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Validation & Contract Layer

Summary

Introduce a structured validation pipeline with explicit schemas/contracts for inputs and outputs (YAML, metadata, BOM, render artifacts). Standardize diagnostics and schema versioning to catch issues early and provide consistent errors to CLI users and downstream tools.

Use Cases for Filare

  • Early detection of schema drift in YAML inputs/components/metadata.
  • Consistent error surfaces for CI/harness consumers (machine-readable diagnostics).
  • Regression checks on BOM invariants (counts, quantities, required fields).
  • Preparing for API/server use where contracts must be explicit.

Technical Evaluation

  • Features: Pre-parse validators (schema version checks, required keys), semantic validators (references, quantities), post-render validators (artifact presence, page counts), and structured error objects. Optional JSON/YAML diagnostics output.
  • Strengths: Reduces silent failures; improves user/CI feedback; clearer upgrade path for schema changes.
  • Weaknesses: Additional development effort; needs maintenance of schemas/contracts alongside code.
  • Limitations: Must preserve backward compatibility; validation must be fast to avoid slowing CLI runs.
  • Compatibility with Filare: Can leverage Pydantic models already present; add a validation pipeline around parser/flows.
  • Required integrations: Validation runner with ordered stages; error taxonomy; optional --validate-only CLI and JSON output mode.

Complexity Score (1–5)

3 — Moderate: new validation stages and error taxonomy; limited algorithmic risk.

Maintenance Risk

  • Filare-side: Medium during rollout; long-term reduction in support churn.
  • External: None; schemas are internal; care needed for versioning/deprecation.
  • Ongoing cost: Keeping validators aligned with schema evolution.

Industry / Business Usage

  • API/services and build systems rely on structured validation and diagnostics (e.g., JSON schema validation, linting passes) to block bad inputs early.

Who Uses It & Why It Works for Them

  • OpenAPI/JSON Schema tooling: Provides clear errors and versioned contracts.
  • CI linters (Yamllint, ESLint): Standardize diagnostics for developers and automation.

Feasibility

  • Feasible now; can be layered atop existing Pydantic models and parsers.

Required Work

  • REWORK tasks: Define validation stages (syntax, semantic, artifact) and error taxonomy; integrate into CLI/flows with optional validate-only mode; add JSON diagnostics output.
  • FEATURE tasks: Versioned schemas/contracts for inputs (components, metadata) and outputs (BOM structure).
  • DOCUMENTATION tasks: Document validation stages, exit codes, and how to consume machine-readable diagnostics.
  • TOOLS tasks: Add CLI command filare validate and optional pre-commit hook support.
  • COVERAGE tasks: Tests for validator coverage and diagnostic shapes.

Recommendation

ADOPT_LATER — Valuable for stability and API-readiness; schedule after config/pipeline cleanup for clearer insertion points.

References

  • JSON Schema/OpenAPI validation patterns; lint-style diagnostic formats.