---
name: AI Feature Testing
slug: ai-feature-testing
category: Quality
description: AI Feature Testing helps verify AI-enabled product features for quality, safety, fairness, fallback behavior, and user impact. Use it to review plans or results and produce bounded, evidence-based recommendations.
github: "https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/ai-feature-testing"
language: Python
stars: 193
forks: 27
install: "npx degit https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/ai-feature-testing ~/.claude/skills/ai-feature-testing"
installs_to: ~/.claude/skills/ai-feature-testing
source_path: skills/en/testing-types/ai-feature-testing/SKILL.md
collection_size: 25
category_size: 1662
collection_url: "https://dirskills.com/collections/naodeng/awesome-qa-skills"
added: 2026-09-05T05:31:35.479Z
last_synced: 2026-09-05T05:31:35.479Z
canonical_url: "https://dirskills.com/skills/ai-feature-testing"
---

# AI Feature Testing

AI Feature Testing helps verify AI-enabled product features for quality, safety, fairness, fallback behavior, and user impact. Use it to review plans or results and produce bounded, evidence-based recommendations.

**Install:**

```bash
npx degit https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/ai-feature-testing ~/.claude/skills/ai-feature-testing
```

## README

# AI Feature Testing

## When to Use

- Use this skill when you need to verify product AI features for quality, safety, fairness, fallback behavior, and user experience.
- Use it to review an existing plan, result, or evidence set and produce actionable improvements.
- Use it when context is incomplete but a bounded first pass is still valuable.

## Output Format Options

- Default to Markdown for review, execution, and incremental refinement.
- When the user requests tables, CSV, JSON, or ticket fields, preserve risk, evidence, priority, and boundary information.
- For machine-consumed output, confirm the schema, enums, and required fields first.

## How to Use

1. Read and follow `prompts/ai-feature-testing.md`, including its input contract, execution rules, minimum coverage, and output order.
2. Add only context that changes the decision: scope, environment, version, constraints, evidence, and success criteria.
3. Audit the input, then separate confirmed facts, working assumptions, and open questions.
4. Rank by risk and evidence strength, and produce an artifact that can be executed or reviewed directly.
5. If information is missing, deliver a bounded first pass and state which conclusions remain unsupported.

## Reference Files

- Always read `prompts/ai-feature-testing.md`; it is the complete execution specification for this skill.
- For evaluation or regression, read `evals/eval.yaml` and the relevant cases under `evals/cases/`.
- Load `references/`, `examples/`, `scripts/`, or `output-formats.md` only when those directories exist and the task needs them.

## Core Constraints

- do not test only ideal inputs
- distinguish model limitations from integration defects
- require refusal or human fallback for high-risk outputs
- Never invent system behavior, fields, data, metrics, or root causes absent from the evidence.
- Link important conclusions to evidence; mark unsupported conclusions as hypotheses with a verification method.
- Explain priority using business impact, likelihood, or detectability.

## Delivery Checklist

- [ ] Covered: task quality, factuality, consistency, safety, fairness, privacy, latency and cost, fallback and human handoff.
- [ ] Separated facts, assumptions, gaps, and recommendations.
- [ ] Gave high-risk items a priority, evidence basis, owner or next action.
- [ ] Defined verifiable decision criteria instead of generic advice.
- [ ] Performed no unauthorized production writes or destructive actions.

## Common Pitfalls

- Listing checks without preconditions, expected outcomes, or evidence.
- Marking everything high priority and avoiding tradeoffs.
- Substituting tool names or generic theory for domain reasoning.
- Refusing incomplete input, or pretending incomplete evidence supports certainty.

## Best Practices

- Start with paths most likely to cause business loss, safety issues, or release blockage.
- Reduce uncertainty through the smallest verifiable experiment and record reproduction conditions.
- Make the artifact executable and independently reviewable by another engineer.