two-brain-audit 0.1.1

Dual-layer audit system combining automated scoring (left brain) with manual qualitative grading (right brain) and reconciliation.

Two-Brain Audit

A dual-layer audit system that combines automated quantitative scoring (left brain) with manual qualitative grading (right brain) and reconciles them automatically.

  LEFT BRAIN (Auto)              RIGHT BRAIN (Manual)
  ─────────────────              ────────────────────
  pytest pass rate    ──┐    ┌── Human grade (A)
  ruff lint score     ──┤    ├── LLM review findings
  semgrep scan        ──┤    ├── User feedback (4.2/5)
  endpoint health     ──┘    └── Team notes
                        │    │
                        ▼    ▼
                    ┌──────────┐
                    │RECONCILER│
                    └────┬─────┘
                         │
              ┌──────────┼──────────┐
              ▼          ▼          ▼
           Aligned    Diverged    Failing
           (green)    (yellow)     (red)

Why Two Brains?

Scenario	Auto catches it	Manual catches it
Test coverage drops silently	Yes	Maybe
"Feels slow" but metrics are fine	No	Yes
Stale manual grade after major refactor	Yes (divergence)	No
Security vuln in dependency	Yes (scanner)	No
UX regression that tests can't express	No	Yes
Reviewer optimism ("looks good to me")	Yes (cross-validation)	No

Quick Start

pip install two-brain-audit

two-brain-audit init                      # create DB + baseline sidecar
two-brain-audit register --preset python  # 8 dimensions for Python projects
two-brain-audit run light                 # first scan (~2s)
two-brain-audit status                    # view scores + divergences

Dimension                  Auto   Grade  Manual  Status
-----------------------------------------------------------------
  test_coverage            0.930      A      —   ok
  lint_score               1.000      S      —   ok
  type_coverage            0.720     B-      —   ok
  security                 0.500      D      —   ok

Overall: B+ (0.788)

Web Dashboard

pip install two-brain-audit[dashboard]
two-brain-audit dashboard                 # http://localhost:8484/audit/

Dark-mode UI with grade ring, score bars, divergence alerts, tier triggers, and a feedback widget. Zero external dependencies.

Full walkthrough with examples → docs/QUICKSTART.md

Features

• 12-grade scale (S through F) with automatic score-to-grade conversion
• 4 audit tiers — light (CI), medium (on-demand), daily (scheduled), weekly (deep scan)
• Divergence detection — auto vs manual disagreement surfaces automatically
• Ratchet rules — prevent silent score regression per dimension
• User feedback — star rating + free text, optionally classified by LLM
• 5 presets — Python, REST API, Database, Infrastructure, ML Pipeline
• 4 integrations — GitHub, semgrep, PyPI, Ollama (pluggable)
• 3 exporters — JSON, CSV, Markdown reports
• Web dashboard — self-contained Flask blueprint, embed anywhere
• CLI — init, run, status, health, export, dashboard
• CI-friendly — two-brain-audit health returns exit code 0/1 + JSON

Python API

from two_brain_audit import AuditEngine, Dimension, Tier

engine = AuditEngine(db_path="audit.db", baseline_path="audit_baseline.json")

engine.register(Dimension(
    name="test_coverage",
    check=lambda: (passed / total, {"passed": passed, "total": total}),
    confidence=0.95,
    tier=Tier.LIGHT,
))

results = engine.run_tier("daily")
health = engine.health_check()        # {"ok": True, "grade": "A", ...}
engine.record_feedback(score=0.8, text="Looking good")

Flask Integration

from two_brain_audit.dashboard import create_blueprint
app.register_blueprint(create_blueprint(engine), url_prefix="/audit")

Presets

Preset	Dimensions	Best for
python	test coverage, lint, types, deps, docs, security, complexity, imports	Python repos
api	endpoint health, latency, errors, auth, schema, rate limits, CORS, TLS	REST APIs
database	schema, indexes, queries, backups, replication, pool, migrations	Databases
infrastructure	uptime, certs, resources, config drift, secrets, DNS, CDN, containers	DevOps
ml_pipeline	model freshness, data drift, latency, accuracy, features, GPU, experiments	ML workflows

Docs

• Quickstart Guide — step-by-step with examples
• Architecture — design decisions and data flow
• examples/biged/ — 12-dimension reference implementation

Origin

Extracted from BigEd CC after production use on a 125-skill AI fleet with 12 audit dimensions, 4 tiers, and automated daily/weekly scheduling.

License

MIT