Metadata-Version: 2.5
Name: mini-omega-lock
Version: 0.8.0
Summary: 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.
Project-URL: Homepage, https://github.com/hibou04-ops/mini-omega-lock
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Project-URL: Parent package, https://github.com/hibou04-ops/omegaprompt
Project-URL: Issues, https://github.com/hibou04-ops/mini-omega-lock/issues
Author-email: Kyunghoon Gwak <hibouaile04@gmail.com>
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License-File: AUTHORS.md
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License-File: NOTICE
Keywords: evals,llm-as-judge,llm-evaluation,llm-judge,mcp,omega-lock,omegaprompt,preflight,prompt-calibration,prompt-engineering
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
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Classifier: Operating System :: OS Independent
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# mini-omega-lock

> **Measure repeated judge variability and missing measurements before interpreting evaluation results. An A/B significance decision needs a separate comparison design.**

```bash
pip install mini-omega-lock
```

[![CI](https://github.com/hibou04-ops/mini-omega-lock/actions/workflows/ci.yml/badge.svg)](https://github.com/hibou04-ops/mini-omega-lock/actions/workflows/ci.yml)
[![PyPI](https://img.shields.io/pypi/v/mini-omega-lock.svg?cacheSeconds=3600)](https://pypi.org/project/mini-omega-lock/)
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[![License](https://img.shields.io/pypi/l/mini-omega-lock.svg?cacheSeconds=3600)](LICENSE)
[![Parent](https://img.shields.io/badge/parent-omegaprompt%E2%89%A51.1.0-blueviolet.svg?cacheSeconds=3600)](https://pypi.org/project/omegaprompt/)

## 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.

```bash
python -m pip install mini-omega-lock==0.8.0
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](https://github.com/hibou04-ops/omega-lock/blob/main/DOCKING.md) · [Full guide](README.md).

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.**

Relative repeated-score variability is not comparable directly to an absolute A/B delta. Use a declared comparison design with paired samples and uncertainty before accepting a candidate improvement.

```bash
# 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)

```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))
print("measurement_status:", warnings.measurement_status)
for w in warnings:                 # retain human-readable diagnostics
    print("[mini-omega-lock]", w)
```

`judge_noise_floor` is clipped relative CV (`1 - consistency`). It describes repeated scores of one fixed case; it does not define the absolute A/B delta needed for significance. The default judge/gate batch uses 5 actual observations; additional configured probes add calls. Cost depends on provider pricing and usage.

## 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`). Assemble them into a `PreflightReport` and call `derive_adaptation_plan`. The plan distinguishes recommendations from runtime consumption; some policy fields, including schema fallback, remain advisory. Read the artifact consumption record before claiming a policy was applied. 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"

| | Trust the A/B delta | mini-omega-lock |
|---|---|---|
| Tells you the judge's self-disagreement | no | **yes** (`judge_noise_floor`) |
| Repeated-score variability and measurement coverage | manually inspect | explicit metrics and structured states |
| Flags silent strict-schema degradation | no | **yes** (`silent_degradation_detected`) |
| Estimates wall time before a long run | no | **yes** |
| Cost | free, but misleading | ~5 cheap API calls (provider-dependent) |

## What it measures

| Surface | Function | What it tells you |
|---|---|---|
| **Judge noise floor** | `judge_noise_floor`, `measure_judge_consistency` | Clipped CV (`1 - consistency`) over repeated scores of one fixed pair. Relative variability only; not an A/B significance test. |
| Hard-gate flip rate | `measure_gate_flip_rate` | How often a pass/fail gate flips on the *same* input — a flipping gate randomises the ship verdict even when the score looks stable. |
| Endpoint schema reliability | `probe_strict_schema` | STRICT_SCHEMA parse-success fraction and silent parse degradation. A suggested `JSON_OBJECT` fallback is advisory; runtime does not automatically apply it. Inspect policy consumption in the artifact. |
| Context budget margin | `compute_context_margin` (chars) / `compute_context_margin_from_texts` (tokenizer-exact) | `1 - (longest_call_tokens / context_window)`. Negative = projected overflow for the supplied components; request framing can add overhead. |
| Performance projection | `project_performance` | Probe latency × calibration scale → wall-time estimate before launching. |

One `empirical_preflight()` call returns `(judge_quality, endpoint, performance, warnings)`. Fail-closed numeric defaults remain compatible. Use `warnings.measurement_status` as the primary measurement-state contract and persist `probe_accounting` too. Human warnings remain diagnostics; substring inference is only a legacy fallback when structured states are absent, and does not verify historical measurement coverage.

## CLI: machine summary, scorecard, threshold gates

```bash
# 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

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

Sibling projects: [omegaprompt](https://github.com/hibou04-ops/omegaprompt) (calibration engine) · [omega-lock](https://github.com/hibou04-ops/omega-lock) (broader audit framework) · [mini-antemortem-cli](https://github.com/hibou04-ops/mini-antemortem-cli) (analytical, no-API preflight) · [antemortem-cli](https://github.com/hibou04-ops/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](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:

```bash
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](docs/generated/claims.md).

```bash
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`](https://pypi.org/project/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) without claiming A/B significance from a repeated-item variability statistic. See [docs/trust_model.md](docs/trust_model.md) and [docs/claim_ledger.md](docs/claim_ledger.md) for the per-claim boundary.

## License

Apache 2.0. See [LICENSE](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.

## 0.8.0: 실제 관측 공유와 측정 상태

- 같은 `(rubric, item, response)`의 consistency와 gate flip은 실제 judge 관측 배치를 공유합니다. 기본 3+5회가 5회로 줄며 각 지표는 요청한 3개·5개 prefix만 사용합니다. 독립 표본 수를 합산하지 않습니다. 호출 순서가 바뀌므로 stateful judge의 수치가 달라질 수 있습니다.
- 기존 4-tuple과 warnings list 동작을 유지합니다. 반환 warnings의 `measurement_status`와 `probe_accounting`을 함께 저장하세요. CLI/MCP JSON도 두 필드를 제공합니다. 요약은 상태를 우선 읽고 구형 일반 list에는 경고 문자열 호환 읽기를 사용합니다.
- 상태는 `measured`, `unmeasured`, `insufficient_samples`, `failed`, `estimated`, `not_applicable`입니다. 1회 채점은 변동 측정에 부족합니다. 실패한 호출 시간도 accounting에 포함하고, 성공 호출 latency projection과 구분합니다. provider 내부 재시도·실패 토큰 비용은 관측할 수 없으면 `null`/unknown으로 남깁니다.
- `judge_noise_floor`는 호환 이름이며 값은 clipped CV입니다. 새 별칭 `judge_relative_variability`도 같은 값입니다. `fitness_noise_floor`는 aggregate fitness의 표준편차입니다. 둘 다 임의 A/B 개선의 유의성을 판정하지 않습니다.
- CLI: 정상 실행 0은 지정된 측정 완료를 뜻합니다. 미측정·실패·표본 부족·추정은 2, **측정한** 값의 사용자 threshold 위반은 3, 잘못된 입력은 1입니다. threshold 없는 성공을 judge 품질 통과로 해석하지 마세요.

[전후 근거·호환성·재현 명령](docs/upgrade-0.8.0.md). 유료 provider 검증은 수행하지 않았습니다.
