CC 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.
AI-powered autonomous test generation and self-healing test maintenance.
As a process C 53/100 · Has gaps — weak spots: result and completion, consistency, running it twice
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 1
✓ No critical or high findings
Medium and low: 1
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low Risky intent
intent-offensive-securitySKILL.md:26Offensive-security / dual-use content (legitimate for authorised testing; review intended use)penetration testing, performance/load engineering, accessibility conformance audits,
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 6514 tokens (recommended < 5000); move details to references/ - note
edit-residuethe text marks something as outdated (lines 119, 340): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 8 mutating operations with no state check
- 40Consistency. Frontmatter name (Agentic Test Engineer) differs from the folder (agentic-test-engineer)
- 50When it triggers. No condition that starts the skill
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 6514 tokens
- 85Steps. 82 steps, 1 vague phrases
- 100Progress reporting. Reports progress
- low 10 top-level sections: this looks like several domains in one skill
- high The skill tells the model to perform an irreversible action with no human approval
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 417: enough signal without eating the budget
- +4Structure: 23 headings
- +3Step-by-step instructions: 82 items
- +4Has examples (13 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 57.