on a branch and post a PR comment. Use when asked to "run e2e", "trigger e2e on CI", or "run coin testers" for a PR/branch.

Why use Run E2e Ci on TypingMind?

Because you install it once and use it with any model. Run E2e Ci 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 Run E2e Ci in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LedgerHQ/ledger-live/tree/develop/.agents/skills/run-e2e-ci. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Run E2e Ci?

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 Run E2e Ci?

As many as you like. As long as a model supports skills, you can use Run E2e Ci with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Run E2e Ci AI skill free?

Yes. It is published on GitHub by LedgerHQ 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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