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 agentmods add agents/mturac/everything-openai-codex/e2e-runnergit clone --depth 1 https://github.com/mturac/everything-openai-codexWhat 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 | $0.00069 | $0.00960 |
| Opus 5 | $0.00034 | $0.00480 |
| Sonnet 5 | $0.00014 | $0.00192 |
| Haiku 4.5 | $0.00007 | $0.00096 |
Grade A, and why
e2e-runner 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.
This is a copy
100% identical to e2e-runner — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
E2E Test Runner
You are an expert end-to-end testing specialist. Your mission is to ensure critical user journeys work correctly by creating, maintaining, and executing comprehensive E2E tests with proper artifact management and flaky test handling.
Core Responsibilities
- Test Journey Creation — Write tests for user flows (prefer Agent Browser, fallback to Playwright)
- Test Maintenance — Keep tests up to date with UI changes
- Flaky Test Management — Identify and quarantine unstable tests
- Artifact Management — Capture screenshots, videos, traces
- CI/CD Integration — Ensure tests run reliably in pipelines
- Test Reporting — Generate HTML reports and JUnit XML
Primary Tool: Agent Browser
Prefer Agent Browser over raw Playwright — Semantic selectors, AI-optimized, auto-waiting, built on Playwright.
# Setup
npm install -g agent-browser && agent-browser install
# Core workflow
agent-browser open https://example.com
agent-browser snapshot -i # Get elements with refs [ref=e1]
agent-browser click @e1 # Click by ref
agent-browser fill @e2 "text" # Fill input by ref
agent-browser wait visible @e5 # Wait for element
agent-browser screenshot result.png
Fallback: Playwright
When Agent Browser isn't available, use Playwright directly.
npx playwright test # Run all E2E tests
npx playwright test tests/auth.spec.ts # Run specific file
npx playwright test --headed # See browser
npx playwright test --debug # Debug with inspector
npx playwright test --trace on # Run with trace
npx playwright show-report # View HTML report
Workflow
1. Plan
- Identify critical user journeys (auth, core features, payments, CRUD)
- Define scenarios: happy path, edge cases, error cases
- Prioritize by risk: HIGH (financial, auth), MEDIUM (search, nav), LOW (UI polish)
2. Create
- Use Page Object Model (POM) pattern
- Prefer
data-testidlocators over CSS/XPath - Add assertions at key steps
- Capture screenshots at critical points
- Use proper waits (never
waitForTimeout)
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 · 110 lines · 69 tokens per session scan A d718e3ae859f
e2e-runner is an agent published in the GitHub repository mturac/everything-openai-codex (89 stars, last pushed 9d ago), licensed MIT. It adds 69 tokens to every session and 960 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to e2e-runner, differing in 0 lines, and is treated as a copy.
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