Robert Helewka
a967f73d09
Add CLAUDE.md defining the Palladium master notebook conventions and Red Panda Approval criteria, plus a review-notebook slash command for LLM-driven notebook review. Expand .gitignore to block client/engagement documents and generated exports, keeping masters client-clean while allowing text/image sources. Normalize slider widget numeric values from floats to integers in notebook JSON.
71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
"""Shared fixtures: the mock workshop scenario every pin is hand-checked
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against (see test_value_math for the arithmetic). Also makes diaglib
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importable without the study venv active (normal setup is
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``pip install -e ".[dev]"`` into the study-local ``.venv/``)."""
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import pathlib
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import sys
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from datetime import datetime
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import pytest
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sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent.parent))
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from diaglib import OperationalBaseline, build_scores, load_config # noqa: E402
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CONFIGS = pathlib.Path(__file__).resolve().parent.parent / "configs"
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SCORED_AT = datetime(2026, 7, 19, 9, 0)
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#: The seed capability profile: data_readiness is the unique weakest
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#: foundation (level 2), so the binding constraint is a single competency.
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SEED_SCORES = {
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"automation_ai_strategy": (2, "AI driven by board pressure; no written thesis"),
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"value_realization": (2, "Business cases pre-investment only"),
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"executive_alignment": (3, "COO owns CX AI; steering meets quarterly"),
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"process_discovery": (3, "Top 10 call reasons mapped with volumes"),
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"data_readiness": (2, "KB stale; interaction data siloed in recordings"),
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"technical_architecture": (3, "CCaaS APIs available; shared integration layer WIP"),
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"use_case_prioritization": (3, "Scored backlog reviewed monthly"),
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"delivery_capability": (3, "Two bots in production via SI partner"),
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"talent_and_skills": (2, "One conversation designer, contractor"),
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"ai_operations": (2, "Containment eyeballed weekly, no drift alerts"),
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"change_adoption": (3, "Agent champions for copilot rollout"),
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"governance_and_risk": (3, "AI policy signed; review board for voice bots"),
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}
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@pytest.fixture(scope="session")
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def config():
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return load_config("contact_center", CONFIGS)
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@pytest.fixture(scope="session")
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def stub_config():
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return load_config("financial_services", CONFIGS)
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@pytest.fixture()
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def baseline():
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return OperationalBaseline(
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annual_contact_volume=1_200_000,
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blended_cost_per_contact=6.50,
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agent_headcount=450,
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annual_attrition_rate=0.30,
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current_containment_rate=0.20,
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average_handle_time_seconds=420,
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field_confidence={
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"annual_contact_volume": "known",
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"blended_cost_per_contact": "estimated",
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"agent_headcount": "known",
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"annual_attrition_rate": "estimated",
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"current_containment_rate": "estimated",
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"average_handle_time_seconds": "known",
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},
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)
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@pytest.fixture()
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def scores(config):
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return build_scores(config, SEED_SCORES, scored_at=SCORED_AT)
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