ovoscope 0.9.2a3

End-to-end test framework for OpenVoiceOS skills

OvoScope

End-to-end testing for OVOS skills. OvoScope runs a full OVOS Core pipeline in-process using a FakeBus — no server, no audio stack, no network. Load real skill plugins, emit a test utterance, and assert on every bus message that comes back: type, data, routing context, session state, and message ordering.

Like a microscope for your OVOS skills.

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Features

Full pipeline	Runs real intent pipeline plugins (Adapt, Padatious, Fallback, Converse, Common Query)
Isolated	Config isolation strips user preferences; deterministic DEFAULT_TEST_PIPELINE excludes AI/persona/OCP stages
Ordered assertions	Assert message type, data keys, routing context, and session state in sequence
Recording mode	Capture a live message sequence and save it as a JSON fixture — no manual construction needed
Multi-turn	Pass a list of utterances to test full conversational flows
pytest fixture	minicroft class-scoped fixture auto-discovered via the pytest11 entry point
Inject skills	extra_skills={id: SkillClass} to load inline test skills without a PyPI entry point
Inject messages	MiniCroft.inject_message() to trigger non-utterance handlers (GUI events, timers, API calls)
Typed models	Optional ovoscope[pydantic] bridge to ovos-pydantic-models for schema-validated messages

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Installation

pip install ovoscope

With optional typed message model support:

pip install ovoscope[pydantic]

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Quick Start

import unittest
from ovos_bus_client.message import Message
from ovos_bus_client.session import Session
from ovoscope import End2EndTest
SKILL_ID = "ovos-skill-hello-world.openvoiceos"
session = Session("test-session")
utterance = Message(
    "recognizer_loop:utterance",
    {"utterances": ["hello world"], "lang": "en-US"},
    {"session": session.serialize(), "source": "A", "destination": "B"},
)
class TestHelloWorld(unittest.TestCase):
    def test_intent_match(self):
        End2EndTest(
            skill_ids=[SKILL_ID],
            source_message=utterance,
            expected_messages=[
                utterance,
                Message(f"{SKILL_ID}.activate", context={"skill_id": SKILL_ID}),
                Message(f"{SKILL_ID}:HelloWorldIntent",
                        data={"utterance": "hello world"}, context={"skill_id": SKILL_ID}),
                Message("mycroft.skill.handler.start", context={"skill_id": SKILL_ID}),
                Message("speak", data={"lang": "en-US"}, context={"skill_id": SKILL_ID}),
                Message("mycroft.skill.handler.complete", context={"skill_id": SKILL_ID}),
                Message("ovos.utterance.handled", context={"skill_id": SKILL_ID}),
            ],
        ).execute(timeout=10)

Only keys you specify in expected.data and expected.context are checked — extra keys in the received message are ignored.

Recording Mode

Don't know the exact message sequence yet? Record it from a live run:

from ovoscope import End2EndTest
test = End2EndTest.from_message(
    message=utterance,
    skill_ids=[SKILL_ID],
    timeout=20,
)
test.save("tests/fixtures/hello_world.json")  # anonymises location data by default

Replay in CI:

End2EndTest.from_path("tests/fixtures/hello_world.json").execute(timeout=10)

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pytest Fixture

The minicroft class-scoped fixture is auto-registered when ovoscope is installed. No setUp/tearDown boilerplate needed:

class TestMySkill:
    skill_ids = ["my-skill.author"]
    def test_something(self, minicroft):
        End2EndTest(
            minicroft=minicroft,
            skill_ids=self.skill_ids,
            source_message=utterance,
            expected_messages=[...],
        ).execute(timeout=10)

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Pipeline Control

OvoScope exposes composable pipeline stage lists so tests are deterministic regardless of which AI plugins are installed on the host:

from ovoscope import ADAPT_PIPELINE, PADATIOUS_PIPELINE, FALLBACK_PIPELINE, PERSONA_PIPELINE
# Adapt only — fastest
mc = get_minicroft([SKILL_ID], default_pipeline=ADAPT_PIPELINE)
# Full intent chain
mc = get_minicroft([SKILL_ID],
                   default_pipeline=ADAPT_PIPELINE + PADATIOUS_PIPELINE + FALLBACK_PIPELINE)
# Opt in to persona for AI testing
mc = get_minicroft([SKILL_ID], default_pipeline=DEFAULT_TEST_PIPELINE + PERSONA_PIPELINE)

DEFAULT_TEST_PIPELINE (the default when isolate_config=True) includes all standard built-in stages and deliberately excludes persona, Ollama, OCP, and m2v plugins.

Documentation

Document
docs/usage-guide.md	Start here — 8 test patterns with full worked examples
docs/ci-integration.md	Wiring ovoscope into GitHub Actions
docs/minicroft.md	MiniCroft and get_minicroft() reference
docs/capture-session.md	CaptureSession internals
docs/end2end-test.md	End2EndTest full parameter reference
docs/pydantic-integration.md	Typed message models with ovos-pydantic-models
FAQ.md	Common questions and gotchas

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License

Apache 2.0

Contributing

PRs are welcome! See CONTRIBUTING.md for guidelines.

AI Disclosure

Parts of this project are developed with the assistance of AI tools. In the interest of transparency, two files are maintained as a public record of AI involvement:

• FAQ.md — Frequently asked questions that emerged from real development sessions, including design rationale, gotchas, and usage patterns. Many entries were authored or refined with AI assistance during the process of building and testing this framework.
• MAINTENANCE_REPORT.md — A chronological log of changes made to this repository. Each entry records what was changed, why, which AI model was involved, what actions it took, and what human oversight was applied. This log is updated after every significant AI-assisted session. These files are intentionally published so that contributors and users can understand how the project evolves and where AI assistance has been applied.