A workflow for running and maintaining Earn end-to-end tests, which verify complete Earn user journeys, on Ledger Wallet Desktop with Playwright and Mobile with Detox.

Use it for Earn v2 dashboard, staking, and delegation tests; related page objects and flows; and failures involving selectors, amounts, windows, webviews, or feature flags.

It combines test execution, failure investigation, and maintenance guidance for the Earn test suites instead of treating each failure as an isolated issue.

How LedgerHQ/ledger-live configures Claude Code and Codex

Reuse

Borrowing it

Nothing to install: this file belongs to LedgerHQ/ledger-live. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file

curl -O https://raw.githubusercontent.com/LedgerHQ/ledger-live/develop/.agents/skills/e2e-earn/SKILL.md

Clone the repo

git clone --depth 1 https://github.com/LedgerHQ/ledger-live

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/ledgerhq/ledger-live/e2e-earn/github.svg)](https://agentmods.dev/skills/ledgerhq/ledger-live/e2e-earn)

Your own site

<a href="https://agentmods.dev/skills/ledgerhq/ledger-live/e2e-earn"><img src="https://agentmods.dev/badge/skills/ledgerhq/ledger-live/e2e-earn/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/ledgerhq/ledger-live/e2e-earn"><img src="https://agentmods.dev/badge/skills/ledgerhq/ledger-live/e2e-earn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,863 The whole file, excluding the scripts and references it only reads on demand.

Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 3 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

high Tool Misuse · line 142
Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).

Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
medium MCP Rug Pull · line 144
npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.

Fix: Pin the version: npx @scope/[email protected]
medium MCP Rug Pull · line 149
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00078	$0.03863
Opus 5	$0.00039	$0.01931
Sonnet 5	$0.00016	$0.00773
Haiku 4.5	$0.00008	$0.00386

Measured 9d ago against content hash 49f423667b29, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade C, and why

e2e-earn scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Recursive force deletehighDestructive command

rm -rf with a variable or a broad path is one typo away from removing the wrong tree.

rm -rf apps/ledger-live-desktop/.webpack && pnpm desktop build:testing

.agents/skills/e2e-earn/SKILL.md · 188 lines

How it starts

The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Earn E2E Skill

You are both a test runner and a maintenance guide for the Earn E2E suites. When invoked as a command (/e2e-earn), default behaviour is to run the relevant Earn tests locally and triage any failures — but always confirm scope with the user first (see "Running the Earn E2E tests").

Use this skill when

Running the Earn E2E tests locally (earn.v2, delegate, or both) and triaging failures.
Updating Earn specs/page objects in e2e/desktop or e2e/mobile.
Refactoring Earn page objects or moving reusable flow logic out of specs.
Adapting the Earn v2 dashboard flows (ice/cold/hot start, deposit, withdrawal, provider selection, native vs redirect staking).
Fixing PR review comments about dead code, selector ownership, brittle amounts, webview/window context, or feature-flag drift.

Running the Earn E2E tests

This is the primary command behaviour. Follow these steps in order.

Step 1 — Confirm scope with the user

Do not assume. Ask which subset to run before doing anything heavy:

Desktop:
- earn.v2 only — pnpm e2e:desktop test:playwright earn.v2
- delegate only — pnpm e2e:desktop test:playwright delegate
- both — run earn.v2 then delegate
- optionally narrow with a --grep (e.g. a single currency/provider): pnpm e2e:desktop test:playwright earn.v2 --grep "Ethereum"
Mobile (heavier — requires a built app + simulator/emulator): ask whether to include it, and if so iOS (debug) or Android (release). Defer build/setup to /e2e-mobile-onboard.

The earn.v2 / delegate argument is a path substring filter Playwright matches against spec file paths — earn.v2 matches tests/specs/earn.v2.spec.ts, delegate matches tests/specs/delegate.spec.ts.

Step 2 — Verify environment (Speculos mode, MOCK=0)

These must be set in the shell that runs the tests. If any are missing, point the user to /e2e-desktop-onboard (or /e2e-mobile-onboard) rather than guessing.

Variable	Required value / note
`MOCK`	`0` (Speculos mode)
`SEED`	Non-empty. NEVER print or echo it. Read at runtime (e.g. from 1Password).
`COINAPPS`	Path to a cloned, up-to-date `coin-apps` repo
`SPECULOS_IMAGE_TAG`	See "Speculos image" in Troubleshooting — pin to avoid `:latest` drift
`SPECULOS_DEVICE`	e.g. `nanoSP` (options: nanoS, nanoSP, nanoX, stax, flex, nanoGen5)

Read the full file on GitHub · 188 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

9d ago First seen · 188 lines · 78 tokens per session scan C 49f423667b29

Subscribe to this mod's changes

e2e-earn is a skill published in the GitHub repository LedgerHQ/ledger-live (617 stars, last pushed today), licensed MIT. It adds 78 tokens to every session and 3,863 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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ledger-live: Skill for Claude Code