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Allure Reporting

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manikosto
allure-reporting

Allure Report integration with pytest — decorators, steps, attachments, severity levels, test categorization, CI/CD integration, and custom report plugins.

Overview

Publishermanikosto
Repositoryclaude-code-python-stack
Skill nameallure-reporting
Stars
54
Forks
10
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by manikosto on GitHub. Read the source before you install it.

Installation

Install the Allure Reporting AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/manikosto/claude-code-python-stack/tree/main/skills/allure-reporting
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/manikosto/claude-code-python-stack.git /tmp/claude-code-python-stack
mkdir -p .claude/skills
cp -r /tmp/claude-code-python-stack/skills/allure-reporting .claude/skills/allure-reporting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Allure Reporting in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Allure Reporting on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Allure Reporting is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Allure Reporting Patterns

Comprehensive Allure Report integration for pytest test suites.

When to Activate

  • Setting up Allure in a pytest project
  • Adding Allure metadata to test classes
  • Configuring Allure in CI/CD pipelines
  • Debugging test report rendering issues
  • Creating custom Allure plugins or extensions

Setup

bash
# Install
pip install allure-pytest

# Run tests with Allure
pytest --alluredir=allure-results -v

# Generate and open report
allure serve allure-results

# Generate static report
allure generate allure-results -o allure-report --clean

pyproject.toml

toml
[tool.pytest.ini_options]
addopts = "--alluredir=allure-results --clean-alluredir"

Allure Decorator Hierarchy

Required on EVERY test class and method:

python
import allure

@allure.epic("E-Commerce Platform")           # Top-level product area
@allure.feature("Shopping Cart")               # Feature being tested
class TestAddToCart(BaseTest):

    @allure.story("Add single item")           # User story
    @allure.severity(allure.severity_level.CRITICAL)  # Priority
    @allure.title("User can add product to cart")     # Human-readable title
    @allure.description("Verify that authenticated user can add a product to their cart")
    @allure.tag("cart", "positive")
    @allure.link("https://jira.example.com/PROJ-123", name="PROJ-123")
    def test_add_product_to_cart(self):
        ...

Severity Levels

LevelWhen to UseExample
BLOCKERApp unusable if failsLogin, payment processing
CRITICALMajor feature brokenUser creation, order placement
NORMALStandard functionalityProfile update, search
MINORMinor inconveniencePagination, sorting
TRIVIALCosmeticUI text, formatting

Allure Steps

Method-level steps (API service classes)

python
class OrdersAPI(BaseAPIClient):

    @allure.step("Create order with {len(items)} items")
    def create_order(self, items: list[dict]) -> httpx.Response:
        return self.client.post("/api/v1/orders", json={"items": items})

    @allure.step("Get order #{order_id} status")
    def get_order_status(self, order_id: int) -> httpx.Response:
        return self.client.get(f"/api/v1/orders/{order_id}/status")

Inline steps (inside test methods)

python
def test_complete_order_flow(self):
    with allure.step("Create a new user"):
        user = self.create_test_user()

    with allure.step("Add items to cart"):
        self.cart_api.add_item(user["id"], product_id=1, quantity=2)

    with allure.step("Place order"):
        response = self.orders_api.create_order(user["id"])
        assert response.status_code == 201
        order_id = response.json()["id"]

    with allure.step("Verify order status is 'pending'"):
        status_resp = self.orders_api.get_order_status(order_id)
        assert status_resp.json()["status"] == "pending"

Attachments

Attach request/response to every API call

python
class HTTPClient:
    def _attach_to_allure(self, response: httpx.Response):
        allure.attach(
            body=str(response.request.url),
            name="Request URL",
            attachment_type=allure.attachment_type.TEXT,
        )
        if response.request.content:
            allure.attach(
                body=response.request.content.decode("utf-8"),
                name="Request Body",
                attachment_type=allure.attachment_type.JSON,
            )
        allure.attach(
            body=response.text,
            name=f"Response [{response.status_code}]",
            attachment_type=allure.attachment_type.JSON,
        )
        allure.attach(
            body=str(dict(response.headers)),
            name="Response Headers",
            attachment_type=allure.attachment_type.JSON,
        )

Attach screenshots (E2E/UI tests)

python
@allure.step("Take screenshot: {name}")
def attach_screenshot(page, name: str):
    screenshot = page.screenshot()
    allure.attach(screenshot, name=name, attachment_type=allure.attachment_type.PNG)

Attach logs on failure

python
@pytest.fixture(autouse=True)
def attach_logs_on_failure(request, caplog):
    yield
    if request.node.rep_call and request.node.rep_call.failed:
        allure.attach(
            body=caplog.text,
            name="Test Logs",
            attachment_type=allure.attachment_type.TEXT,
        )

@pytest.hookimpl(tryfirst=True, hookwrapper=True)
def pytest_runtest_makereport(item, call):
    outcome = yield
    rep = outcome.get_result()
    setattr(item, f"rep_{rep.when}", rep)

Dynamic Allure Metadata

python
class BaseTest:
    @pytest.fixture(autouse=True)
    def _set_allure_metadata(self, request):
        """Auto-set Allure metadata from test class/method."""
        if request.cls:
            allure.dynamic.parent_suite(request.cls.__module__)
            allure.dynamic.suite(request.cls.__name__)
        allure.dynamic.sub_suite(request.node.name)

        # Add environment info
        allure.dynamic.parameter("env", self.settings.ENV_NAME)

Environment Info

python
# conftest.py — generate environment.properties for Allure
import pytest

def pytest_sessionfinish(session, exitstatus):
    """Write environment info for Allure report."""
    allure_dir = session.config.getoption("--alluredir", default=None)
    if allure_dir:
        env_file = Path(allure_dir) / "environment.properties"
        settings = Settings()
        env_file.write_text(
            f"Base.URL={settings.BASE_URL}\n"
            f"Environment={settings.ENV_NAME}\n"
            f"Python={sys.version}\n"
            f"pytest={pytest.__version__}\n"
        )

Categories (defect classification)

json
// allure-results/categories.json
[
  {
    "name": "API Errors",
    "matchedStatuses": ["failed"],
    "messageRegex": ".*assert.*status_code.*"
  },
  {
    "name": "Schema Validation Failures",
    "matchedStatuses": ["failed"],
    "messageRegex": ".*validation error.*|.*ValidationError.*"
  },
  {
    "name": "Infrastructure Issues",
    "matchedStatuses": ["broken"],
    "messageRegex": ".*ConnectionError.*|.*TimeoutError.*"
  },
  {
    "name": "Flaky Tests",
    "matchedStatuses": ["failed"],
    "traceRegex": ".*flaky.*"
  }
]

CI/CD Integration

GitHub Actions

yaml
name: API Tests
on: [push, pull_request]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.12'

      - name: Install dependencies
        run: pip install -r requirements-test.txt

      - name: Run tests
        run: pytest --alluredir=allure-results -v -n auto
        env:
          BASE_URL: ${{ secrets.TEST_BASE_URL }}
          TEST_USER_EMAIL: ${{ secrets.TEST_USER_EMAIL }}
          TEST_USER_PASSWORD: ${{ secrets.TEST_USER_PASSWORD }}

      - name: Upload Allure results
        if: always()
        uses: actions/upload-artifact@v4
        with:
          name: allure-results
          path: allure-results/

      - name: Allure Report
        if: always()
        uses: simple-elf/allure-report-action@master
        with:
          allure_results: allure-results

      - name: Deploy report to GitHub Pages
        if: always()
        uses: peaceiris/actions-gh-pages@v3
        with:
          github_token: ${{ secrets.GITHUB_TOKEN }}
          publish_dir: allure-report

Quick Reference

DecoratorLevelRequired
@allure.epic("...")Test classYes
@allure.feature("...")Test classYes
@allure.story("...")Test methodYes
@allure.severity(...)Test methodYes
@allure.title("...")Test methodRecommended
@allure.step("...")API methodsYes
@allure.tag("...")Test methodOptional
@allure.link("...")Test methodOptional
allure.attach(...)Inside stepsFor API calls
allure.dynamic.*RuntimeFor base classes

Remember: A good Allure report tells a story. Epic -> Feature -> Story -> Steps. Anyone reading the report should understand what was tested, how, and why it failed.

Frequently asked questions

What does the Allure Reporting AI skill do?

Allure Report integration with pytest — decorators, steps, attachments, severity levels, test categorization, CI/CD integration, and custom report plugins.

Why use Allure Reporting on TypingMind?

Because you install it once and use it with any model. Allure Reporting is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Allure Reporting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/manikosto/claude-code-python-stack/tree/main/skills/allure-reporting. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Allure Reporting?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Allure Reporting?

As many as you like. As long as a model supports skills, you can use Allure Reporting with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Allure Reporting AI skill free?

It is published on GitHub by manikosto. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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