feat: Multi-Dimensional Quality Scoring for Structured Outputs

[a827681306]

Summary

Implements a multi-dimensional quality scoring engine for structured submissions (JSON, markdown, code, text), addressing issue #1.

Architecture

scorer.py — Pure Python, zero external dependencies. Two main classes:

• QualityScorer — Entry point. Configurable weights, pass threshold. Supports single and batch scoring.
• Rubric — Defines expectations: required fields, expected format, keywords, JSON schema, etc.

Auto-format detection via regex heuristics for JSON, markdown, code, and plain text.

Scoring Dimensions

Dimension	Weight	What it measures
Completeness	0.30	Required fields/sections present, minimum length
Format Compliance	0.20	Matches expected format, structural quality
Coverage	0.25	Keyword/topic coverage, vocabulary diversity
Clarity	0.15	Sentence length, repetition, readability
Validity	0.10	JSON schema, bracket balance, syntax patterns

Output Format

{
    "weighted_score": 0.8725,      # 0-1 weighted aggregate
    "quality_rating": "good",      # excellent/good/acceptable/poor/failing
    "scores": {                    # per-dimension breakdown
        "completeness": 0.95,
        "format_compliance": 0.90,
        "coverage": 0.80,
        "clarity": 0.85,
        "validity": 1.0
    },
    "feedback": ["..."],           # actionable per-dimension feedback
    "pass_threshold": true,        # meets minimum bar
    "detected_format": "json"
}

Bonus: NLP Feedback Generation

generate_nlp_feedback() produces a natural-language summary identifying strongest/weakest dimensions and priority improvements.

Performance

• Single submission: ~0.05ms
• 100 submissions: ~0.15s (well under the 10s requirement)
• Pure Python, no ML dependencies — fast cold start

Test Coverage

35 test cases in test_scorer.py covering:

• Format detection (6 tests)
• JSON scoring with schema validation (5 tests)
• Markdown scoring with section/structure checks (3 tests)
• Code scoring with bracket balance (2 tests)
• Text scoring with keyword coverage (2 tests)
• Edge cases: empty, short, placeholder, format mismatch (6 tests)
• Output structure validation (5 tests)
• NLP feedback generation (2 tests)
• Performance benchmarks (2 tests)
• Custom weights and validation (2 tests)

Design Decisions

• Zero dependencies beyond Python stdlib — no numpy, no sklearn, no NLTK. Keeps it lightweight and deployable anywhere.
• Rubric-driven — scoring adapts to what the rubric specifies rather than hardcoded expectations.
• Extensible — new dimensions can be added by implementing a scorer function and adding it to _DIMENSION_SCORERS.
• Batch-first — score_batch() designed for bulk evaluation pipelines.

Closes #1