What makes Agentic Test Engineer notable?

977 downloads. Trust evidence available

How should Agentic Test Engineer be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Agentic Test Engineer

AI-powered autonomous test generation and self-healing test maintenance. Generates unit/integration/E2E tests, detects flaky tests, auto-fixes broken selectors, and maintains test coverage. Built for QA engineers and developers. Keywords: AI test automation, self-healing tests, autonomous QA, test generation, Playwright, Selenium, CI/CD testing, flaky test detection, visual AI testing, test coverage, AI-native QA. Skill: Agentic Test Engineer Owner: gechengling Summary: AI-powered autonomous test generation and self-healing test maintenance. Generates unit/integration/E2E tests, detects flaky tests, auto-fixes broken selectors, and maintains test coverage. Built for QA engineers and developers. Keywords: AI test automation, self-healing tests, autonomous QA, test generation, Playwright, Selenium, CI/CD testing, flaky test dete

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

977

Updated

Oct 11, 2026

Version

1.0.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 977 downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 977 downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.2release · observed Sep 28, 2026
Handshake status: UNKNOWNsecurity

What changed

Newest first. Only changes that point at a source are listed.

Release 1.0.2release
修正minimal human intervention自相矛盾表述；新增Scope and Data-Handling Policy（SQP-1/SQP-2）含SaaS厂商六问清单与测试数据最小化规则；三张表各新增一列；新增Example 6截图外发决策；生态动态更新至2026-09-28
Observed Sep 28, 2026 · source

Install and run

Setup complexity: low.

clawhub skill install s17ewqc4f2s6gpcbm88hy7fgvn85kg1g:agentic-test-engineer

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-gechengling-agentic-test-engineer/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

64,012 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: "Agentic Test Engineer"
description: "AI-powered autonomous test generation and self-healing test maintenance. Generates unit/integration/E2E tests, detects flaky tests, auto-fixes broken selectors, and maintains test coverage. Built for QA engineers and developers. Keywords: AI test automation, self-healing tests, autonomous QA, test generation, Playwright, Selenium, CI/CD testing, flaky test detection, visual AI testing, test coverage, AI-native QA."
version: "1.0.2"
---

# Agentic Test Engineer

## Overview

An AI-powered autonomous testing assistant that revolutionizes QA workflows. It generates comprehensive test suites from user stories and code, self-heals broken selectors using visual AI, detects and diagnoses flaky tests, and continuously maintains test coverage metrics. It is an **advisory** skill: it
produces test code, selectors, diagnoses and configuration for you to review and run
in your own CI. It does not execute tests, does not edit your repository, and does not
push changes. Every healing suggestion carries a human checkpoint before merge.

## Scope and Data-Handling Policy (read first)

**Advisory only.** This skill drafts tests, selectors and diagnoses. It does not run
them, does not write files into your repository, and does not talk to your CI. Code
blocks below are reference material to copy into your own environment.

**In scope**: unit / integration / E2E / API test design, flaky-test diagnosis,
selector maintenance, coverage triage, and the review policy for AI-generated tests.

**Out of scope (do not activate for these)**: production incident response, security
penetration testing, performance/load engineering, accessibility conformance audits,
and general debugging that is not about a test. Those need their own tooling.

### Third-party and SaaS test-vendor checklist (SQP-2)

Self-healing and visual-regression features are frequently delivered as a SaaS
service. That means **screenshots, DOM snapshots and sometimes test data leave your
network**. Confirm the following before enabling any external vendor:

| Question to answer | Why it matters | Deal-breaker |
|---|---|---|
| Are screenshots and DOM snapshots stored outside our network? | Screenshots of a real policy or claim screen are customer data | Regulated data with no on-prem option |
| Where is the data processed and for how long is it retained? | Retention turns a test artefact into a data store | Retention the vendor cannot state in writing |
| Is the healing engine self-hostable? | Self-hosting removes the egress question entirely | No self-host option and no DPA |
| Can we redact before upload? | Redaction is the practical middle ground | No redaction hook and no masking |
| Does the vendor train on our artefacts? | Your UI may contain unreleased business logic | Training on customer data without an opt-out |
| Who can access the artefact store? | Broad vendor-side access is a real exposure | Shared, unlogg

_meta.json

{
  "ownerId": "kn74e704j3ygjcygnpf02rdvd185js13",
  "slug": "agentic-test-engineer",
  "version": "1.0.2",
  "publishedAt": 1790572757039
}

skill-card.md

## Description:

Drafts test cases, test code, selector fixes, flaky-test diagnoses, and coverage guidance for human review without running tests or changing repositories.

This skill is ready for commercial/non-commercial use.

## Publisher:

[gechengling](https://clawhub.ai/user/gechengling)

### License/Terms of Use:

MIT-0

## Use Case:

QA engineers and developers use this advisory skill to design unit, integration, API, and browser tests, diagnose flaky failures, propose selector repairs, and prioritize coverage gaps. They review its suggestions before running tests or merging changes.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Screenshots, DOM snapshots, CI logs, and coverage files may expose sensitive information when shared with external testing services.

Mitigation: Use synthetic test data, redact sensitive content, limit snapshots to what is needed, and keep visual testing self-hosted unless vendor handling is approved.

Risk: A suggested selector repair may hide a product regression or target the wrong element.

Mitigation: Review every repair before merging; stop and escalate ambiguous selectors or behavior changes instead of automatically healing them.

## Reference(s):

- [Agentic Test Engineer on ClawHub](https://clawhub.ai/gechengling/skills/agentic-test-engineer)

## Skill Output:

**Output Type(s):** [Code, Analysis, Configuration instructions, Guidance]

**Output Format:** [Markdown with code blocks and tables]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Advisory output for human review; does not run tests, modify repositories, or access CI.]

## Skill Version(s):

1.0.2 (source: release metadata and frontmatter)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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