agentleFS
Harshvardhan86Harshvardhan86/claude-wave-plugin/skills/teet-verify/SKILL.md

teet-verify

Skill14 starsChanged 16 days agoMITView on GitHub

Use only after the user has approved the GREEN screenshot. Performs True End-to-End Testing across backend, frontend, DB, and external services with computed-style and boundingBox assertions, producing the final wave evidence pack.

The file

We split this skill into the rules it gives an agent

7 rules. About 0 are common, 5 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈5

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use the project's documented startup procedure (do not invent one)

    line 30
  2. Distinctive:

    Wait until every service reports healthy

    line 31
  3. Distinctive:

    Verify the final state is fully correct, not just "mostly done"

    line 39
  4. Distinctive:

    Verify the side effect via direct DB query / API call / log inspection

    line 57
  5. Very specific:

    Read .wave/ac.md. For each AC, mark in .wave/teet.md:

    line 70
  6. Very specific:

    No toBeVisible()-only assertions: Always pair with computed-style or boundingBox.

    line 104
  7. Distinctive:

    Visual baselines must be user-approved before commit: If you create a new golden screenshot, surface it for user approval — don't lock a …

    line 106

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “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

TEET — True End-to-End Testing

TEET is the final phase. It is the independent re-verification of the wave: a fresh agent (you) runs the full app stack and asserts every AC with the strictest possible evidence — computed styles, bounding boxes, real network calls, real persistence.

Why this is its own phase

GREEN's job is to make tests pass. TEET's job is to distrust GREEN and re-verify against the original AC. The same agent that wrote the implementation cannot reliably grade it. Lean, dedicated context per phase is the rule.

Pre-condition

The orchestrator only invokes you after the user typed approval of the GREEN screenshot. If you're invoked without that, refuse and tell the orchestrator to gate.

Inputs

  • .wave/ac.md
  • .wave/dr.md
  • .wave/green.md
  • .wave/screenshots/green-*.png

Procedure

1. Bring up the entire stack

  • Frontend, backend, DB, queues, external service emulators if used
  • Use the project's documented startup procedure (do not invent one)
  • Wait until every service reports healthy

2. Walk every route, click every interactive element

For multi-step flows (wizards, onboarding, dashboards):

  • Click through the entire flow end-to-end with Playwright
  • Take a screenshot at every step, save to .wave/screenshots/teet-step-N-<route>.png
  • Verify the final state is fully correct, not just "mostly done"
  • Reload the page mid-flow — does state persist correctly?
  • Click every button, link, toggle and verify its observable effect

3. Computed-style and boundingBox assertions

For every visual AC from DR:

  • Assert with getComputedStyle() not toBeVisible()
  • Assert with boundingBox() for sizes and positions
  • Compute total effective CSS bytes for the route — if below a project-set threshold, flag (the page is unstyled)
  • Compare each route screenshot against a known-good reference if one exists (e.g., the previous successful wave's screenshot)

4. Cross-system assertions

For ACs that span systems (e.g., "toggle dark mode → persisted in DB → visible after fresh login"):

  • Drive the action through the UI
  • Verify the side effect via direct DB query / API call / log inspection
  • Log evidence in .wave/teet.md

5. Negative cases

For each AC's "rejects" clause, attempt the failure scenario and prove it does fail:

  • Disable the toggle while persistence is in flight — does the optimistic state revert correctly?
  • Rapid-fire-click — no double-fire?
  • Keyboard-only navigation — fully operable?

6. Diff vs original AC

Read .wave/ac.md. For each AC, mark in .wave/teet.md:

  • ✅ verified, with evidence reference (screenshot path / log line / DB query result)
  • ⚠️ partial, with what's missing
  • ❌ failed, with screenshot of the failure

7. Output

Write .wave/teet.md:

# TEET Report — <feature>

## Stack health
<services up, response times, etc.>

## AC-by-AC verification
- AC-1: ✅ — see screenshots/teet-step-1-settings.png + getComputedStyle assertion log
- AC-2: ⚠️ — persistence works but takes 800ms (AC said <500ms)
- AC-3: ❌ — keyboard navigation skips the toggle; see screenshots/teet-keyboard-fail.png

## Evidence
- Screenshots: <list>
- DB queries run: <list>
- Network requests captured: <list>

## Recommendation
- PASS / PARTIAL PASS / FAIL

Return to orchestrator with this verdict. The orchestrator escalates any ⚠️ or ❌ to the user as a P0 — not "minor", not "follow-up".

Hard rules

  • All bugs are P0. A ⚠️ is a bug. There is no "we'll get to it later".
  • No toBeVisible()-only assertions. Always pair with computed-style or boundingBox.
  • No mock infrastructure for TEET. Real services, real DB, real network.
  • Visual baselines must be user-approved before commit. If you create a new golden screenshot, surface it for user approval — don't lock a broken state in as the reference.

Anti-patterns

  • Re-running GREEN's tests and calling that "TEET" — TEET writes new assertions
  • Inferring success from log absence ("no errors logged, must be working")
  • Skipping the reload-mid-flow check because "state persistence isn't part of this AC" — it always is
  • Reporting PASS with any ⚠️ unaddressed

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.