mini-omega-lock

Your prompt-eval improvement might be smaller than your judge's own noise. mini-omega-lock measures that noise floor before you trust any A/B result.

pip install mini-omega-lock

Start here · Standalone use · Integration/Docking

Omega Aile — Quiet precision. AI research guided by evidence.

Probe judge consistency, schema behavior, context margin and run projections before a calibration. This is a separately distributed measurement tool.

Requires Python 3.11+. Installation needs internet.

python -m pip install mini-omega-lock==0.7.1
preflight --help
python -c "from mini_omega_lock import compute_context_margin; print(compute_context_margin(system_prompt_chars=0, rubric_chars=0, longest_input_chars=380, longest_reference_chars=0, longest_response_chars=0, context_window_tokens=1000))"

The offline API example prints 0.9: a character-based context projection, not an exact tokenizer measurement or model-quality result. Full preflight CLI probes require the selected provider infrastructure. Unmeasured warnings give exit 2; threshold breaches give 3; usage/runtime errors give 1. Exit 0 means requested measurements ran, not that their values are acceptable.

omegaprompt>=1.1.0 is required and installed automatically; no calibration pipeline is needed to use the probes. The supported current combination is omegaprompt 2.1.2. Pack judge_quality, endpoint, performance and warnings into PreflightReport, then derive_adaptation_plan. The package is not the omega-lock search engine.

Docking contracts and runnable data handoff · Full guide.

MCP: install the distribution with [mcp] and use its existing server executable. FastMCP support is bounded to MCP SDK >=1.0.0,<2.0.0; the tool names and schemas are unchanged.

What is the "noise floor"?

An LLM judge does not give the same response the same score every time. Ask it to grade one fixed (response, rubric) pair five times and you'll often get five slightly different scores. That spread is the judge's noise floor.

It matters because of one rule:

A small delta relative to observed judge variability needs more evidence before acceptance.

If prompt B scores 0.4% better than prompt A, but your judge swings 1.2% when re-grading the identical answer, your delta needs additional evidence. You'd ship B, but you measured a coin flip. mini-omega-lock fires a few cheap probe calls and tells you that floor number before you trust the A/B delta.

# One number, no Python, CI-friendly exit codes:
preflight --provider anthropic --rubric rubric.json \
          --probe-item item.json --probe-response "4" --summary
# -> {"judge_noise_floor": 0.07, "schema_reliability": 0.0, ...}

It also measures three more pre-flight surfaces in the same pass: endpoint schema reliability, context-budget margin, and a wall-time projection for the full run.

Quick start (Python)

from omegaprompt import make_provider
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_noise_floor

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

judge_quality, endpoint, performance, warnings = empirical_preflight(
    judge=judge, rubric=rubric, probe_item=probe,
    probe_response="4", consistency_repeats=5,
)

print("judge noise floor:", judge_noise_floor(judge_quality))
for w in warnings:                 # fail-closed warnings are load-bearing
    print("[mini-omega-lock]", w)

That's ~5 cheap API calls (cost depends on provider pricing and token usage). judge_noise_floor is 1 - consistency: 0.0 = the judge never disagreed with itself; the bigger the number, the larger the A/B delta you need before a "win" is believable.

Works with omegaprompt — and standalone

• Standalone: the noise-floor probe is useful on its own. Run preflight against any LLM-judge / prompt-calibration setup, get the floor + schema-reliability numbers, gate your CI on them. No omegaprompt pipeline required to read the value.
• In the ecosystem: empirical_preflight emits omegaprompt.preflight.PreflightReport records (JudgeQualityMeasurement / EndpointMeasurement / PerformanceMeasurement). Feed them to omegaprompt's derive_adaptation_plan and the calibration engine adapts its thresholds to what your infrastructure can actually deliver. mini-omega-lock is the empirical probe layer; omegaprompt is the engine it feeds.

It depends on omegaprompt (>=1.1.0) to build those records, so pip install mini-omega-lock pulls omegaprompt in.

vs. "just trust the eval delta"

What it measures

One call — empirical_preflight() — runs them in one pass and returns (judge_quality, endpoint, performance, warnings). Any unmeasured field fails closed (e.g. schema_reliability=0.0, not 1.0) and is named in warnings, so an agent can always tell "measured zero" from "we never ran that probe". Treat the warnings list as load-bearing in CI, not cosmetic.

CLI: machine summary, scorecard, threshold gates

# Flat, CI-consumable JSON (headline number + schema_version, byte-stable):
preflight ... --summary

# Single-file scorecard (stdlib only) for a PR artifact:
preflight ... --scorecard html --scorecard-out preflight.html

# Fail the build when the judge is too noisy or the endpoint too unreliable:
preflight ... --fail-over-noise-floor 0.10 --fail-under-schema-reliability 0.90

Exit codes: 0 all measured & in-bound · 2 a field fell back to a fail-closed default (unmeasured) · 3 a measured value breached a --fail-* threshold (takes precedence over 2) · 1 usage/runtime error. A measured-but-bad value alone (noisy judge, gate flip) is still 0 — it was measured.

Read more

Sibling projects: omegaprompt (calibration engine) · omega-lock (broader audit framework) · mini-antemortem-cli (analytical, no-API preflight) · antemortem-cli (pre-implementation recon).

What's new in 0.7.0

• Judge noise-floor metrics, front and centre. New judge_noise_floor() helper + a build_summary() that produces a flat, schema_version-tagged, byte-stable CI dict, and a stdlib-only render_scorecard() (Markdown / self-contained HTML).
• CLI --summary (machine summary), --scorecard md|html (+ --scorecard-out), and --fail-over-noise-floor / --fail-under-schema-reliability / --fail-under-context-margin threshold gates (new exit code 3).
• Version-agnostic publish workflow + dynamic PyPI shields that track releases automatically.
• Frozen surface unchanged: empirical_preflight, the three contract records, console scripts, and the omegaprompt>=1.1.0 pin are all the same — additive only.

See CHANGELOG.md for the full history.

Trust loop (no network)

These run entirely offline (no API keys) and are exactly what scripts/release_audit.py enforces, so local CI and the release gate stay 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

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

MCP server

This package also exposes ten agent-callable MCP tools (empirical_preflight, measure_judge_consistency, measure_gate_flip_rate, measure_scale_monotonicity, probe_strict_schema, compute_context_margin, compute_context_margin_from_texts, noise_floor_estimate, project_performance, derive_adaptation_plan) — regenerated list in docs/generated/claims.md.

pip install "mini-omega-lock[mcp]"
python -m mini_omega_lock.mcp           # stdio (Claude Code default)
python -m mini_omega_lock.mcp --http    # streamable-http

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

What this does not prove

Not a benchmark of model quality, judge quality, or provider reliability under load. Not a production-readiness proof. It measures a narrow pre-flight surface (judge noise / endpoint / context / latency) so you stop trusting eval deltas that are smaller than your judge's own noise. See docs/trust_model.md and docs/claim_ledger.md for the per-claim boundary.

License

Apache 2.0. See LICENSE.

License history. PyPI distributions of 0.1.0 shipped with an MIT LICENSE. The repository was relicensed to Apache 2.0 on 2026-04-22 (commit ff489a9); 0.2.0 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.