agentleFS
chattermatechattermate/chattermate.chat/.claude/skills/write-e2e-test/SKILL.md

write-e2e-test

Skill156 starsChanged 31 days agoApache-2.0View on GitHub

Write or change a ChatterMate Playwright end-to-end test (e2e/). Use when adding e2e coverage for a feature, fixing a failing e2e spec, or adding a page object, fixture or fake-LLM script.

5 other projects use a version of this.

Adopted by

Most recently taken up by

  • Other versions5 projects use a version of this.

    Word for word · 0

    None found.

    Edited · 0

    None found.

    Wrote their own version · 5

    1. usebruno/bruno★ 48ksame name
    2. baserow/baserow★ 6.3ksame name
    3. authgear/authgear-server★ 2.1ksame name
    4. agentconfig/agentconfig.org★ 7same name
    5. tlolkema/skills-explained★ 4same name

    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repositoryOne other file it gives its agents.

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 5 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Prove it is stable: before finishing: cd e2e && npx playwright test specs/<feature> --repeat-each=10 && npm run lint && npm run typecheck …

    line 25

“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Writing a ChatterMate e2e test

The suite lives in e2e/. Read e2e/AUTHORING.md first — it is the source of truth for layout and rules. The essentials:

  1. Specs speak product language. specs/<feature>/<name>.spec.ts, imports only from @e2e, no selectors (page.locator) and no raw HTTP — lint fails otherwise. Title ends with tags: @<feature>, plus @smoke only for the few journeys that must run on every PR.
  2. Selectors go in src/pages/ (a screen) or src/components/ (shared UI). Prefer getByRole/getByLabel; if the element has no accessible name, add an aria-label to the Vue component rather than reaching for CSS. CSS needs a one-line "why" comment.
  3. Seed through src/api/, assert through the UI. Need a new endpoint? Add a function to the matching src/api/*.ts module.
  4. Script the AI with llm.script(agent, s => …) — reply, handOver, createTicket, callTool(...).thenReply(...). Replies are structured ChatResponse JSON; handover and tickets are boolean fields, not tool calls.
  5. Fixtures give each test its own agent + widget. test.use({ agentSettings: { transferToHuman: true } }) for handover. inbox is Sam Agent's dashboard in a second browser. Never change org-wide settings outside a *.org.spec.ts.
  6. Prove it is stable before finishing: cd e2e && npx playwright test specs/<feature> --repeat-each=10 && npm run lint && npm run typecheck (start the stack once with ./scripts/stack.sh up; in a Claude worktree pass E2E_PYTHON and E2E_FRONTEND_NODE_MODULES pointing at the main checkout, and run with the sandbox disabled — Postgres, ports and the node_modules symlink need it).
  7. Update e2e/FEATURES.md — the row for the journey, with the spec path and ✅.

Enterprise specs live in the private enterprise_frontend repo under e2e/, import only from @e2e, and must never be added to this public repo.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.