Borrowing it
Nothing to install: this file belongs to joaovjo/linkedin-mcp-server-ts. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/joaovjo/linkedin-mcp-server-ts/main/.agents/skills/e2e/SKILL.mdgit clone --depth 1 https://github.com/joaovjo/linkedin-mcp-server-tsWrote 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/joaovjo/linkedin-mcp-server-ts/e2e)<a href="https://agentmods.dev/skills/joaovjo/linkedin-mcp-server-ts/e2e"><img src="https://agentmods.dev/badge/skills/joaovjo/linkedin-mcp-server-ts/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/joaovjo/linkedin-mcp-server-ts/e2e"><img src="https://agentmods.dev/badge/skills/joaovjo/linkedin-mcp-server-ts/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 | $0.00910 |
| Opus 5.5 | $0.00017 | $0.00364 |
| Sonnet 5.5 | $0.00008 | $0.00182 |
| Haiku 4.5 | $0.00004 | $0.00091 |
Grade A, and why
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 2mo 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 — 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 — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
E2E Testing
Failure Taxonomy
Every e2e failure is exactly one of:
A. Flaky (test infrastructure issue)
- Race conditions, timing-dependent assertions, stale selectors, missing waits
- Symptom: passes on retry, fails intermittently
B. Outdated (test no longer matches implementation)
- Test asserts old behavior that was intentionally changed; selectors reference removed elements
- Symptom: consistent failure, app works correctly
C. Bug (implementation doesn't match spec)
- Test correctly asserts spec'd behavior, code is wrong
- Only classify as bug when a spec exists to validate against
- If no spec exists, classify as "unverified failure" and report to the user
Fix Rules by Category
Flaky fixes:
- Replace
waitForTimeoutwith auto-waiting locators - Replace brittle CSS selectors with
getByRole/getByLabel/getByTestId - Fix race conditions with
expect()web-first assertions - Fix mock/route setup ordering (before navigation)
- Never add arbitrary delays - fix the underlying wait
- Never add retry loops around assertions - use the framework's built-in retry
Outdated fixes:
- Update test assertions to match current (correct) behavior
- Update selectors to match current DOM/API
- Never change source code - the implementation is correct, the test is stale
Bug fixes:
- Quote the spec section that defines expected behavior
- Fix the source code to match the spec
- Unit tests MUST exist before the fix is complete — write them first if missing (TDD)
- Never change e2e assertions to match buggy code
- Never change API contracts or interfaces without spec backing
- If no spec exists, ask the user: bug or outdated test?
Source Code Boundary
E2e test fixes must not change application logic, API contracts, database schemas, or configuration defaults. The only exception: bug fixes where a spec explicitly defines the correct behavior and unit tests cover the fix.
Workflow
Step 1: Discover Test Infrastructure
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.
- 2mo ago First seen · 106 lines · 42 tokens per session scan A be85925d2f68
e2e is a skill published in the GitHub repository joaovjo/linkedin-mcp-server-ts (4 stars, last pushed 2mo ago), licensed MIT. It adds 42 tokens to every session and 910 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to e2e, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
verify-omnigent
Drive Omnigent the way a user does and prove a behavior with recorded evidence, using an isolated server, runner, and mock model plus a feature map of every user entry point. Load before reproducing a user-facing bug, before claiming a fix works, or when reviewing whether a change covered every surface (session vs.…
test-master
Generates test files, creates mocking strategies, analyzes code coverage, designs test architectures, and produces test plans and defect reports across functional, performance, and security testing disciplines. Use when writing unit tests, integration tests, or E2E tests; creating test strategies or automation…
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
dogfood
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to dogfood, QA, exploratory test, find issues, bug hunt, or test this app on mobile.
cua-driver
Use Cua Driver for desktop or browser tasks that are awkward or unavailable through Bash/APIs, or when the user explicitly wants GUI interaction: app testing, visual bug reproduction, form filling, calendar entry, screenshots, and demo recording. Also covers Cua setup; not OpenAI Codex Computer Use or web research.
test-warp-ui
Guides testing Warp UI features and changes using the computer use tool. Use this skill only when computer-use testing was requested (explicit request or accepted offer) and the computeruse tool is available to the agent. Covers launching Warp and verifying UI behavior.