Code Quality Review

The original review described the public 0.13.0 source tree after repository/documentation hardening. DynosAI 0.14.0 adds small, bounded application/product modules (app_server.py, validation_discovery.py, risk.py) without moving workflow authority out of the established Core.

Current scale

• 42 Python modules in src/dynosai_flow.
• Approximately 16,000 source lines.
• 39 test modules and approximately 5,900 test lines in the beta source snapshot.
• SQLite schema v6.

Strengths

Clear authority model

The architecture separates workflow authority (SQLite), code truth (Git), and transient provider reasoning. This reduces reliance on unstructured chat state.

Strong evidence boundaries

Scope, Git diff, validation, human gates, provider routing, and retry evidence are persisted and testable. Success does not automatically rewrite historical failure attribution.

Failure hygiene

The code explicitly distinguishes model failures from provider/tool/orchestration/validation-precondition conditions, which is important for safe model-control learning.

Recovery and migration coverage

State snapshots use temporary restore databases and integrity checks before atomic replacement. RC hardening also covers schema migration, restart/resume, corrupted snapshots/checkpoints, scope lifecycle, and gate polling.

Provider compatibility is explicit

Codex and Cursor transport differences are handled as provider capabilities instead of assuming identical MCP model-visible behavior.

Maintainability hotspots

Four modules are intentionally large because they accumulated stable orchestration behavior during the pre-public development cycle:

• acceptance.py (~1.4k lines)
• engine.py (~1.3k lines)
• mcp.py (~1.1k lines)
• token_usage.py (~1.0k lines)

They are documented and tested, but are the main candidates for a future dedicated refactor release. They were not split during this GitHub-readiness pass because moving validated control-flow code solely for aesthetics would increase regression risk and violate the no-functional-change constraint.

Improvements made for the public source tree

• SPDX MIT identifier and author copyright on every production Python module.
• English module-level documentation on the complete production package.
• Version-history comments replaced with rationale-oriented comments in production code.
• Generated egg-info, wheels, caches, acceptance logs, smoke outputs, and repetitive release artifacts removed from the public root.
• Public documentation separated into user, architecture, quality, model-control, observability, brownfield, reference, validation, and history areas.
• Package metadata identifies the author, MIT license, supported Python versions, and dev dependencies.
• Repository self-check script validates source headers, module docs, package-version consistency, required public files, and absence of generated artifacts.
• GitHub CI and issue templates added.

Deliberately deferred

These should be separate changes with their own tests, not mixed into a documentation/publication cleanup:

• splitting the large orchestration modules;
• converting compact legacy statements into a project-wide formatting rewrite;
• enforcing a new formatter/linter that could produce thousands of unrelated lines of churn;
• changing runtime Spanish provider prompts/templates, because their wording affects provider behavior;
• changing validation defaults;
• activating Predictive Router authority;
• introducing a mandatory independent second-model code reviewer.

Quality recommendation for 0.14.x and beyond

Use small refactor PRs around one module boundary at a time. Establish behavior-characterization tests first, then extract cohesive services with zero public-contract changes. Prefer measurable reductions in complexity/coupling over large cosmetic rewrites.