mini-antemortem-cli 0.1.0

Analytical preflight for omegaprompt calibration: deterministic classifier over seven calibration trap patterns (self-agreement bias, small-sample KC-4 power, variant homogeneity, rubric concentration, judge budget, empty reference, missing held-out slice). Emits AnalyticalFinding records the omegaprompt pipeline consumes via derive_adaptation_plan.

mini-antemortem-cli

Analytical preflight for omegaprompt calibration. Reads the run configuration and classifies seven calibration-specific trap patterns against deterministic rules. No API calls, no network; reasoning is deterministic given the inputs. Emits AnalyticalFinding records that feed omegaprompt's derive_adaptation_plan.

pip install mini-antemortem-cli

Why this is separate from omegaprompt

omegaprompt ships a plugin interface (omegaprompt.preflight.contracts + omegaprompt.preflight.adaptation) but no classifier code. Standalone users do not need analytical preflight — the main pipeline runs with declared defaults. Users who want analytical risk assessment over their configuration install this package alongside:

pip install omegaprompt mini-antemortem-cli

Trap patterns

Seven deterministic classifications run against the run config:

Trap id	Hypothesis
self_agreement_bias	Target and judge share a vendor; judge's biases overlap with target.
small_sample_kc4_power	Dataset too small for Pearson correlation to carry statistical power.
variants_homogeneous	System-prompt variants are too similar for sensitivity to have signal.
rubric_weight_concentration	A single rubric dimension carries most of the weight.
judge_budget_too_small	Judge output budget is SMALL but rubric has many dimensions + gates.
empty_reference_with_strict_rubric	No dataset item has a reference; rubric implies ground-truth comparison.
no_held_out_slice	No --test slice; walk-forward cannot run.

Each pattern returns one of REAL / GHOST / NEW / UNRESOLVED with a severity (blocker / high / medium / low) and a remediation hint.

Usage

from omegaprompt.domain.dataset import Dataset, DatasetItem
from omegaprompt.domain.judge import Dimension, HardGate, JudgeRubric
from omegaprompt.domain.params import PromptVariants
from omegaprompt.preflight import PreflightReport, derive_adaptation_plan
from mini_antemortem_cli import analytical_preflight

rubric = JudgeRubric(
    dimensions=[
        Dimension(name="accuracy", description="correct", weight=0.85),
        Dimension(name="clarity",  description="readable", weight=0.15),
    ],
    hard_gates=[HardGate(name="no_refusal", description="x", evaluator="judge")],
)
variants = PromptVariants(system_prompts=["You are an assistant."], few_shot_examples=[])
train = Dataset(items=[DatasetItem(id=f"t{i}", input=f"task {i}") for i in range(5)])
test = Dataset(items=[DatasetItem(id=f"v{i}", input=f"val {i}") for i in range(3)])

findings = analytical_preflight(
    target_provider="openai",
    target_model="gpt-4o-mini",
    judge_provider="openai",
    judge_model="gpt-4o-mini",
    train_dataset=train,
    test_dataset=test,
    rubric=rubric,
    variants=variants,
    judge_output_budget="small",
)

report = PreflightReport(analytical_findings=findings)
plan = derive_adaptation_plan(report=report)
# plan.skip_axes, plan.max_gap_override, etc.

Design principles

• Deterministic. Same config in, same findings out. No LLM calls; no sampling noise.
• Source-level citations. Each finding carries a note describing the rule that fired and a remediation hint. No hand-wave.
• Severity discipline. high severity findings drive AdaptationPlan overrides that only strengthen the discipline (per apply_adaptation_plan invariants).
• Extensible. Add your own TrapPattern and classifier function; compose with the built-in seven.

Validation

Every trap pattern has positive + negative test cases. No API calls, fully offline. Run with pytest -q.

Relation to the family

• Antemortem / antemortem-cli — pre-implementation reconnaissance discipline for code changes. The naming "mini-antemortem-cli" echoes this family; the enumerate-then-classify pattern comes from there.
• omegaprompt — prompt calibration engine. This package feeds its preflight plugin interface.
• mini-omega-lock — empirical sibling. Runs live probes to measure judge consistency and endpoint reliability.

License

MIT. See LICENSE.