mini-omega-lock 0.3.0

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

New to this? Start here: EASY_README.md (English) · EASY_README_KR.md (한국어). Compressed plain-language introductions for readers who find the full doc below intimidating.

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

Apache 2.0. See LICENSE.

License history. PyPI distributions of version 0.1.0 were shipped with an MIT LICENSE file. The repository was relicensed to Apache 2.0 on 2026-04-22 (commit ff489a9); 0.2.0 (2026-04-28) and all later versions ship under Apache 2.0. Anyone who installed 0.1.0 holds an MIT license to that copy — license changes do not apply retroactively.