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 codebygarv/Ai-skills --skill e2e-scenario-plannergit clone --depth 1 https://github.com/codebygarv/Ai-skillsWrote 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/codebygarv/ai-skills/e2e-scenario-planner)<a href="https://agentmods.dev/skills/codebygarv/ai-skills/e2e-scenario-planner"><img src="https://agentmods.dev/badge/skills/codebygarv/ai-skills/e2e-scenario-planner.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.00392 |
| Opus 5 | $0.00014 | $0.00196 |
| Sonnet 5 | $0.00006 | $0.00078 |
| Haiku 4.5 | $0.00003 | $0.00039 |
Grade A, and why
e2e-scenario-planner 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 5d 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.
What it actually says
Purpose
Architect a resilient, non-flaky End-to-End (E2E) test suite using Playwright or Cypress focused on core business user journeys (authentication, checkout, workspace invitation, data export) while keeping test run times fast.
When to Use
- Establishing an automated E2E smoke and regression suite.
- Refactoring brittle E2E tests filled with arbitrary
sleep()calls and unstable CSS selectors. - Prioritizing which test scenarios must run on pull requests vs nightly builds.
What to Analyze
- Critical User Journeys (CUJs): Identify flows where a failure stops users from converting or using core features.
- Stable Locators: Prioritize user-facing accessibility locators (
getByRole,getByLabel,getByText) over brittle CSS hierarchy selectors. - Authentication State Re-use: Use storage state fixtures to log in once via API rather than UI login on every test.
- Network Mocking vs Real APIs: Mocking slow third-party widgets (Stripe, Intercom, Analytics) while hitting real internal APIs.
- Parallelism & Sharding: Splitting tests across CI runners for sub-5-minute runs.
Output Format
- CUJ Coverage Matrix: User Journey, Priority (P0/P1/P2), Execution Frequency.
- Playwright Test Suite Code: Clean Page Object Model (POM) implementation.
- Flakiness Mitigation Rules: Explicit wait strategies, retries, and network intercept patterns.
Avoid
- Automating every minor edge case via E2E instead of unit/integration tests (Testing Pyramid inversion).
- Using hardcoded
page.waitForTimeout(5000)sleeps.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 34 lines · 28 tokens per session scan A c199ded0a8f5
e2e-scenario-planner is a skill published in the GitHub repository codebygarv/Ai-skills (25 stars, last pushed 19d ago), licensed MIT. It adds 28 tokens to every session and 392 once invoked, about $0.0001 per session on Opus 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 2026-09-03.
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