govkit 0.5.0

Governed AI delivery kit — spec-driven scaffolding for AI coding agents

govkit — Governed AI Delivery

AI coding agents are powerful — but without constraints, they drift. They invent architecture, skip tests, ignore NFRs, and make decisions that belong to your team. govkit puts the agent inside a governed system where your architecture contracts, acceptance criteria, and evaluation thresholds are the source of truth, not the agent's training data.

pip install govkit
govkit apply --agent claude-code --target .

One command installs govkit. One more applies it to your project. The governance workflow your team follows from that point on is what keeps the agent aligned, every feature, every time.

govkit works with any project language — Python, C#, Java, Go, TypeScript, or anything else. It copies Markdown specs, YAML configs, and Gherkin feature files into your project directory. Python is a dev-machine tool requirement only; it is not added as a project dependency.

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Table of Contents

• Prerequisites
• Quickstart
• Maturity Levels
• Supported Agents
• Key Concepts
• Working With the Agent
• Project Type Details
• Switching Tech Stacks
• Interpreting Validation Failures
• Troubleshooting & FAQ
• Multi-Repository Features
• Architecture Reference
• Evaluation Reference
• Getting Help
• License
• Glossary
• Resources

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Prerequisites

• Python 3.11+ — required to install and run govkit (does not need to be added to your project)
• pip — for installation (pip install govkit)
• An AI coding agent — Claude Code, GitHub Copilot, or OpenAI Codex (govkit provides the configuration, not the agent itself)

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Quickstart

1. Install govkit

pip install govkit

2. List available agents

govkit list

3. Apply to your project

From your project root, run govkit apply and answer the prompts:

govkit apply --agent claude-code --target .

Or specify all options explicitly:

# Level 3: Spec-driven development (no architecture changes imposed)
govkit apply --agent claude-code --level 3 --type api --ui none --ci github --target .

# Level 4: Full governed AI delivery (default)
govkit apply --agent claude-code --level 4 --type api --ui react --ci github --target .

# Level 5: GenAI operations (LLM routing, evaluation, guardrails)
govkit apply --agent claude-code --level 5 --type api --ui none --ci github --target .

# Python CLI tool + Azure DevOps (no UI)
govkit apply --agent copilot --type cli --ui none --ci azure --target .

# API backend only + GitHub Actions
govkit apply --agent copilot --type api --ui none --ci github --target .

# OpenAI Codex, L4 API backend + React UI
govkit apply --agent codex --type api --ui react --ci github --target .

A .govkit marker file is written to your project root, tracking the applied level and options. This enables govkit init and govkit validate to auto-detect your level.

When using interactive mode (no --type, --ui, --ci flags), you'll see prompts like:

$ govkit apply --agent claude-code --target .

Applying govkit agent 'claude-code' to /path/to/your/project

  Project type? [api / cli] (default: api): api
  UI framework? [none / react / angular] (default: none): react
  CI platform? [github / azure] (default: github): github

  Configuration: {'type': 'api', 'ui': 'react', 'ci': 'github'}

Agent files:
  copied  /path/to/your/project/CLAUDE.md
  copied  /path/to/your/project/.claude/rules/api.md
  ...

4. Verify installation

After applying, your project will contain:

your-project/
├── CLAUDE.md (or .github/copilot-instructions.md, or AGENTS.md)
├── .claude/rules/ (or .github/instructions/, or nested AGENTS.md per layer)
│   ├── api.md, services.md, ports.md, adapters.md, security.md
│   └── (UI rules if --ui was specified)
├── .claude/skills/ (or .github/skills/, or .agents/skills/)
│   ├── architecture-preflight/, spec-planning/, implementation-plan/, adr-author/
│   └── (UI skills if --ui was specified)
├── docs/
│   ├── backend/architecture/   — ARCH_CONTRACT, API_CONVENTIONS, TECH_STACK, etc.
│   └── backend/evaluation/     — eval_criteria.md, scoring rubrics
├── features/                   — created empty; scaffold your first feature with govkit init
├── governance/
│   └── backend/schemas/        — eval_criteria.schema.json
└── ci/
    └── github/ (or azure/)     — quality-gate.yml, eval-gate.yml

If --ui react or --ui angular was specified, you'll also see docs/ui/, governance/ui/, and UI-specific CI pipelines.

Starter templates and worked examples are bundled inside the govkit package, not copied into your project by govkit apply. Use govkit init <feature-name> to scaffold a new feature from the appropriate starter, or run govkit list to see what is available.

5. Customize your governance artifacts

[!WARNING] This step is not optional. The installed docs/ files are authoritative starting points — they reflect sound defaults, but they are written for a generic project. Your agent will treat them as law. If you skip this step, the agent will govern your project against someone else's architecture decisions.

Before writing a single line of feature code, review and update the following to match your project:

Backend projects (API or CLI):

• docs/backend/architecture/TECH_STACK.md — replace with your actual approved libraries, frameworks, and versions
• docs/backend/architecture/ARCH_CONTRACT.md — confirm the hexagonal layer names and boundaries match your codebase structure
• docs/backend/architecture/API_CONVENTIONS.md (API) or docs/backend/architecture/CLI_CONVENTIONS.md (CLI) — update conventions to match your project standards
• docs/backend/architecture/SECURITY_AUTH_PATTERNS.md — replace with your actual auth provider, token pattern, and scope conventions
• docs/backend/evaluation/eval_criteria.md — confirm FIRST and 7 Virtue thresholds are appropriate for your team's standards

React UI projects:

• docs/ui/architecture/react/TECH_STACK.md — confirm your React version, state management libraries, and testing stack
• docs/ui/architecture/react/COMPONENT_CONVENTIONS.md — update to reflect your project's folder structure and naming conventions
• docs/ui/evaluation/eval_criteria.md — confirm accessibility standard and FIRST thresholds

Angular UI projects:

• docs/ui/architecture/angular/TECH_STACK.md — confirm your Angular version, TanStack Query setup, and testing stack
• docs/ui/architecture/angular/COMPONENT_CONVENTIONS.md — update to reflect your project's folder structure and naming conventions
• docs/ui/evaluation/eval_criteria.md — confirm accessibility standard and FIRST thresholds

These files are the source of truth for your AI agent. The agent reads them before every planning and implementation step. Keep them accurate and up to date as your project evolves.

6. Validate governance compliance

govkit validate --target .

Validation is level-aware. Level 3 checks: 3 required artifacts, Gherkin structure, NFR completeness, and tag coverage. Level 4 adds: eval_criteria.yaml schema validation, evaluation prediction thresholds. The level is auto-detected from .govkit or can be overridden:

govkit validate --level 3 --target .

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Maturity Levels

govkit supports three operating levels, allowing teams to adopt incrementally:

Level	Name	What You Get
Level 3	Spec-Driven Development	Spec-first, test-first workflow. 3 artifacts per feature (acceptance.feature, nfrs.md, plan.md). Generic agent rules. Basic CI gates. No architecture changes imposed.
Level 4	Governed AI Delivery	Full governance. 5 artifacts per feature (adds eval_criteria.yaml, architecture_preflight.md). Architecture contracts, FIRST/Virtues scoring, evaluation prediction thresholds, boundary enforcement, path-scoped rules.
Level 5	GenAI Operations	Everything in L4 plus governed GenAI tooling: LiteLLM (model routing), OpenLLMetry + Langfuse (observability), DeepEval + Promptfoo + RAGAS (evaluation), NeMo Guardrails + Guardrails AI (runtime safety). LLM-specific NFRs, CI evaluation gates, and adversarial testing.

Start at Level 3 if your team wants spec-driven development without changing their existing project structure. Move to Level 4 when ready for full architectural governance and evaluation scoring. Move to Level 5 when building LLM-powered features that need governed model routing, evaluation, and safety.

Level 3 — Spec-Driven Development

Every feature is:

• Defined with Gherkin acceptance criteria tagged to NFR categories
• Constrained with fully populated NFRs (no TBD entries permitted)
• Planned with increments that list tests before implementation (test-first)
• Enforced by basic CI gates (artifact checks, commit format, lint, tests)

Level 4 — Governed AI Delivery

Everything in Level 3, plus:

• Governed by evaluation criteria validated against a JSON Schema
• Planned through Architecture Preflight + Implementation Plan prompts
• Enforced by CI gates, quality rules, and evaluation thresholds
• Bounded by hexagonal architecture contracts with import-linter enforcement

Level 5 — GenAI Operations

Everything in Level 4, plus:

• Routed through LiteLLM as the sole LLM gateway (model routing, fallback, cost tracking)
• Observed via OpenLLMetry + Langfuse (LLM-specific telemetry, trace storage, prompt versioning)
• Evaluated with DeepEval (quality metrics), Promptfoo (adversarial testing), and RAGAS (retrieval evaluation)
• Guarded by NeMo Guardrails (conversational safety) and Guardrails AI (structured output validation)
• Extended with LLM-specific NFRs (latency, cost, fallback, safety) and 3 additional CI gates

The AI agent operates inside a governed system. Architecture, evaluation, and feature artifacts are the source of truth — not the agent.

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Supported Agents

govkit installs agent-specific configuration files into your target project at the paths shown below. Three agents are supported, each with the same variant options:

Agent	AI Tool	Files installed into your project
claude-code	Claude Code	CLAUDE.md, .claude/rules/, .claude/skills/
copilot	GitHub Copilot	.github/copilot-instructions.md, .github/instructions/, .github/skills/
codex	OpenAI Codex	AGENTS.md (root + nested per layer), .agents/skills/

All agents support the same variant options:

Option	Choices	Default
--level	3, 4, 5	4
--type	api, cli	api
--ui	none, react, angular	none
--ci	github, azure	github

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Key Concepts

Hexagonal Architecture (Ports & Adapters) — Your domain logic lives at the center, isolated from infrastructure. Inbound adapters (API routes, CLI commands) call inbound ports. Outbound ports define contracts that adapters (databases, APIs, message queues) implement. Domain code never imports infrastructure libraries.

MVVM (UI projects) — Model-View-ViewModel. Components (View) render UI. Hooks or inject functions (ViewModel) provide data and actions. API functions (Model) call the backend. Components never call APIs directly.

FIRST Principles — Test quality framework. Tests must be Fast, Isolated, Repeatable, Self-verifying, and Timely. Each principle is scored 1–5 with a minimum average of 4.0.

7 Code Virtues — Implementation quality framework. Code must be Working, Unique, Simple, Clear, Easy to maintain, Developed (tested and clean), and Brief. Each virtue is scored 1–5 with a minimum average of 4.0.

Gherkin Acceptance Criteria — Features are specified using Given/When/Then scenarios. Scenarios are tagged with NFR categories (@nfr-performance, @nfr-security) to ensure non-functional requirements have test coverage.

Governed Development — The agent reads architecture contracts, evaluation criteria, and feature specs before generating code. CI gates enforce compliance. The agent proposes; your governance artifacts decide.

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Working With the Agent

Once govkit is installed, here is how you interact with the agent to deliver a feature. This lifecycle applies to every feature, regardless of project type or agent.

Step 1: Create a Feature Folder

Use govkit init to create a feature from the appropriate starter:

govkit init my_feature --target .

Or specify the starter type explicitly:

govkit init my_feature --starter backend --target .

The command auto-detects your maturity level from .govkit and selects the appropriate starter template. Level 3 starters have 3 artifacts; Level 4 starters have 5. You can override with --level 3 or --level 4.

For Level 4 projects, each starter's eval_criteria.yaml includes mode selection instructions at the top. Set the mode field to match your feature type: llm (LLM generation/retrieval), deterministic (pure logic), or none (configuration artifacts). If the mode is deterministic or none, delete the llm_evaluation section.

Step 2: Write Your Acceptance Criteria

Edit features/my_feature/acceptance.feature with your Gherkin scenarios:

• Write happy path and failure/edge case scenarios
• Tag NFR scenarios with @nfr-performance, @nfr-security, etc.
• Tag E2E scenarios with @e2e (UI projects)
• Add @contract scenarios if the feature produces shared artifacts

Step 3: Complete Your NFRs

Edit features/my_feature/nfrs.md — replace every TBD entry with concrete requirements. The agent will refuse to proceed if any TBD entries remain.

Step 4: Run Architecture Preflight

Ask the agent to validate your feature against the architecture contracts:

Agent	Command
Claude Code	/architecture-preflight my_feature
Copilot	/architecture-preflight (with feature context)
Codex	$architecture-preflight my_feature

The agent produces architecture_preflight.md covering boundary analysis, security impact, evaluation impact, and whether an ADR is needed. If an ADR is required, create it next:

Agent	Command
Claude Code	/adr-author my_feature
Copilot	/adr-author
Codex	$adr-author my_feature

Step 5: Generate the Plan

Ask the agent to create the implementation plan:

Agent	Command
Claude Code	/spec-planning my_feature
Copilot	/spec-planning
Codex	$spec-planning my_feature

The agent generates plan.md and eval_criteria.yaml. The plan includes:

• Increments with deliverables and tests
• An Evaluation Compliance Summary predicting FIRST and 7 Virtue scores
• Each increment sized as a single committable unit (~300 lines)

The agent will not proceed if predicted averages are below 4.0.

Step 6: Review the Implementation Plan

Ask the agent to break the plan into a detailed task checklist:

Agent	Command
Claude Code	/implementation-plan my_feature
Copilot	/implementation-plan
Codex	$implementation-plan my_feature

Review and approve before implementation begins.

Step 7: Implement Incrementally

Work through the plan one increment at a time. For each increment:

1. The agent writes production code respecting architecture boundaries
2. The agent writes tests (unit, integration, contract as needed)
3. You review and commit: feat(my_feature): increment 1 — <name>
4. Move to the next increment

Do not skip increments or combine multiple increments into one commit.

Step 8: Push and Merge

Open a PR. CI gates automatically run:

• Schema validation of eval_criteria.yaml
• FIRST and 7 Virtue prediction completeness
• Unit, component, and E2E tests
• Architecture boundary enforcement
• Security scan and quality gates
• Accessibility checks (UI projects)

All gates must pass before merge.

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Project Type Details

The 8-step workflow above applies to all project types. Key differences by type:

Backend API

Architecture: Hexagonal Architecture — ports and adapters. API routes are the inbound adapter layer. See docs/backend/architecture/API_CONVENTIONS.md.

Layer rules (load automatically when editing files):

• api.md for **/api/**
• services.md for **/services/**
• ports.md for **/ports/**
• adapters.md for **/adapters/**
• security.md for **/security/** and **/auth/**

CI gates: ci/github/quality-gate.yml, ci/github/eval-gate.yml (or ci/azure/ for Azure DevOps)

CLI

Architecture: Hexagonal Architecture — CLI commands are the inbound adapter layer (same position as API routes). See docs/backend/architecture/CLI_CONVENTIONS.md.

Layer rules (load automatically):

• cli.md for **/cli/** and **/commands/**
• services.md, ports.md, adapters.md, security.md as above

CI gates: Same backend gates — ci/github/quality-gate.yml, ci/github/eval-gate.yml

React UI

Architecture: MVVM with vertical slice feature structure. Layer order is API → ViewModel → View. See docs/ui/architecture/MVVM_CONTRACT.md.

Layer rules (load automatically):

• components.md for View layer
• viewmodel.md for hooks and store
• api.md for API client functions
• accessibility.md for accessibility concerns

Implementation order: API functions → React Query hooks → Zustand stores → Components → E2E tests

CI gates: ci/github/ui-quality-gate.yml, ci/github/ui-eval-gate.yml

Angular UI

Architecture: MVVM with vertical slice feature structure. Standalone components with OnPush. See docs/ui/architecture/MVVM_CONTRACT.md.

Layer rules: Same as React, with Angular-specific content.

Implementation order: API functions → TanStack Query inject functions → Signal stores → Standalone components → E2E tests

CI gates: ci/github/ui-quality-gate.yml, ci/github/ui-eval-gate.yml

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Switching Tech Stacks

The default installed stack is Python / FastAPI. To switch to a different backend language, copy the 6 stack-specific architecture doc files from docs/stacks/<stack>/ into docs/backend/architecture/. The AI agents read those files as the authoritative source of truth — no agent rules, manifests, or CLI configuration changes needed.

Why only 6 files?

The agent rules and most architecture docs are language-agnostic. Only these 6 files define stack-specific conventions:

File	What it defines
TECH_STACK.md	Languages, versions, approved frameworks
API_CONVENTIONS.md	Route patterns and request/response idioms
TESTING.md	Test framework, mocking library, BDD tool
LAYER_IMPLEMENTATION.md	DI patterns, interface idioms, DTO style
SECURITY_AUTH_PATTERNS.md	Auth libraries, token handling, hashing
OBSERVABILITY_PORT_CONTRACT.md	Structured logging library, OTel SDK

All other docs (DESIGN_PRINCIPLES, ARCH_CONTRACT, BOUNDARIES, GHERKIN_CONVENTIONS, ERROR_MAPPING, etc.) are universal and require no changes.

Available stacks

Directory	Stack
docs/stacks/dotnet-aspnet/	C# 12 / .NET 8 / ASP.NET Core Minimal APIs
docs/stacks/java-spring-boot/	Java 21 / Spring Boot 3 / Spring Web MVC
docs/stacks/nodejs-fastify/	Node.js 20 LTS / TypeScript 5 / Fastify 4
docs/stacks/go-gin/	Go 1.22+ / Gin

How to switch

# Switch to C# / ASP.NET Core
cp docs/stacks/dotnet-aspnet/* docs/backend/architecture/

# Switch to Java / Spring Boot
cp docs/stacks/java-spring-boot/* docs/backend/architecture/

# Switch to Node.js / Fastify
cp docs/stacks/nodejs-fastify/* docs/backend/architecture/

# Switch to Go / Gin
cp docs/stacks/go-gin/* docs/backend/architecture/

After copying, review the files and update any project-specific details (approved library versions, internal service names, etc.). Consider raising an ADR to document the stack decision.

See docs/stacks/README.md for the complete guide, including how to add new stacks.

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Interpreting Validation Failures

When govkit validate --target . reports failures, here's what they mean and how to fix them:

Failure	Meaning	Fix
acceptance.feature not found	Feature folder is missing its Gherkin spec	Copy from starter and write scenarios
no Feature: keyword or no Scenario: keyword	Gherkin file exists but is malformed	Add Feature: header and at least one Scenario: with Given/When/Then
nfrs.md contains TBD entries	NFR categories have placeholder values	Replace every TBD with a concrete, measurable requirement
eval_criteria.yaml missing or invalid	Eval config doesn't match the JSON Schema	Check governance/*/schemas/eval_criteria.schema.json for required fields
plan.md missing evaluation_prediction	Plan exists but has no prediction block	Add the evaluation_prediction YAML block (see worked examples)
predicted FIRST average below 4.0	Predicted test quality is below threshold	Revise the plan — improve test strategy or split complex increments
predicted Virtue average below 4.0	Predicted code quality is below threshold	Revise the plan — simplify design, reduce complexity, improve separation
NFR tag coverage incomplete	Some NFR categories lack corresponding Gherkin tags	Add @nfr-<category> tags to relevant scenarios in acceptance.feature

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Troubleshooting & FAQ

Q: govkit: command not found after installation A: Ensure your Python scripts directory is on your PATH. Try python -m cli.govkit as a fallback, or reinstall with pip install --user govkit.

Q: govkit apply fails with "no agent found" A: Check that you're using a valid agent name (claude-code, copilot, or codex). Run govkit list to see available agents.

Q: The agent ignores my architecture rules A: Verify the rules files were copied to the correct location (.claude/rules/, .github/instructions/, or the nested AGENTS.md files for Codex). Check that file paths match what the agent expects — Claude Code loads rules based on the file path you're editing, Codex walks the directory tree from repo root down to the current working directory and concatenates each AGENTS.md it finds.

Q: How do I update to a newer version of govkit? A: Run pip install --upgrade govkit. Then re-run govkit apply — it will skip files that already exist. To force update a specific file, delete it first.

Q: Can I use govkit on an existing project with existing code? A: Yes. govkit apply copies governance artifacts into your project without modifying existing code. You may need to adjust docs/backend/architecture/ARCH_CONTRACT.md and other docs to reflect your existing architecture rather than the defaults.

Q: What if my architecture doesn't match the Hexagonal defaults? A: Customize the architecture docs after install. The agent follows whatever ARCH_CONTRACT.md says — if your project uses a different pattern, document it there. Consider creating an ADR explaining the architectural choice.

Q: Can I use multiple agents in the same project? A: Yes. Run govkit apply once for each agent. They install to different paths (.claude/, .github/, and AGENTS.md + .agents/ for Codex) and share the same docs/, governance/, and features/ artifacts. All agents read the same architecture contracts.

Q: How do I add a new NFR category? A: Add the category as a ## Heading in your feature's nfrs.md, add corresponding @nfr-<category> tags to acceptance scenarios, and update cli/validate.py's category_to_tag mapping if you want automated tag coverage validation.

Q: The CI pipeline fails because SonarQube/Snyk isn't configured A: These tools are optional. If your team doesn't use them, remove or comment out those jobs from the CI pipeline files. See ci/README.md for details on required secrets.

Q: What does "thresholds_met: false" mean in my plan? A: Your predicted FIRST or Virtue average is below 4.0, or a predicted accessibility violation count is above zero. Revise the plan — simplify the design, improve test strategy, or split large increments before proceeding.

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Multi-Repository Features

If your feature spans multiple repositories (e.g., Auth Service + Client SDK + API Gateway), see:

• CROSS_REPO_FEATURES.md — Complete guide to planning, implementing, and testing features across repos
• REPO_SCOPE_ANALYSIS_GUIDANCE.md — How to declare repo ownership in your feature spec
• features/example-jwt-unification/ — Worked example of a 3-repo JWT authentication feature

The key principle: Every feature must declare which repositories own which parts. This prevents agents from writing code in the wrong repo.

Multi-Repo FAQ

Q: My feature needs changes in Auth Service, API Gateway, and Frontend. Where do I document this? A: In the primary owner repo's features/<feature>/nfrs.md, add a "Repository Scope" section with a table listing each repo, owner team, modules, and contracts. See CROSS_REPO_FEATURES.md#repository-ownership-table.

Q: Can we implement the feature in just one repo and copy code to the others later? A: No — this violates ownership and creates duplication. Each repo implements its own portion against the shared contract. See CROSS_REPO_FEATURES.md#common-pitfalls.

Q: Should we wait for Repo A to finish before Repo B starts? A: No. Each repo implements in parallel using mocks for external dependencies. Only the final integration tests (after all repos merge) verify cross-repo contracts. See CROSS_REPO_FEATURES.md#implementation-stage-parallel.

Q: How do we test a feature that depends on another repo's code? A: Each repo has unit tests (mocking externals) and contract tests (verifying its own implementation). After all repos merge to main, run integration tests to verify end-to-end behavior. See CROSS_REPO_FEATURES.md#testing-strategy.

Q: What if the repos have deployment dependencies (one must be live before the other)? A: Document the order in your nfrs.md "Key Cross-Repo Contracts" section. Ideally, design contracts to be backward-compatible so deployment order is flexible. See CROSS_REPO_FEATURES.md#integration-stage-sequential.

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Architecture Reference

The files linked below are installed into your project by govkit apply. Links point to their source in the govkit repository for reference.

Backend (Core — Level 4)

• ARCH_CONTRACT.md — Hexagonal Architecture contract
• BOUNDARIES.md — Layer dependency rules
• API_CONVENTIONS.md — FastAPI conventions
• CLI_CONVENTIONS.md — Click/Typer conventions
• DESIGN_PRINCIPLES.md — SOLID, DRY, YAGNI, KISS
• GHERKIN_CONVENTIONS.md — NFR tags, coverage rules
• GHERKIN_TAGS.md — Standard tag reference
• SECURITY_AUTH_PATTERNS.md
• TECH_STACK.md
• TESTING.md
• AGENT_ARCHITECTURE.md — AI agent design patterns (LangGraph, tools, evaluation)
• ERROR_MAPPING.md — Domain exception to HTTP status mapping
• OBSERVABILITY_PORT_CONTRACT.md — Observability port interface
• CROSS_CUTTING_CONCERNS.md — DTOs, validation, pagination, timestamps
• ADR/TEMPLATE.md

Backend (GenAI Contracts — Level 5)

• LLM_GATEWAY_CONTRACT.md — LiteLLM as sole LLM gateway, provider abstraction, fallback, cost tracking
• OBSERVABILITY_LLM_CONTRACT.md — OpenLLMetry (telemetry emission) + Langfuse (trace storage, prompt versioning)
• GUARDRAILS_CONTRACT.md — NeMo Guardrails (conversational safety) + Guardrails AI (structured output validation)
• EVALUATION_LLM_CONTRACT.md — DeepEval (quality), Promptfoo (adversarial), RAGAS (retrieval)

Backend (Practical Guides — Level 5)

• litellm-setup.md — LiteLLM proxy config, model aliases, fallback chains
• openllmetry-setup.md — Auto-instrumentation, export to Langfuse
• langfuse-integration.md — Trace viewing, prompt management, dashboards
• deepeval-usage.md — Writing DeepEval test cases, metrics, CI integration
• promptfoo-usage.md — Adversarial configs, red-team suites
• nemo-guardrails-setup.md — Colang dialog definitions, rail configs
• guardrails-ai-setup.md — Guard definitions, validator config
• ragas-evaluation.md — RAG-specific metrics, dataset preparation

UI (Shared)

• MVVM_CONTRACT.md — framework-agnostic MVVM contract
• ADR/TEMPLATE.md

React UI

• COMPONENT_CONVENTIONS.md
• STATE_MANAGEMENT.md — React Query + Zustand
• TECH_STACK.md

Angular UI

• COMPONENT_CONVENTIONS.md
• STATE_MANAGEMENT.md — TanStack Angular Query + Signals
• TECH_STACK.md

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Evaluation Reference

Standards (All Levels)

• eval_criteria.md — FIRST, 7 Code Virtues, LLM eval, scoring model
• FIRST_SCORING_RUBRIC.md — 1-5 scoring definitions per FIRST principle
• VIRTUE_SCORING_RUBRIC.md — 1-5 scoring definitions per virtue
• EVAL_STACK.md — Evaluation tooling by environment (Langfuse, DeepEval, Promptfoo, RAGAS, home-grown)

Schemas

• eval_criteria.schema.json — Validates feature eval_criteria.yaml (includes L5 eval_class values)
• evaluation_prediction.schema.json — Schema for plan.md prediction blocks (includes optional L5 llm_evaluation section)
• guardrails_config.schema.json — Validates guardrails configuration (L5)

UI Evaluation

• eval_criteria.md — FIRST, accessibility, E2E coverage
• FIRST_SCORING_RUBRIC.md — UI-adapted FIRST scoring
• VIRTUE_SCORING_RUBRIC.md — UI-adapted virtue scoring
• eval_criteria.schema.json

Evaluation by Level

Level	What's Evaluated	Tools
L3	Spec completeness, Gherkin structure, NFR coverage	govkit validate
L4	L3 + FIRST scores, 7 Virtue scores, eval_criteria schema	govkit validate + CI quality/eval gates
L5	L4 + LLM quality (DeepEval), adversarial safety (Promptfoo), retrieval quality (RAGAS), guardrails config	govkit validate + deepeval-gate + promptfoo-gate + guardrails-check

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Getting Help

• Issues and feature requests: github.com/Accelerated-Innovation/governed-ai-delivery/issues
• Contributing: See CONTRIBUTING.md for development setup, project structure, and contribution guidelines
• Changelog: See CHANGELOG.md for release history
• CI Reference: See ci/README.md for what's enforced vs predicted in each CI gate

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License

Copyright 2026 Accelerated Innovation

Licensed under the Apache License, Version 2.0. See LICENSE for details.

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Glossary

Term	Definition
Agent	The AI coding tool (Claude Code, GitHub Copilot, or OpenAI Codex) that reads governance artifacts and generates code
Rule (Claude Code)	A path-scoped .md file in .claude/rules/ that loads automatically when editing files matching its path
Skill (Claude Code)	A reusable prompt in .claude/skills/ invoked via slash command (e.g., /architecture-preflight)
Instruction (Copilot)	A path-scoped .md file in .github/instructions/ — Copilot equivalent of a rule
Skill (Copilot)	A reusable task in .github/skills/ invoked via slash command — open agent skills standard
AGENTS.md (Codex)	A markdown instructions file read by Codex. A root AGENTS.md applies globally; nested AGENTS.md files at layer directories scope rules to that subtree via directory walk
Skill (Codex)	A SKILL.md under .agents/skills/<name>/ invoked via $skill-name
Port	An interface defining a contract between layers (inbound ports for API entry, outbound ports for infrastructure)
Adapter	An implementation of a port that connects to infrastructure (database, external API, message queue)
Domain	Business logic that has no dependencies on frameworks or infrastructure
FIRST	Test quality framework — Fast, Isolated, Repeatable, Self-verifying, Timely (scored 1–5)
7 Virtues	Code quality framework — Working, Unique, Simple, Clear, Easy, Developed, Brief (scored 1–5)
ADR	Architecture Decision Record — documents and gates architectural changes
NFR	Non-Functional Requirement — performance, security, availability, etc.
Evaluation Prediction	Predicted FIRST and Virtue scores in plan.md — must average >= 4.0 before implementation
govkit validate	CLI command that checks all features for governance compliance (artifact completeness, thresholds)
/architecture-preflight	Agent skill that validates a feature against architecture contracts before planning ($architecture-preflight in Codex)
/genai-preflight	L5 agent skill that validates LLM gateway, observability, guardrails, and evaluation decisions ($genai-preflight in Codex)
/eval-suite-planning	L5 agent skill that plans DeepEval, Promptfoo, and RAGAS test suites ($eval-suite-planning in Codex)
LiteLLM	L5 sole LLM gateway — model routing, provider abstraction, fallback, cost tracking
OpenLLMetry	L5 LLM telemetry emission standard (OpenTelemetry for LLMs)
Langfuse	Trace storage, prompt versioning, and production evaluation visibility
DeepEval	L5 LLM quality evaluation — faithfulness, relevancy, hallucination metrics
Promptfoo	L5 adversarial and regression testing — jailbreak, injection, safety
RAGAS	L5 retrieval-specific evaluation — context recall, precision (RAG pipelines only)
NeMo Guardrails	L5 conversational safety — dialog flow control, topic boundaries, jailbreak prevention
Guardrails AI	L5 structured output validation — JSON schema enforcement on LLM responses

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Resources

Video Demo (COMING SOON)

Watch govkit apply on a FastAPI project — GitHub Copilot Prompts Explained