mini-omega-lock 0.5.0

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 judge consistency, endpoint schema reliability, context-budget margin, latency, and noise floor — emits PreflightReport records that omegaprompt's derive_adaptation_plan consumes.

pip install mini-omega-lock

Trust & verification

Topic	English	한국어
Generated source-of-truth claims	docs/generated/claims.md	docs/generated/claims_kr.md
Trust model	docs/trust_model.md	docs/trust_model_kr.md
Toolkit positioning	docs/toolkit_positioning.md	docs/toolkit_positioning_kr.md
Claim ledger	docs/claim_ledger.md	docs/claim_ledger_kr.md
Examples / deterministic demo	docs/examples.md	docs/examples_kr.md
Release checklist	docs/release_checklist.md	—
Post-release verification	docs/post_release_verification.md	—
Simpler intro	EASY_README.md	EASY_README_KR.md
Full Korean	—	README_KR.md
Cross-toolkit cookbook	AGENT_TRIGGERS.md	—

Sibling projects: omegaprompt (calibration engine) · omega-lock (broader audit framework) · antemortem-cli (pre-implementation recon CLI) · mini-antemortem-cli (analytical preflight) · Antemortem (methodology).

Use it when

• The same response keeps getting different judge scores across runs.
• Your endpoint sometimes rejects STRICT_SCHEMA mode silently.
• You want a wall-time estimate before launching a long calibration.

You don't need it when you're on stock frontier-tier providers with declared defaults — omegaprompt runs fine without probes there.

Trust loop (no network)

These commands run entirely offline (no provider/API keys). They are also the commands that scripts/release_audit.py enforces — keeping local CI and release gate in lockstep.

python -m pip install -e ".[dev,mcp]"
python -m pytest -q
python scripts/generate_readme_claims.py --check
python scripts/check_repo_consistency.py
python examples/demo_replay.py
python scripts/run_golden_cases.py --check
python scripts/verify_fixture_integrity.py
python scripts/release_audit.py --no-network

Deterministic demo (one command, no API keys)

python examples/demo_replay.py

Replays empirical_preflight against a scripted fake judge; the output is byte-for-byte equal to examples/_demo_output.txt (verified by tests/test_demo_replay.py). Use this as the "did I break the warning surface?" smoke test.

How is this different?

Capability	mini-omega-lock (this)	mini-antemortem-cli	omegaprompt default preflight	Ad-hoc provider smoke test
Live empirical judge probe (production)	yes — scripted/mocked in tests	no (analytical)	no (declared defaults)	varies
Judge consistency / gate-flip measurement	measure_judge_consistency, measure_gate_flip_rate	not in scope	not in scope	ad-hoc
Strict-schema reliability measurement	probe_strict_schema; fail-closed at 0.0 when probe not supplied	not in scope	not in scope	typically pass/fail, no rate
Context margin	compute_context_margin (chars heuristic) + compute_context_margin_from_texts (tokenizer-exact)	analytical estimate	partial	ad-hoc
Latency projection	yes — reuses consistency-probe wall time	no	no	ad-hoc
Noise floor	caller-supplied fitness_samples; fail-closed otherwise	no	no	no
Offline testability	default pytest -q is fully offline	deterministic by construction	yes	typically not
Emits omegaprompt.preflight.PreflightReport shape	yes	yes	source of truth	partial
What it does not prove	model quality, provider reliability under load, production adoption, external validation	same	same	same
Analytical trap classification	not in scope — use mini-antemortem-cli	yes	no	no

Boundary in one line: this package's empirical probes measure a narrow preflight surface (judge / endpoint / context / latency / noise floor); they are not benchmarks of model quality or proofs of production readiness. See docs/trust_model.md and docs/toolkit_positioning.md for the full boundary, and docs/claim_ledger.md for the per-claim source-of-truth mapping.

Looking for the analytical (no-API, deterministic) preflight? See sibling tool mini-antemortem-cli — same plugin interface, deterministic rule-based classifier instead of LLM probes.

MCP server. This package also exposes six probes (empirical_preflight, measure_judge_consistency, measure_gate_flip_rate, compute_context_margin, noise_floor_estimate, project_performance) as agent-callable MCP tools — see docs/generated/claims.md for the regenerated tool list. Install with pip install "mini-omega-lock[mcp]" then run python -m mini_omega_lock.mcp (stdio, default for Claude Code). See AGENT_TRIGGERS.md scenario 2.

---

TL;DR

omegaprompt ships a plugin interface for preflight probes (omegaprompt.preflight.contracts + omegaprompt.preflight.adaptation) but no probe implementation. This package fills that gap with five empirical measurements, then hands the result to omegaprompt's adaptation layer:

• Judge consistency — same (response, rubric) scored N times → 1 - CV. Low = noisy judge, need rescore_count > 1.
• Schema reliability — STRICT_SCHEMA probe success rate. < 0.9 triggers JSON_OBJECT fallback automatically.
• Context budget margin — 1 - (longest_call_tokens / context_window). Negative = guaranteed overflow.
• Performance projection — probe latency × calibration scale → wall-time estimate before launching.
• Noise floor — fitness stdev under identical params → adaptive min_kc4 threshold.

One call (empirical_preflight()) returns the three measurement records omegaprompt's derive_adaptation_plan() consumes, plus a warnings list naming every field that fell back to a fail-closed default (e.g. schema_reliability=0.0 when the strict-schema probe was not supplied).

Looking for the analytical (no-API, deterministic) preflight? See sibling tool mini-antemortem-cli — same plugin interface, deterministic rule-based classifier instead of LLM probes.

---

Quick start (3-minute)

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")

# One call → five measurements → adaptation plan
judge_quality, endpoint, performance, warnings = empirical_preflight(
    judge=judge, rubric=rubric, probe_item=probe_item,
    probe_response="4", consistency_repeats=3,
)
for w in warnings:
    print(f"[mini-omega-lock] {w}")

report = PreflightReport(judge_quality=judge_quality, endpoint=endpoint, performance=performance)
plan = derive_adaptation_plan(report)
print(plan.recommendations)

👋 Simpler intro: EASY_README.md (English) · EASY_README_KR.md

---

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 a 4-tuple — three measurement records omegaprompt's adaptation layer consumes plus a warnings list. Any unmeasured field is fail-closed (e.g. schema_reliability=0.0 rather than 1.0) and named in the warnings; CI gates should treat the warnings list as load-bearing, not cosmetic.

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

# Surface fail-closed warnings before trusting the measurements.
for w in warnings:
    print(f"[mini-omega-lock] {w}")

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 success. Unmeasured fields fail closed (schema_reliability=0.0, noise_floor=0.0, scale_monotonic=False) and emit explicit warnings — agents can tell "measured zero" from "we never ran the probe". The context-margin probe runs as a length-based projection by default (compute_context_margin, chars_per_token=3.8); pass real texts and a token_counter to upgrade to a tokenizer-exact measurement (compute_context_margin_from_texts).
• 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.