docguard

AI-driven documentation repair with structured research workflow, template-aware generation, and quality validation loops. Generates or fixes canonical documentation by researching the actual codebase, not using placeholders. Iterates until guard passes.

Run DocGuard guard validation against Canonical-Driven Development standards. Parses output, triages severity, suggests targeted fixes, and optionally chains to docguard-fix for automated remediation. Use as a quality gate before commits or after implementation phases.

Cross-document consistency analysis and quality assessment. Performs read-only analysis across all canonical docs, identifies drift, coverage gaps, and quality issues. Produces a structured report with severity-ranked findings. Modeled after speckit-analyze.

CDD maturity assessment with category-aware improvement roadmap. Runs scoring engine, analyzes category breakdown, identifies highest-impact improvements, and generates a before/after improvement plan with projected score gains.

Keep canonical documentation ALWAYS UP TO DATE. Refreshes code-truth doc sections in place (mechanical, idempotent, preserves human prose) and flags the prose sections you must review when code changes.

Perform cross-artifact consistency analysis across spec.md, plan.md, and tasks.md. Use after task generation to identify gaps, duplications, and inconsistencies before implementation.

Generate custom quality checklists for validating requirements completeness and clarity. Use to create unit tests for English that ensure spec quality before implementation.

Structured clarification workflow for underspecified requirements. Use before planning to resolve ambiguities through coverage-based questioning. Records answers in spec clarifications section.

Execute all tasks from the task breakdown to build the feature. Use after task generation to systematically implement the planned solution following TDD approach where applicable.

Generate technical implementation plans from feature specifications. Use after creating a spec to define architecture, tech stack, and implementation phases. Creates plan.md with detailed technical design.

Create or update feature specifications from natural language descriptions. Use when starting new features or refining requirements. Generates spec.md with user stories, functional requirements, and acceptance criteria following spec-driven development methodology.

Break down implementation plans into actionable task lists. Use after planning to create a structured task breakdown. Generates tasks.md with ordered, dependency-aware tasks.

Convert tasks from tasks.md into GitHub issues. Use after task breakdown to track work items in GitHub project management.