evidence-driven-testing: Design & UI for AI Agents | OpenAgentSkill

Overview

Records visual proof while testing UI behavior — the agent tests the app hands-on via computer use while a screen recording with structured test/assertion annotations captures the session — then posts the video and a results summary to the PR and tracker issue. Use whenever a change needs verifiable evidence that it works, instead of prose claims — including headless environments (scripted screenshots and probes) and non-UI changes (measured numbers, output pairs).

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Evidence-Driven Testing

Record annotated proof of behavior, then attach it to the PR and tracker issue.

The recording is the capture of you testing the app via computer use: start the recorder, then drive the app yourself — click, type, navigate — through each test target. Every action in the video is the test being performed live; the recording has no value as evidence unless it shows that interactive session. If the harness has no computer-use tools but a GUI exists, drive the app with cua-driver instead (see below) — it is still your live session.

Inputs

Test targets (required): The behaviors/flows to verify, phrased as testable statements.
PR / issue (optional): Where to post the evidence. If omitted, deliver to the requester only.

Instructions

1. Prepare the screen

Maximize the browser/app window; close popups, notifications, and extra panels.
Navigate to the starting state (logged in, correct page) BEFORE recording, unless setup itself is under test.

2. Start recording

Begin the screen recording before the first meaningful action.
Add a setup annotation describing the starting context, e.g. "Logged in, navigating to connectors page".

3. Test via computer use, annotating as you go

Perform every interaction through computer use on the live app — the recording captures your session, so the testing and the evidence are the same act. Work at a watchable pace: let the UI settle after each action so state changes are visible on video.
At each named test's start, add a test_start annotation in Jest style: It should execute the tool directly when permission is 'always'.
After each check, add an assertion annotation with result passed, failed, or untested.
Rules for assertions:
- One assertion per meaningful state change — consolidate, don't annotate per UI label.
- Use "Precondition: ..." assertions to establish starting state.
- Keep under ~80 characters, high-signal.
- If a test cannot run (missing prerequisite, expired auth window), mark it untested with the reason — never skip silently.

4. Stop and review

Stop recording after the final assertion.
Confirm the recording captured the key moments before sharing.

5. Post the evidence

Write a short report: what was tested, environment + exact commit, pass/fail per test, caveats.
Post the video + summary as a PR comment (embed in the PR description if it's your PR).
Attach the same video to the tracker issue (Linear/Jira) with a one-line result.
Send the report + recording to the requester.

Guardrails

The video must show the actual test session being driven live. Never present scripted playback, stitched clips, or synthetic footage as a recording; if the harness lacks computer-use tools but a GUI exists, drive via cua-driver; with no GUI at all, use the headless path instead.
Never record a half-covered or tiled window — maximize first.
When verifying a fix, show or reference the old failure alongside the new success.
Always state the exact commit/branch/deployment tested against.

No computer-use tools? Drive with cua-driver (GUI available)

When a display exists but the agent has no built-in computer-use capability, use cua-driver (macOS / Windows / Linux) as the actuator. It is still you testing the app live — the recording rule holds unchanged; only the input mechanism differs.

Verify the setup with cua-driver doctor before recording. If a cua-driver skill is installed, read it and follow its protocol — the snapshot-before-action invariant is mandatory.
Loop per interaction: launch_app → get_window_state (accessibility tree
- screenshot) → act via element_token (click, type_text, press_key) → verify_state for the expected postcondition. Each verify_state check maps 1:1 onto an assertion annotation.
cua-driver recording start <output-dir> / cua-driver recording stop can double as the recorder (the output directory is required, and the daemon must be running: cua-driver serve). Video capture is on by default and is finalized to <output-dir>/recording.mp4 on stop — but on Windows/Linux it shells out to ffmpeg, so a missing ffmpeg or display yields only the per-turn trajectory folders (before/after screenshots, action.json, click.png), no video. After stopping, verify recording.mp4 exists before citing it; if it is absent, fix the recorder or present the per-turn before/after screenshots as numbered captures per the headless protocol.
If no annotation overlay is available on this path, keep the protocol as files: an assertions.md listing each test_start / assertion with its result, exactly as in the headless path.

Headless path (no GUI available)

When the agent has no desktop to record, keep the same assertion discipline; swap the recorder for scripted capture:

Save everything to .artifacts/<task-name>/ (gitignore it — evidence gets uploaded, never committed). Keep the capture script beside the captures so the run is repeatable.
Screenshots: the before-and-after CLI (@vercel/before-and-after) captures URLs or elements and its pairs feed PR embeds directly. In containers/VMs where Chrome fails with "No usable sandbox", set AGENT_BROWSER_ARGS="--no-sandbox".

Video / multi-step flows: a one-off Playwright script, run without adding playwright to the project's dependencies:

npx --yes --package=playwright node record.mjs

(Plain npx playwright node record.mjs fails — node is not a Playwright CLI command; --package=playwright is what puts the module on the path.) Minimal record.mjs:

import { chromium } from "playwright";
const browser = await chromium.launch();
const context = await browser.newContext({
  recordVideo: { dir: ".artifacts/<task-name>/" },
});
const page = await context.newPage();
await page.goto("http://localhost:3000/path-under-test");
// ...drive the flow, one meaningful state change per step...
await context.close(); // finalizes the .webm
await browser.close();

Trim or compress with ffmpeg if the file is large.

The annotation protocol becomes files: number captures in test order with the assertion in the name — 01-precondition-signed-in.png, 02-it-saves-on-blur-passed.png — and keep an assertions.md in the artifacts folder listing each test_start / assertion with its result (passed / failed / untested + reason).

Non-UI changes still need evidence

API / performance: a scripted probe with measured numbers — request counts per phase, latency before/after — captured to probe-output.txt.
Rendering / canvas / shader: rendered frames plus pixel assertions (diff values), reviewed by eye and saved as PNGs.
Agent behavior: the relevant transcript excerpt showing the tool call and response.
Bug fixes: reproduce and capture the failure before writing the fix — that capture is the "before" half of a before/after pair.

Capture hygiene

Confirm the server you're probing is running your code (right port, right process), especially when multiple agents share a machine: lsof -i :<port> — or where lsof isn't installed, ss -ltnp "sport = :<port>" to find the listener's PID, then ps -p <pid> -o args= to confirm it's yours.
Evidence complements the repo's checks (typecheck/build/tests); it never replaces them.
Hand before/after media pairs to a before/after tool for the PR embed (e.g. before-and-after before.png after.png --markdown).

View source ↗

File metadata

name: evidence-driven-testing
description: >
  Records visual proof while testing UI behavior — the agent tests the app
  hands-on via computer use while a screen recording with structured
  test/assertion annotations captures the session — then posts the video and a
  results summary to the PR and tracker issue. Use whenever a change needs
  verifiable evidence that it works, instead of prose claims — including
  headless environments (scripted screenshots and probes) and non-UI changes
  (measured numbers, output pairs).
compatibility: Screen-recording path requires a GUI environment the agent can drive — built-in computer use, or the cua-driver CLI (trycua/cua) when the harness has no computer-use tools — plus an authenticated browser session for the app under test; the headless path requires only a running app and a scriptable browser (e.g. Playwright via npx). Posting evidence requires gh (GitHub CLI) or equivalent.
metadata:
  version: "1.0"

View original text

---
name: evidence-driven-testing
description: >
Records visual proof while testing UI behavior — the agent tests the app
hands-on via computer use while a screen recording with structured
test/assertion annotations captures the session — then posts the video and a
results summary to the PR and tracker issue. Use whenever a change needs
verifiable evidence that it works, instead of prose claims — including
headless environments (scripted screenshots and probes) and non-UI changes
(measured numbers, output pairs).
compatibility: Screen-recording path requires a GUI environment the agent can drive — built-in computer use, or the cua-driver CLI (trycua/cua) when the harness has no computer-use tools — plus an authenticated browser session for the app under test; the headless path requires only a running app and a scriptable browser (e.g. Playwright via npx). Posting evidence requires gh (GitHub CLI) or equivalent.
metadata:
version: "1.0"
---

# Evidence-Driven Testing

Record annotated proof of behavior, then attach it to the PR and tracker issue.

The recording is the capture of you testing the app via computer use: start the
recorder, then drive the app yourself — click, type, navigate — through each
test target. Every action in the video is the test being performed live; the
recording has no value as evidence unless it shows that interactive session.
If the harness has no computer-use tools but a GUI exists, drive the app with
`cua-driver` instead (see below) — it is still your live session.

## Inputs

- **Test targets** (required): The behaviors/flows to verify, phrased as testable statements.
- **PR / issue** (optional): Where to post the evidence. If omitted, deliver to the requester only.

## Instructions

### 1. Prepare the screen

- Maximize the browser/app window; close popups, notifications, and extra panels.
- Navigate to the starting state (logged in, correct page) BEFORE recording, unless setup itself is under test.

### 2. Start recording

- Begin the screen recording before the first meaningful action.
- Add a `setup` annotation describing the starting context, e.g. "Logged in, navigating to connectors page".

### 3. Test via computer use, annotating as you go

- Perform every interaction through computer use on the live app — the
recording captures your session, so the testing and the evidence are the
same act. Work at a watchable pace: let the UI settle after each action so
state changes are visible on video.
- At each named test's start, add a `test_start` annotation in Jest style: `It should execute the tool directly when permission is 'always'`.
- After each check, add an `assertion` annotation with result `passed`, `failed`, or `untested`.
- Rules for assertions:
- One assertion per meaningful state change — consolidate, don't annotate per UI label.
- Use "Precondition: ..." assertions to establish starting state.
- Keep under ~80 characters, high-signal.
- If a test cannot run (missing prerequisite, expired auth window), mark it `untested` with the reason — never skip silently.

### 4. Stop and review

- Stop recording after the final assertion.
- Confirm the recording captured the key moments before sharing.

### 5. Post the evidence

- Write a short report: what was tested, environment + exact commit, pass/fail per test, caveats.
- Post the video + summary as a PR comment (embed in the PR description if it's your PR).
- Attach the same video to the tracker issue (Linear/Jira) with a one-line result.
- Send the report + recording to the requester.

## Guardrails

- The video must show the actual test session being driven live. Never present
scripted playback, stitched clips, or synthetic footage as a recording; if
the harness lacks computer-use tools but a GUI exists, drive via
`cua-driver`; with no GUI at all, use the headless path instead.
- Never record a half-covered or tiled window — maximize first.
- When verifying a fix, show or reference the old failure alongside the new success.
- Always state the exact commit/branch/deployment tested against.

## No computer-use tools? Drive with cua-driver (GUI available)

When a display exists but the agent has no built-in computer-use capability,
use [cua-driver](https://github.com/trycua/cua) (macOS / Windows / Linux) as
the actuator. It is still you testing the app live — the recording rule holds
unchanged; only the input mechanism differs.

- Verify the setup with `cua-driver doctor` before recording. If a
`cua-driver` skill is installed, read it and follow its protocol — the
snapshot-before-action invariant is mandatory.
- Loop per interaction: `launch_app` → `get_window_state` (accessibility tree
+ screenshot) → act via `element_token` (`click`, `type_text`, `press_key`)
→ `verify_state` for the expected postcondition. Each `verify_state` check
maps 1:1 onto an `assertion` annotation.
- `cua-driver recording start <output-dir>` / `cua-driver recording stop` can
double as the recorder (the output directory is required, and the daemon
must be running: `cua-driver serve`). Video capture is on by default and is
finalized to `<output-dir>/recording.mp4` on stop — but on Windows/Linux it
shells out to ffmpeg, so a missing ffmpeg or display yields only the
per-turn trajectory folders (before/after screenshots, `action.json`,
`click.png`), no video. After stopping, verify `recording.mp4` exists
before citing it; if it is absent, fix the recorder or present the
per-turn before/after screenshots as numbered captures per the headless
protocol.
- If no annotation overlay is available on this path, keep the protocol as
files: an `assertions.md` listing each `test_start` / `assertion` with its
result, exactly as in the headless path.

## Headless path (no GUI available)

When the agent has no desktop to record, keep the same assertion discipline;
swap the recorder for scripted capture:

- Save everything to `.artifacts/<task-name>/` (gitignore it — evidence gets
uploaded, never committed). Keep the capture script beside the captures so
the run is repeatable.
- **Screenshots**: the `before-and-after` CLI (`@vercel/before-and-after`)
captures URLs or elements and its pairs feed PR embeds directly. In
containers/VMs where Chrome fails with "No usable sandbox", set
`AGENT_BROWSER_ARGS="--no-sandbox"`.
- **Video / multi-step flows**: a one-off Playwright script, run without
adding playwright to the project's dependencies:

```bash
npx --yes --package=playwright node record.mjs
```

(Plain `npx playwright node record.mjs` fails — `node` is not a Playwright
CLI command; `--package=playwright` is what puts the module on the path.)
Minimal `record.mjs`:

```js
import { chromium } from "playwright";
const browser = await chromium.launch();
const context = await browser.newContext({
recordVideo: { dir: ".artifacts/<task-name>/" },
});
const page = await context.newPage();
await page.goto("http://localhost:3000/path-under-test");
// ...drive the flow, one meaningful state change per step...
await context.close(); // finalizes the .webm
await browser.close();
```

Trim or compress with ffmpeg if the file is large.
- **The annotation protocol becomes files**: number captures in test order
with the assertion in the name — `01-precondition-signed-in.png`,
`02-it-saves-on-blur-passed.png` — and keep an `assertions.md` in the
artifacts folder listing each `test_start` / `assertion` with its result
(`passed` / `failed` / `untested` + reason).

## Non-UI changes still need evidence

- **API / performance**: a scripted probe with measured numbers — request
counts per phase, latency before/after — captured to `probe-output.txt`.
- **Rendering / canvas / shader**: rendered frames plus pixel assertions
(diff values), reviewed by eye and saved as PNGs.
- **Agent behavior**: the relevant transcript excerpt showing the tool call
and response.
- **Bug fixes**: reproduce and capture the failure **before** writing the
fix — that capture is the "before" half of a before/after pair.

## Capture hygiene

- Confirm the server you're probing is running *your* code (right port,
right process), especially when multiple agents share a machine:
`lsof -i :<port>` — or where `lsof` isn't installed,
`ss -ltnp "sport = :<port>"` to find the listener's PID, then
`ps -p <pid> -o args=` to confirm it's yours.
- Evidence complements the repo's checks (typecheck/build/tests); it never
replaces them.
- Hand before/after media pairs to a before/after tool for the PR embed
(e.g. `before-and-after before.png after.png --markdown`).

Review the source

Source needs review

The tracked source changed or could not be synchronized. Review the current source before installing.

Review before install: Avoid automatic install

License: Unknown

License is unclear
Dependency or permission surface needs review
Permission surface may require sandboxing
Potential broker, wallet, exchange, or real-money execution surface; sandbox and explicit approval are required
Repository license is unknown; the skill itself does not specify a license, which may create legal ambiguity for reuse.
This skill may touch real-money trading, broker, wallet, or exchange operations; use only in a sandbox with explicit approval.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 332 stars, 47 forks; issue activity unavailable in current metadata
License clarity: Unknown
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Source & usage notes

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: michaelshimeles/skills
License: Unknown
Version: 1.0.0
Last GitHub push: Sep 1, 2026
Registry updated: Sep 17, 2026
Instruction path: evidence-driven-testing/SKILL.md @ 10f638c24773

Version reported in registry metadata; check source releases before relying on it.

Quality

67/100

Promising

Trust

58/100

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "michaelshimeles-evidence-driven-testing",
      "task": "Use evidence-driven-testing in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/michaelshimeles-evidence-driven-testing",
    "api": "https://www.openagentskill.com/api/agent/skills/michaelshimeles-evidence-driven-testing",
    "audit": "https://www.openagentskill.com/skills/michaelshimeles-evidence-driven-testing/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=michaelshimeles-evidence-driven-testing&task=Use%20evidence-driven-testing%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20evidence-driven-testing%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20evidence-driven-testing%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/michaelshimeles-evidence-driven-testing/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/michaelshimeles-evidence-driven-testing"
  }
}

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Creator: michaelshimeles
Source: michaelshimeles/skills
Indexed by: OpenAgentSkill community index

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