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# SDD + GitOps Documentation Stack # SDD + GitOps Documentation Framework
A comprehensive documentation strategy for modern software development that aligns different types of documentation with their specific purposes, audiences, and tooling. ## Overview
## The Big Picture The **SDD + GitOps Documentation Framework** is a comprehensive, structured approach to software development documentation that aligns technical work with business outcomes through clear separation of concerns.
This framework ensures that every piece of documentation serves a clear purpose and reaches the right audience. It emphasizes: This framework ensures that every piece of documentation serves a specific purpose, reaches the right audience, and can be measured for effectiveness. It's designed to prevent common pitfalls like feature creep, communication gaps, and operational fragility.
- **Machine-readable truths** as the foundation for automation
- **Separation of concerns** between human-facing docs and machine-consumable contracts
- **GitOps integration** where deployment and configuration are version-controlled
- **Multi-role audience targeting** from stakeholders to DevOps
--- ---
## Documentation Matrix ## The Documentation Matrix
| Document | Purpose ("The Why") | Primary Audience | Format / Tooling | Example (SaaS Context) | | Document | Purpose & Rationale (The "Why") | Audience | Format / Content | Measurement (KPI/SLO) | Example (SaaS Context) |
|----------|---------------------|------------------|------------------|------------------------| |----------|----------------------------------|----------|------------------|----------------------|------------------------|
| **Requirements** | Define business goals & user needs | Stakeholders, PM, Lead Dev | GitHub Issues, Notion | "System must support 5-member teams with real-time sync." | | **Requirements** | The Business North Star. Defines exactly what problem the user has and what success looks like. It prevents "feature creep" by setting hard boundaries on what we will NOT build. | Founder, Team, PM | Format: Shared Wiki (Notion/GitHub Wiki). Content: User stories, business constraints, competitive context, and success metrics. | **KPI**: Business Outcomes. Measured by User Retention, Conversion Rates, and Monthly Recurring Revenue (MRR). | "The system must process high-volume math so clients see reports instantly. Goal: 15% increase in daily active users." |
| **The Spec** | The Contract. Machine-readable truth. | Developers, QA, Machines | OpenAPI, Protobuf, YAML | A `.yaml` file defining `user_id` as a UUID in snake_case. | | **Spec** | The Technical Contract. A machine-readable, strictly typed definition of all data interfaces. It is the "Single Source of Truth" that prevents bugs caused by communication gaps between services. | Developers, QA, Automation | Format: OpenAPI/YAML or Protobuf. Content: API endpoints, snake_case key naming, data validation rules, and error response codes. | **SLA/SLO**: System Performance. Measured by API Uptime (99.9%), Response Latency (<100ms), and Error Rates. | A `contract.yaml` defining exactly how Julia sends Arrow data to Node.js. It forces `user_id` to be a UUID. |
| **Architecture** | High-level structural blueprint | Senior Devs, DevOps | Mermaid.js, IcePanel | Diagram of SvelteKit ↔ NATS ↔ Julia 6-node cluster. | | **Architecture** | The Structural Blueprint. A visual map of how the components (services, DBs, networks) fit together. It shows how the data flows through the 6-node cluster and where bottlenecks live. | Senior Devs, DevOps | Format: Diagrams-as-code (Mermaid.js). Content: System Context diagrams, Database ERDs, Network Security Policies, and Infrastructure maps. | **Efficiency Metrics**: Resource utilization. Measured by CPU Load (<70%), RAM per pod, and internal network throughput. | A diagram showing the data path: Caddy (Proxy) → Node.js (API) → NATS (Queue) → Julia (Math Engine). |
| **Walkthrough** | The Intuition. The "Big Picture" narrative. | New Devs, The Team | Recorded Video, TOUR.md | "Why we use a Claim-Check pattern for large Arrow data." | | **Walkthrough** | The Intuition & Logic. A narrative guide that explains the "steps" and "rationale" behind end-to-end flows. It's about building a mental model so devs understand why the sequence matters. | The Team, New Hires | Format: TOUR.md file or Loom Video. Content: Step-by-step traces of core features, explanation of architectural trade-offs, and "The Big Picture" flow. | **Quality**: Developer Velocity. Measured by "Time-to-First-Commit" for new hires and reduction in conceptual bugs. | "End-to-End Trace": 1. UI sends JSON. 2. API wraps it in Claim-Check. 3. Julia pulls it. Rationale: To avoid NATS memory spikes. |
| **Implementation** | The actual logic & generated code | Developers | SvelteKit, Julia, Node.js | Auto-generated TypeScript types from the OpenAPI spec. | | **Implementation** | The Functional Reality. The actual code that does the work. In SDD, the "boring" parts (types/routes) are auto-generated from the Spec to ensure the code never lies. | Developers, Reviewers | Format: Git Repository. Content: Business logic, internal helper functions, Unit Tests, and a README.md for local environment setup. | **Code Health**: Internal Quality. Measured by Test Coverage (90%+), Linting compliance, and Cyclomatic Complexity. | The SvelteKit frontend components and the specific Julia math-processing functions. |
| **Validation** | Automated "Contract" enforcement | CI/CD Pipelines, QA | GitHub Actions, Prism | A test that fails if the Julia API returns camelCase keys. | | **Validation** | The Enforcement Layer. Automated gates that prove the Implementation matches the Spec. It prevents human error (like changing a key name) from reaching production. | CI/CD Pipeline, QA | Format: GitHub Actions / Tests. Content: Contract tests (Dredd/Prism), Integration tests, and Security scans that run on every pull request. | **Compliance**: Safety Metrics. Measured by Build Success Rate and 0 "Contract Violations" in the production logs. | A CI job that blocks a Pull Request because a developer used camelCase in a database field instead of snake_case. |
| **Runbook** | Deployment, Scaling, & Recovery | DevOps, SRE | K8s Manifests, Flux | `git push` to update the replica count from 3 to 6. | | **Maintenance** | The Health & Evolution. Defines how to upgrade dependencies, manage technical debt, and rotate secrets. It's the guide for "future-proofing" the software over time. | The Team, DevOps | Format: MAINTENANCE.md. Content: Dependency update schedules, Secret rotation steps, DB Migration logs, and Tech Debt "Graveyard" tracking. | **Sustainability**: System Longevity. Measured by "Package Age", "Security Vulnerabilities Found", and "Migration Success Rate". | "Steps to upgrade the Julia version across all 6 nodes without downtime using a Blue-Green deployment strategy." |
| **Runbook** | The Operational Life-Support. The instructions for when the system is alive (or dying). In GitOps, this is the "Desired State" of the infrastructure. | DevOps, SRE, On-call Devs | Format: K8s Manifests (Flux/Argo). Content: Deployment steps, Scaling triggers, Backup/Restore procedures, and "3:00 AM" troubleshooting guides. | **Reliability**: Operational Health. Measured by MTTR (Mean Time to Recovery) and Error-Free Deployments. | A Flux manifest that ensures 6 replicas of the Julia service are always healthy and restarts them if they hit 80% RAM. |
--- ---
## Detailed Explanations ## Detailed Document Descriptions
### 1. Requirements ### 1. Requirements
**Purpose**: Define business goals & user needs. **Purpose**: Establish the Business North Star.
**Why it matters**: Before writing code, we need to understand *why* we're building something. Requirements capture the business context, user pain points, and success criteria. **Why It Matters**: Without clear requirements, teams drift into "feature creep" - building things that don't solve the actual problem. This document anchors the project in business outcomes.
**Primary Audience**: **Key Elements**:
- **Stakeholders**: Business owners who need to approve the direction - **User Stories**: What the user needs to accomplish
- **Product Managers**: Translate requirements into features - **Business Constraints**: Budget, timeline, regulatory requirements
- **Lead Developers**: Understand scope and technical constraints - **Competitive Context**: What competitors do and how you differentiate
- **Success Metrics**: Quantifiable goals that define "done"
**Format / Tooling**:
- **GitHub Issues**: Simple, version-controlled, integrated with code
- **Notion**: Rich text, collaborative, good for initial brainstorming
**Best Practices**: **Best Practices**:
- Write in user story format: "As a [role], I want [feature] so that [benefit]" - Keep it in a shared wiki (Notion, GitHub Wiki) for collaborative editing
- Include acceptance criteria as checklist items - Focus on outcomes, not solutions
- Link to related specs and architecture decisions - Explicitly state what you will NOT build
**Example**: "System must support 5-member teams with real-time sync."
--- ---
### 2. The Spec (The Contract) ### 2. Spec (Specification)
**Purpose**: Machine-readable truth that defines the API contract. **Purpose**: Create a machine-readable technical contract.
**Why it matters**: The spec is the single source of truth for how systems communicate. It enables code generation, automated testing, and ensures consistency across services. **Why It Matters**: Communication gaps between services cause bugs. A strict, typed spec prevents these by being the Single Source of Truth.
**Primary Audience**: **Key Elements**:
- **Developers**: Implement the API according to the spec - **API Endpoints**: All routes with HTTP methods
- **QA Engineers**: Create test cases based on the spec - **Data Types**: Strict typing with validation rules
- **Machines**: Used for code generation, validation, and documentation - **Error Codes**: Comprehensive error response definitions
- **Naming Conventions**: snake_case keys, consistent patterns
**Format / Tooling**:
- **OpenAPI (Swagger)**: REST API specifications
- **Protobuf**: gRPC service definitions
- **YAML/JSON**: Configuration and data schema definitions
**Best Practices**: **Best Practices**:
- Use snake_case for consistency - Use OpenAPI (YAML/JSON) for REST APIs or Protobuf for gRPC
- Define all fields with types and constraints - Automate generation of client/server code from the spec
- Include examples for complex data structures - Run contract tests against the spec in CI/CD
- Keep specs versioned alongside code
**Example**: A `.yaml` file defining `user_id` as a UUID in snake_case.
--- ---
### 3. Architecture ### 3. Architecture
**Purpose**: High-level structural blueprint showing how components interact. **Purpose**: Visualize the system structure and data flow.
**Why it matters**: Architecture diagrams help everyone understand the system's structure without drowning in implementation details. They're crucial for onboarding, design reviews, and long-term maintainability. **Why It Matters**: Complex systems (like your 6-node cluster) need clear maps. Without them, teams can't identify bottlenecks or make informed decisions.
**Primary Audience**: **Key Elements**:
- **Senior Developers**: Design decisions and component responsibilities - **System Context Diagram**: Shows the system and its external dependencies
- **DevOps**: Understand deployment topology and service dependencies - **Database ERD**: Entity-Relationship diagrams for data model
- **Technical Leads**: Evaluate trade-offs and scalability concerns - **Network Security Policies**: Firewall rules, service mesh configs
- **Infrastructure Maps**: Cloud resources, scaling groups
**Format / Tooling**:
- **Mermaid.js**: Code-based diagrams that are version-controlled
- **IcePanel**: Interactive, automated architecture visualization
- **C4 Model**: Standardized approach to architectural diagrams
**Best Practices**: **Best Practices**:
- Focus on *relationships* between components, not implementation details - Use Mermaid.js for diagrams-as-code (versionable, diffable)
- Include technology choices (e.g., NATS vs WebSocket)
- Show data flow direction with arrows
- Update diagrams when architecture changes - Update diagrams when architecture changes
- Focus on data flow and decision points
**Example**: Diagram of SvelteKit ↔ NATS ↔ Julia 6-node cluster.
--- ---
### 4. Walkthrough ### 4. Walkthrough
**Purpose**: The intuition and "Big Picture" narrative. **Purpose**: Build a mental model through narrative.
**Why it matters**: Code alone doesn't explain *why* decisions were made. Walkthroughs provide context, historical decisions, and architectural intuition that helps new developers become productive quickly. **Why It Matters**: Code doesn't explain *why*. Walkthroughs capture the reasoning behind architectural trade-offs, making onboarding faster and reducing conceptual bugs.
**Primary Audience**: **Key Elements**:
- **New Developers**: Understand the system's philosophy and patterns - **Step-by-step traces**: End-to-end flow of user actions
- **The Team**: Share context and reasoning behind design choices - **Trade-off explanations**: Why you chose option A over B
- **Code Reviewers**: Evaluate design decisions alongside implementation - **The Big Picture**: How components fit together conceptually
**Format / Tooling**:
- **Recorded Video**: Personal, engaging, good for complex explanations
- **TOUR.md**: Markdown file with narrative walk-through of the codebase
- **Architecture Decision Records (ADRs)**: Formal documentation of key decisions
**Best Practices**: **Best Practices**:
- Explain *why* more than *how* - Write in a TOUR.md file or record Loom videos
- Include anti-patterns to avoid - Focus on intuition, not just mechanics
- Link to related documentation - Include "Rationale" sections for each major decision
- Keep walkthroughs updated with architecture changes
**Example**: "Why we use a Claim-Check pattern for large Arrow data."
--- ---
### 5. Implementation ### 5. Implementation
**Purpose**: The actual logic and generated code. **Purpose**: The functional reality - the actual code.
**Why it matters**: This is the executable truth of the system. Well-structured implementation code should be clear, maintainable, and follow established patterns. **Why It Matters**: This is what runs in production. In SDD, the spec-driven approach ensures boring parts are generated automatically, so developers focus on business logic.
**Primary Audience**: **Key Elements**:
- **Developers**: Read, modify, and extend the code - **Business Logic**: The unique value you provide
- **Reviewers**: Verify correctness and adherence to standards - **Unit Tests**: Covering edge cases and error paths
- **CI/CD**: Run tests and builds - **README.md**: Local environment setup instructions
**Format / Tooling**:
- **SvelteKit**: Frontend framework with server-side rendering
- **Julia**: High-performance numerical computing
- **Node.js**: Backend services and tooling
**Best Practices**: **Best Practices**:
- Generate code from specs to ensure consistency - Generate boilerplate (types, routes) from the Spec
- Use consistent naming conventions (snake_case, camelCase appropriately) - Maintain 90%+ test coverage
- Include unit tests alongside implementation - Keep README.md up-to-date for local development
- Document complex algorithms with inline comments
**Example**: Auto-generated TypeScript types from the OpenAPI spec.
--- ---
### 6. Validation ### 6. Validation
**Purpose**: Automated "Contract" enforcement. **Purpose**: Automated quality gates.
**Why it matters**: Automated tests ensure that the system behaves as specified and prevent regressions. Validation in CI/CD pipelines catches issues before they reach production. **Why It Matters**: Human error happens. Validation layers catch mistakes before they reach production, preventing contract violations and security issues.
**Primary Audience**: **Key Elements**:
- **CI/CD Pipelines**: Run tests automatically on every commit - **Contract Tests**: Verify implementation matches spec (Dredd, Prism)
- **QA Engineers**: Verify system behavior against requirements - **Integration Tests**: Test service-to-service interactions
- **Developers**: Get immediate feedback on changes - **Security Scans**: SAST/SBOM analysis on every PR
**Format / Tooling**:
- **GitHub Actions**: Automated testing and validation workflows
- **Prism (ReadMe)**: OpenAPI spec validation in CI
- **Jest/Vitest**: JavaScript testing framework
- **Pytest**: Python testing framework
**Best Practices**: **Best Practices**:
- Test the contract (spec) not just implementation details - Run validation on every pull request
- Use contract testing (PACT) for service-to-service validation - Block merges on contract violations
- Fail fast: tests should run quickly and provide clear error messages - Track build success rate as a KPI
- Include negative test cases (invalid inputs, edge cases)
**Example**: A test that fails if the Julia API returns camelCase keys.
--- ---
### 7. Runbook ### 7. Maintenance
**Purpose**: Deployment, scaling, and recovery procedures. **Purpose**: Guide for long-term health and evolution.
**Why it matters**: Runbooks ensure that deployments are consistent, repeatable, and recoverable. In GitOps, the runbook *is* the configuration, version-controlled alongside the code. **Why It Matters**: Software decays. Without a maintenance plan, dependency upgrades become risky, secrets accumulate, and technical debt piles up.
**Primary Audience**: **Key Elements**:
- **DevOps Engineers**: Execute deployments and scaling operations - **Dependency Update Schedule**: When and how to upgrade packages
- **SREs**: Manage system reliability and incident response - **Secret Rotation Steps**: How to rotate credentials securely
- **Developers**: Deploy feature branches for testing - **DB Migration Logs**: History of schema changes
- **Tech Debt "Graveyard"**: Documented technical debt with remediation plans
**Format / Tooling**:
- **Kubernetes Manifests**: Declarative deployment configurations
- **Flux**: GitOps operator for Kubernetes
- **Helm Charts**: Package management for Kubernetes
- **Docker Compose**: Local development environments
**Best Practices**: **Best Practices**:
- Use Git as the source of truth (GitOps) - Document the "how" for common maintenance tasks
- Make deployments idempotent (running twice has same effect) - Track package age and security vulnerabilities
- Include rollback procedures - Schedule regular tech debt reviews
- Document scaling procedures for different load levels
**Example**: `git push` to update the replica count from 3 to 6.
--- ---
## How the Stack Fits Together ### 8. Runbook
``` **Purpose**: Operational life-support for production systems.
┌─────────────────────────────────────────────────────────────┐
│ Requirements │
│ (Business goals, user needs) │
└───────────────────┬─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ The Spec │
│ (Machine-readable contract: OpenAPI, Protobuf) │
└───────────────────┬─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Architecture │
│ (Structural blueprint: Mermaid, IcePanel) │
└───────────────────┬─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Walkthrough │
│ (Intuition, big picture narrative) │
└───────────────────┬─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Implementation │
│ (Actual code: SvelteKit, Julia, Node.js) │
└───────────────────┬─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Validation │
│ (Automated tests: GitHub Actions, Prism) │
└───────────────────┬─────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ Runbook │
│ (Deployment, scaling: K8s, Flux) │
└─────────────────────────────────────────────────────────────┘
```
## Key Principles **Why It Matters**: When production is down, teams need clear instructions. In GitOps, the runbook is the "desired state" that the system constantly works toward.
1. **Machine-Readable Truth**: Specs and configurations should be machine-readable to enable automation **Key Elements**:
2. **Separation of Concerns**: Different audiences need different types of information - **Deployment Steps**: How to deploy new versions
3. **Version Control**: All documentation should be in Git, just like code - **Scaling Triggers**: When and how to scale up/down
4. **Automation-First**: Validation should be automated and integrated into CI/CD - **Backup/Restore Procedures**: Disaster recovery steps
5. **Living Documentation**: Documentation should evolve with the codebase - **"3:00 AM" Troubleshooting**: Quick fixes for common failures
## Getting Started **Best Practices**:
- Store in K8s manifests (Flux/Argo) for GitOps
- Automate as much as possible
- Test runbook procedures regularly
To adopt this stack in your project: ---
1. Start with requirements in GitHub Issues or Notion ## How to Use This Framework
2. Create a spec file (OpenAPI/Protobuf) as the contract
3. Add architecture diagrams using Mermaid.js
4. Write a walkthrough explaining the "why" behind decisions
5. Implement code following the spec
6. Add automated tests that validate the spec
7. Create runbooks for deployment and scaling
This framework ensures that every piece of documentation serves a clear purpose and reaches the right audience. 1. **Start with Requirements** - Define the business problem and success criteria
2. **Create the Spec** - Translate requirements into machine-readable contracts
3. **Design Architecture** - Visualize how the system will work
4. **Write Walkthrough** - Document the logic and trade-offs
5. **Implement** - Build the actual code
6. **Set up Validation** - Add automated tests and gates
7. **Document Maintenance** - Plan for long-term health
8. **Create Runbook** - Define operational procedures
This framework ensures that every document serves a clear purpose and that your project remains maintainable, scalable, and aligned with business goals.