How do I install the AI Assisted Testing skill?

Run npx skillmds@latest add naodeng/ai-assisted-testing in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is AI Assisted Testing safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with AI Assisted Testing?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is AI Assisted Testing free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published AI Assisted Testing?

naodeng (@naodeng) published this skill. Their other Agent Skills are listed on their SkillMD profile.

AI Assisted Testing by naodeng — Agent Skill

Name: AI Assisted Testing
Rating: 4.2 (5 reviews)
Author: naodeng

File contents

AI-Assisted Testing

Chinese version： See the corresponding Chinese skill.

When to Use

Need help with ai assisted testing in a real project context.
Need an output that can be used directly for execution, review, or follow-up.

Workflow

Read and follow the main prompt listed under Progressive disclosure (coverage, structure, quality bar).
Add only project context that changes the result: scope, environment, constraints, risks, dependencies, expected deliverable.
If input is incomplete, return a usable first draft and explicitly mark assumptions and gaps.
Default to Markdown; switch formats only when the user asks.

Core Constraints

Prioritize by risk / business impact — do not treat everything equally.
Separate confirmed facts from current assumptions.
Do not invent endpoints, fields, environments, or root causes the user did not provide.
Keep output executable: concrete scenarios, clear priority, clear next steps.

Progressive Disclosure

Before producing output, read and follow prompts/ai-assisted-testing.md (minimum coverage, output structure, quality bar).
When Excel/CSV/JSON/Word is requested: read output-formats.md and honor the format.
When a ready-made template fits: use matching files under output-templates/.
For format conversion or helper checks: prefer existing scripts/ over reinventing.
For evaluating/regressing this skill: use evals/ with skill-up.

Pre-delivery Checklist

Followed the main prompt's output structure
Minimum coverage focus: task scope, best AI-assisted opportunities, human verification points, high-risk areas that need manual judgment, draft artifacts to generate, review and approval steps, quality gates, time-saving opportunities, ... (details in main prompt)
Covered the minimum checklist, or explained omissions
High-risk items have explicit priority
Did not invent details the user did not provide
Assumptions and gaps are marked

Common Pitfalls

Do not pretend completeness when scope/context is missing.
Do not treat every item as equally important.
Do not skip assumptions and information gaps.
Do not dump generic theory unrelated to the current toolchain.

--- name: ai-assisted-testing description: Use this skill when you need AI-assisted testing workflows such as test data generation, root-cause analysis, and prioritization; triggers include AI-assisted testing and AI for QA. --- # AI-Assisted Testing **Chinese version：** See the corresponding Chinese skill. ## When to Use - Need help with ai assisted testing in a real project context. - Need an output that can be used directly for execution, review, or follow-up. ## Workflow 1. Read and follow the main prompt listed under Progressive disclosure (coverage, structure, quality bar). 2. Add only project context that changes the result: scope, environment, constraints, risks, dependencies, expected deliverable. 3. If input is incomplete, return a usable first draft and explicitly mark assumptions and gaps. 4. Default to Markdown; switch formats only when the user asks. ## Core Constraints - Prioritize by risk / business impact — do not treat everything equally. - Separate confirmed facts from current assumptions. - Do not invent endpoints, fields, environments, or root causes the user did not provide. - Keep output executable: concrete scenarios, clear priority, clear next steps. ## Progressive Disclosure - Before producing output, read and follow `prompts/ai-assisted-testing.md` (minimum coverage, output structure, quality bar). - When Excel/CSV/JSON/Word is requested: read `output-formats.md` and honor the format. - When a ready-made template fits: use matching files under `output-templates/`. - For format conversion or helper checks: prefer existing `scripts/` over reinventing. - For evaluating/regressing this skill: use `evals/` with skill-up. ## Pre-delivery Checklist - [ ] Followed the main prompt's output structure - [ ] Minimum coverage focus: task scope, best AI-assisted opportunities, human verification points, high-risk areas that need manual judgment, draft artifacts to generate, review and approval steps, quality gates, time-saving opportunities, ... (details in main prompt) - [ ] Covered the minimum checklist, or explained omissions - [ ] High-risk items have explicit priority - [ ] Did not invent details the user did not provide - [ ] Assumptions and gaps are marked ## Common Pitfalls - Do not pretend completeness when scope/context is missing. - Do not treat every item as equally important. - Do not skip assumptions and information gaps. - Do not dump generic theory unrelated to the current toolchain.

Frequently asked questions

AI Assisted Testing