Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add irahardianto/awesome-agv --skill test-e2egit clone --depth 1 https://github.com/irahardianto/awesome-agvWrote 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.
[](https://agentmods.dev/skills/irahardianto/awesome-agv/test-e2e)<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/test-e2e"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/test-e2e/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.
<a href="https://agentmods.dev/skills/irahardianto/awesome-agv/test-e2e"><img src="https://agentmods.dev/badge/skills/irahardianto/awesome-agv/test-e2e.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00042 | $0.01169 |
| Opus 5.5 | $0.00017 | $0.00468 |
| Sonnet 5.5 | $0.00008 | $0.00234 |
| Haiku 4.5 | $0.00004 | $0.00117 |
Grade A, and why
test-e2e scanned grade A with 0 findings 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 2d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
End-to-End Testing — Mobile & Web
Entry-point guard: This is a user-invoked command skill (
/test-e2e). If you are a spawned subagent, do NOT execute this skill — return to your assigned scope card.
Purpose
Platform-adaptive E2E testing for complete user journeys. Covers web (desktop and mobile browsers) and native mobile (iOS, Android). Use the /test-unit skill for unit tests and the /test-integration skill for integration tests.
When to Use
- New feature affecting user-facing flows
- Pre-release E2E verification
- Critical user journey regression testing
- Cross-platform feature parity validation
When NOT to Use
- Unit test gaps — use
/test-unitskill - Integration test gaps — use
/test-integrationskill - Need test scenarios first — use
/test-scenariosskill
Phases
Phase 1: PLAN
Objective: Identify critical user journeys and map to platforms.
- Identify critical user journeys from requirements, PRD, or existing user flows
- For each journey, determine target platforms:
- Web: desktop browsers (Chrome, Firefox, Safari), mobile browsers (Chrome Android, Safari iOS)
- Mobile native: iOS, Android
- Define test matrix (journey × platform). Prioritize: P0 journeys tested on all target platforms
- Present plan to user for confirmation
Gate: User-approved journey list with platform mapping.
Phase 2: SETUP
Objective: Configure E2E test infrastructure per platform.
Web Setup
- Load
browser-automationskill - Configure Playwright: browsers, viewport sizes, base URL
- Set up auth state management (login once, reuse across tests)
- Configure screenshot and recording capture for evidence
Mobile Setup
- Load
mobile-testingskill - Configure test framework based on stack:
- Flutter:
integration_test+ Patrol for native interactions - React Native: Detox or Maestro
- Native iOS/Android: XCUITest/Espresso or Maestro (cross-platform)
- Flutter:
- Configure device/emulator targets
- Set up golden file baseline if visual regression testing is needed
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.
- 2d ago First seen · 128 lines · 42 tokens per session scan A ede336ff8361
test-e2e is a skill published in the GitHub repository irahardianto/awesome-agv (156 stars, last pushed 2d ago), licensed MIT. It adds 42 tokens to every session and 1,169 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-15.
Other skills, from other repositories
quality-engineering-appium-mcp
Drives iOS/Android mobile devices via Appium MCP. Runs a preflight, picks local emulator/simulator/USB or a device cloud, captures screenshots/page source into a named evidence dir. Use for verifying mobile bugs, E2E tests, and navigating real device clouds (LambdaTest/BrowserStack).
verify-bug
Post-merge UAT verification workflow. Walks JIRA reproduce steps, performs comparative audits (Before/After), attaches evidence to JIRA, and transitions status on PASS.
test-loop
Plan, generate, and heal an executable E2E test suite from approved acceptance criteria (web and mobile).
verify-work
Verify feature, bug, UI, API, mobile, security, or deployment work against acceptance criteria.
uat-signoff
Walk a business/UAT approver through a plain-language demo of a verified feature and capture business acceptance signoff.
zephyr-coverage-analysis
Analyze Zephyr test case coverage for a Jira user story and produce a QE management report with metrics, risk scoring, and prioritized recommendations.