14 docs tagged with "research-pipeline"

Generates Architecture Decision Records documenting research adoption decisions using CODITECT ADR template

Extracts structured data from Phase 1 artifacts and assembles unified JSON for dashboard consumption

Generates c4-architecture.md with full C4 model analysis. C1 System Context (where tech sits relative to CODITECT actors), C2 Container Diagram (how it maps to CODITECT containers), C3 Component Diagram (internal decomposition), C4 Code Diagram (key interfaces, classes, data structures). Each level includes Mermaid diagram and narrative explaining architectural intent, design rationale, and compliance implications.

Creates self-contained React/JSX dashboard files with strict light-mode design system compliance

Generates executive-summary.md for CTO/VP Engineering decision-making. Covers problem statement, solution overview, fit for CODITECT, risks & unknowns, and recommendation (Go/No-Go/Conditional). Decision-support tone — present tradeoffs, not conclusions dressed as analysis.

Builds comprehensive glossary mapping researched technology terms to CODITECT equivalents and ecosystem analogs

Creates actionable follow-up prompts across 6 categories to deepen research and address capability gaps

Generates coditect-impact.md analyzing how researched technology integrates into CODITECT platform. Covers integration architecture, multi-tenancy, compliance surface, observability, multi-agent orchestration fit, advantages, gaps & risks, and integration patterns. Explicit about gaps — not diplomatic.

Creates Mermaid diagrams for system architecture, agentic workflows, data flow, and integration boundaries

Systematically validates research artifacts against quality criteria and produces quality report with pass/fail per artifact

Generates 1-2-3-detailed-quick-start.md from research-context.json with dense quick-start for experienced engineers (assumes TS/Python, Docker, Git, cloud-native background). Every code block is copy-paste runnable.

Generates sdd.md viewing researched technology as a subsystem within CODITECT. Covers context diagram, component breakdown, data & control flows, scaling model, failure modes, observability story, and platform boundary (what framework provides vs. what CODITECT builds).

Generates tdd.md with concrete integration details - APIs & extension points, configuration surfaces, packaging & deployment, data model, security integration, example interfaces (TypeScript/Python types), and performance characteristics.

Crawls provided URLs, GitHub repositories, and local documents to extract technical details across 7 research dimensions (architecture, language support, state management, security, AI/agent capabilities, deployment, compliance), producing structured research-context.json