fix(schemas): normalize confidence from 0-100 scale before Pydantic validation

[Shrotriya-lalit]

Problem

LLMFinding and MetaAnalyzerFinding both declare confidence: float = Field(ge=0.0, le=1.0). When a local model (e.g. Ollama-hosted Llama or Mistral) returns confidence as an integer on a 0–100 scale ("confidence": 85), Pydantic raises a ValidationError at the le=1.0 boundary and crashes the meta-analyzer for the entire file — all findings for that file are silently dropped.

Root Cause

The constraint is evaluated after type coercion but before any user logic, so there is no opportunity to rescale at the model level.

Fix

Add a mode="before" @field_validator on confidence in both schemas:

@field_validator("confidence", mode="before")
@classmethod
def _normalize_confidence(cls, v: object) -> float:
    v = float(v)          # raises on non-numeric
    if v > 1.0:
        v = v / 100.0     # rescale 0-100 → 0-1
    return max(0.0, min(1.0, v))

This also clamps negative values and values above 100 (e.g. 110) gracefully instead of crashing.

Tests

• Confidence 0.85 (already valid) passes through unchanged
• Confidence 85 (0-100 integer) is rescaled to 0.85
• Confidence 100 → 1.0, 0 → 0.0
• Confidence -5 → 0.0, 110 → 1.0
• Non-numeric input raises ValueError
• Both LLMFinding and MetaAnalyzerFinding validated

Closes #89

Checklist

• make lint passes
• Tests added for new behaviour
• DCO sign-off on commit (git commit -s)