anth-ci-integration
Configure CI/CD pipelines for Anthropic Claude API integrations. Use when setting up automated testing, prompt regression tests, or CI validation for Claude-powered features. Trigger with phrases like "anthropic ci", "claude ci/cd", "test claude in pipeline", "anthropic github actions".
Best use case
anth-ci-integration is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Configure CI/CD pipelines for Anthropic Claude API integrations. Use when setting up automated testing, prompt regression tests, or CI validation for Claude-powered features. Trigger with phrases like "anthropic ci", "claude ci/cd", "test claude in pipeline", "anthropic github actions".
Teams using anth-ci-integration 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/anth-ci-integration/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How anth-ci-integration Compares
| Feature / Agent | anth-ci-integration | 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?
Configure CI/CD pipelines for Anthropic Claude API integrations. Use when setting up automated testing, prompt regression tests, or CI validation for Claude-powered features. Trigger with phrases like "anthropic ci", "claude ci/cd", "test claude in pipeline", "anthropic github actions".
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.
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SKILL.md Source
# Anthropic CI Integration
## Overview
Set up CI/CD pipelines that validate Claude API integrations with mock-based unit tests (free, fast) and prompt regression tests (live API, gated to main).
## GitHub Actions Workflow
```yaml
# .github/workflows/claude-tests.yml
name: Claude API Tests
on: [push, pull_request]
jobs:
unit-tests:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with: { python-version: '3.12' }
- run: pip install anthropic pytest
- run: pytest tests/unit/ -v # No API key needed
prompt-regression:
runs-on: ubuntu-latest
if: github.ref == 'refs/heads/main'
steps:
- uses: actions/checkout@v4
- uses: actions/setup-python@v5
with: { python-version: '3.12' }
- run: pip install anthropic pytest
- run: pytest tests/prompt_regression/ -v --timeout=60
env:
ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
```
## Mock-Based Unit Tests
```python
# tests/unit/test_tool_routing.py
from unittest.mock import MagicMock, patch
import anthropic
def make_mock_message(text="Hello", stop_reason="end_turn"):
msg = MagicMock()
msg.id = "msg_mock_123"
msg.model = "claude-sonnet-4-20250514"
msg.stop_reason = stop_reason
block = MagicMock()
block.type = "text"
block.text = text
msg.content = [block]
msg.usage = MagicMock(input_tokens=100, output_tokens=50)
return msg
@patch("anthropic.Anthropic")
def test_service_returns_text(MockClient):
MockClient.return_value.messages.create.return_value = make_mock_message("42")
from myapp.service import ask_claude
assert ask_claude("What is 6*7?") == "42"
```
## Prompt Regression Tests
```python
# tests/prompt_regression/test_prompts.py
import anthropic, pytest, os, json
pytestmark = pytest.mark.skipif(not os.getenv("ANTHROPIC_API_KEY"), reason="No API key")
client = anthropic.Anthropic()
def test_json_output_format():
msg = client.messages.create(
model="claude-haiku-4-20250514",
max_tokens=256,
messages=[
{"role": "user", "content": "Extract: 'Alice, 30, NYC'. Return JSON: {name, age, city}"},
{"role": "assistant", "content": "{"}
]
)
data = json.loads("{" + msg.content[0].text)
assert "name" in data and "age" in data
def test_system_prompt_boundary():
msg = client.messages.create(
model="claude-haiku-4-20250514",
max_tokens=128,
system="You only discuss cooking recipes. For other topics say: 'I only help with cooking.'",
messages=[{"role": "user", "content": "Write me Python code"}]
)
assert "cooking" in msg.content[0].text.lower() or "recipe" in msg.content[0].text.lower()
```
## CI Cost Guard
```python
# conftest.py
MAX_CI_COST = 1.00
_tokens = {"input": 0, "output": 0}
def pytest_runtest_call(item):
yield
cost = (_tokens["input"] * 0.80 + _tokens["output"] * 4.0) / 1_000_000 # Haiku rates
if cost > MAX_CI_COST:
pytest.exit(f"CI cost guard: ${cost:.4f} exceeds ${MAX_CI_COST}")
```
## Error Handling
| CI Issue | Cause | Fix |
|----------|-------|-----|
| Flaky prompt tests | Non-deterministic output | Use `temperature: 0`, check patterns not exact strings |
| 429 in CI | Parallel jobs sharing key | Use separate CI key |
| Secret not found | Missing GitHub secret | Add `ANTHROPIC_API_KEY` in repo Settings > Secrets |
## Resources
- [GitHub Actions Secrets](https://docs.github.com/en/actions/security-guides/encrypted-secrets)
- [Anthropic Pricing](https://docs.anthropic.com/en/docs/about-claude/pricing)
## Next Steps
For deployment automation, see `anth-deploy-integration`.Related Skills
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