mini-omega-lock 0.1.0

Empirical preflight probes for omegaprompt calibration: judge consistency, endpoint schema reliability, context-budget margin, latency, noise floor. Emits PreflightReport records the omegaprompt pipeline consumes via derive_adaptation_plan.

mini-omega-lock

Empirical preflight probes for omegaprompt calibration. Measures the actual environment (judge consistency, endpoint schema reliability, context-budget margin, latency, noise floor) and emits PreflightReport records that feed omegaprompt's derive_adaptation_plan.

pip install mini-omega-lock

Why this is separate from omegaprompt

omegaprompt ships a plugin interface (omegaprompt.preflight.contracts + omegaprompt.preflight.adaptation) but no probe code. Standalone users do not need preflight probes — they run calibration with declared defaults. Users who want adaptive thresholds tuned to their actual infrastructure install this package alongside:

pip install omegaprompt mini-omega-lock

What it measures

Measurement	Function	What it tells you
Judge consistency	measure_judge_consistency	Same (response, rubric) scored N times; 1 - CV. Low = noisy judge, need rescore_count > 1.
Endpoint schema reliability	probe_strict_schema	STRICT_SCHEMA probe success fraction. < 0.9 triggers JSON_OBJECT fallback.
Context budget margin	compute_context_margin	1 - (longest_call_tokens / context_window). Negative = overflow.
Performance projection	project_performance	Mean probe latency → projected calibration wall time.
Noise floor	noise_floor_estimate	Stdev of fitness under identical parameters. Sets adaptive min_kc4.

The composite entry point is empirical_preflight(), which runs all five in one call and returns the three measurement records omegaprompt's adaptation layer consumes.

Usage

from omegaprompt import make_provider, PreflightReport, derive_adaptation_plan
from omegaprompt.domain.dataset import DatasetItem
from omegaprompt.domain.judge import Dimension, JudgeRubric
from omegaprompt.judges.llm_judge import LLMJudge
from mini_omega_lock import empirical_preflight

judge_provider = make_provider("anthropic")
judge = LLMJudge(provider=judge_provider)
rubric = JudgeRubric(dimensions=[Dimension(name="accuracy", description="x", weight=1.0)])
probe_item = DatasetItem(id="probe", input="2+2", reference="4")

judge_quality, endpoint, performance = empirical_preflight(
    judge=judge,
    rubric=rubric,
    probe_item=probe_item,
    probe_response="4",
    consistency_repeats=3,
    dataset_size_hint=10,
    candidates_expected=20,
)

report = PreflightReport(
    judge_quality=judge_quality,
    endpoint=endpoint,
    performance=performance,
)
plan = derive_adaptation_plan(report=report)
# plan.min_kc4_override, plan.rescore_count, etc.

Design principles

• No fabricated numbers. Every measurement is computed from a real provider response. No heuristic estimation.
• Minimal probe budget. Default 3 consistency repeats + 3 schema probes + 1 context-margin compute = 7-10 API calls per preflight. Worth < $0.01 on frontier tiers.
• Protocol-conformant output. Emits omegaprompt.preflight.contracts.JudgeQualityMeasurement / EndpointMeasurement / PerformanceMeasurement exactly. No shape drift.
• Composable. Can run alongside mini-antemortem-cli (analytical preflight) into the same PreflightReport.

Validation

All adapter tests mock the provider SDK; no network, no API credits, fully offline. Run with pytest -q.

Relation to the family

• omega-lock — parameter-calibration framework. The naming "mini-omega-lock" echoes this family; the sensitivity + walk-forward + KC-4 discipline comes from there.
• omegaprompt — prompt calibration engine. This package feeds its preflight plugin interface.
• mini-antemortem-cli — analytical sibling. Runs deterministic trap classification over config before calibration.

License

MIT. See LICENSE.