ai-prompting-testing-ai-applications

Sub-skill of ai-prompting: Testing AI Applications.

5 stars

Best use case

ai-prompting-testing-ai-applications is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Sub-skill of ai-prompting: Testing AI Applications.

Teams using ai-prompting-testing-ai-applications should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/testing-ai-applications/SKILL.md --create-dirs "https://raw.githubusercontent.com/vamseeachanta/workspace-hub/main/.agents/skills/_archive/ai/prompting/ai-prompting/testing-ai-applications/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/testing-ai-applications/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How ai-prompting-testing-ai-applications Compares

Feature / Agent	ai-prompting-testing-ai-applications	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Sub-skill of ai-prompting: Testing AI Applications.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Testing AI Applications

## Testing AI Applications


```python
import pytest
from unittest.mock import Mock

def test_prompt_produces_valid_output():
    """Test prompt template produces expected format."""
    response = generate_with_template(
        template=SUMMARY_TEMPLATE,
        input_text="Sample text for testing..."
    )
    assert len(response) < len(input_text)
    assert response.strip()

def test_chain_handles_empty_context():
    """Test chain gracefully handles edge cases."""
    result = qa_chain.run(context="", question="What is X?")
    assert "cannot answer" in result.lower() or "no information" in result.lower()

def test_embedding_consistency():
    """Test embeddings are deterministic."""
    text = "Test sentence"
    emb1 = get_embedding(text)
    emb2 = get_embedding(text)
    assert emb1 == emb2
```

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