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govkit — Governed AI Delivery

PyPI version Python 3.11+ License Publish

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.

Table of Contents

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)

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. Each govkit apply configures one project shape: backend (api / cli) or UI (ui-react / ui-angular). Fullstack-in-one-repo is supported via the monorepo pattern — one govkit apply per subdirectory.

# Level 3: Governed AI Delivery (Foundations) — agent rules + architecture docs only (default)
govkit apply --agent claude-code --level 3 --type api --ci github --target .

# Level 4: Spec-Driven Add-On — adds the features/ directory and 5-artifact contract
govkit apply --agent claude-code --level 4 --type api --ci github --target .

# Level 5: GenAI Operations (LLM routing, evaluation, guardrails)
govkit apply --agent claude-code --level 5 --type api --ci github --target .

# Python CLI tool + Azure DevOps
govkit apply --agent copilot --type cli --ci azure --target .

# React UI project (L4)
govkit apply --agent copilot --level 4 --type ui-react --ci github --target .

# Angular UI project (L4) on OpenAI Codex
govkit apply --agent codex --level 4 --type ui-angular --ci github --target .

A .govkit marker file is written to your project root, tracking the agent, level, options, and govkit version. This enables govkit init, govkit validate, and govkit upgrade to auto-detect your configuration without re-specifying flags.

When using interactive mode (no --type, --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 / ui-react / ui-angular] (default: api): ui-react
  CI platform? [github / azure] (default: github): github

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

Agent files:
  copied  /path/to/your/project/CLAUDE.md
  copied  /path/to/your/project/src/CLAUDE.md
  copied  /path/to/your/project/.claude/rules/repo-scope.md
  ...

4. Verify installation

After applying, your project will contain artifacts appropriate to the shape you picked.

Backend shape (--type api or --type cli):

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, repo-scope.md
├── .claude/skills/ (or .github/skills/, or .agents/skills/)
│   └── architecture-preflight/, spec-planning/, implementation-plan/, adr-author/
├── docs/backend/
│   ├── architecture/   — ARCH_CONTRACT, API_CONVENTIONS, TECH_STACK, etc.
│   └── evaluation/     — eval_criteria.md, scoring rubrics
├── features/           — created empty at L4+; scaffold your first feature with govkit init
├── governance/backend/schemas/   — eval_criteria.schema.json
└── ci/github/ (or azure/)        — l3-quality-gate.yml + L4/L5 gates

UI shape (--type ui-react or --type ui-angular):

your-project/
├── CLAUDE.md (or .github/copilot-instructions.md, or AGENTS.md)
├── src/CLAUDE.md       — Claude only: nested UI layer rules under src/
│                         (Codex uses src/AGENTS.md + nested AGENTS.md per layer;
│                          Copilot uses .github/instructions/ with src-scoped applyTo: globs)
├── .claude/rules/ (or .github/instructions/)
│   └── repo-scope.md, test-first.md (L4+), spec-compliance.md (L4+)
├── .claude/skills/
│   └── ui-architecture-preflight/, ui-spec-planning/, ui-implementation-plan/, ui-adr-author/
├── docs/ui/
│   ├── architecture/   — MVVM_CONTRACT, ACCESSIBILITY_STANDARDS, react|angular subdirs
│   └── evaluation/     — eval_criteria.md, scoring rubrics
├── governance/ui/      — schemas, templates
└── ci/github/ (or azure/)  — l3-ui-quality-gate.yml + L4/L5 UI gates

Backend installs ship no UI artifacts; UI installs ship no backend artifacts. The CI dispatch is type-aware: backend types get l3-quality-gate.yml, UI types get l3-ui-quality-gate.yml. For fullstack monorepos, run one govkit apply per app subdirectory — see the monorepo pattern.

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. Keep governance contracts up to date

When you upgrade govkit, run govkit upgrade to refresh the files that govkit owns — architecture contracts, CI gate pipelines, plan templates — without touching the files your team owns.

pip install --upgrade govkit
govkit upgrade --target .

govkit distinguishes three categories of files:

Category Examples apply upgrade
Agent config CLAUDE.md, .claude/rules/, .agents/skills/ Always overwrite Always overwrite
Governed contracts docs/backend/architecture/, governance/backend/templates/, ci/github/ Write once (skip if present) Overwrite
Project artifacts features/starter_*/, your ADRs, filled-in feature files Write once (skip if present) Skip

After upgrading, review the diff and commit:

git diff
git add -p
git commit -m "chore: upgrade govkit governance contracts to vX.Y.Z"

Use --force to re-apply even when the version is already current — useful for resetting a contract file to govkit defaults after an accidental edit:

govkit upgrade --target . --force

7. Validate governance compliance

govkit validate --target .

Validation is level-aware. Level 3 is a no-op (Foundations ships agent rules + architecture docs only — there are no per-feature artifacts to check; the CI quality-gate is the L3 compliance surface). Level 4 runs the full 5-artifact governed contract check (existence + Gherkin structure + NFR completeness + tag coverage + eval_criteria.yaml schema + evaluation prediction thresholds). Level 5 adds LLM-specific NFR + DeepEval/Promptfoo checks. The level is auto-detected from .govkit or can be overridden:

govkit validate --level 4 --target .

Maturity Levels

govkit supports three operating levels in an additive ladder. Each level commits the team to a different way of working, not just a different artifact count. The boundary between L3 and L4 sits at the most meaningful place: whether the team adopts a features/ directory model and per-feature spec contracts.

Level Name What it ships What the team commits to
Level 3 Governed AI Delivery (Foundations) Agent rules, architecture contracts under docs/*/architecture/, /adr-author skill, lean CI gate (commit-format + import-linter + sonar/snyk). No features/ directory, no per-feature artifacts. "Our AI agents follow our architecture contracts." Lowest-friction entry; no project-structure change required.
Level 4 Spec-Driven Add-On Adds the features/<name>/ 5-artifact contract (acceptance.feature, nfrs.md, eval_criteria.yaml, plan.md, architecture_preflight.md), feature-coupled skills (/spec-planning, /architecture-preflight, /implementation-plan), test-first + spec-compliance rules (binding), governance CI jobs, and per-feature evaluation prediction (FIRST + 7 Virtues, average ≥ 4.0). "We adopt spec-first, test-first feature delivery on top of L3." govkit init becomes meaningful here.
Level 5 GenAI Operations LLM-specific NFR categories (latency, cost, fallback, safety), agent_topology.md for multi-agent features, deepeval/promptfoo/guardrails CI gates, LLM gateway/observability/multi-agent rules, LiteLLM routing, OpenLLMetry + Langfuse, RAGAS, NeMo Guardrails. "Our LLM features are governed (routing, evaluation, safety)." Builds on L4.

Start at Level 3 (default) if you want governed AI agents without restructuring your codebase. Move to Level 4 when your team is ready to commit to spec-first feature delivery. Move to Level 5 when shipping LLM-powered features that need governed model routing, evaluation, and safety.

The ladder is additive: L4 ⊃ L3, L5 ⊃ L4. Files installed at lower levels are not replaced by higher levels (with one exception: the agent's top-level entry point — CLAUDE.md / .github/copilot-instructions.md / AGENTS.md — is re-issued at each level so the agent sees the right operating mode).

Level 3 — Governed AI Delivery (Foundations)

Your AI agent operates aligned to your architecture contracts:

  • Reads docs/<area>/architecture/ (ARCH_CONTRACT, BOUNDARIES, TESTING, SECURITY_AUTH_PATTERNS, etc.) on every turn
  • Path-scoped rules trigger when editing files in each layer (api/, services/, ports/, adapters/, security/)
  • ADRs required for any standard extension, override, or boundary change — scaffolded with /adr-author
  • Test-first is recommended (the binding rule lives at L4)
  • CI quality-gate enforces commit-format, import-linter (architecture boundaries), SonarQube, Snyk
  • No features/ directory is created. govkit init errors at L3 with a pointer to --level 4.
  • govkit validate is a no-op (returns 0 with informational message).

Level 4 — Spec-Driven Add-On

Everything in Level 3, plus:

Every feature lives under features/<name>/ with five required artifacts:

  • Defined with Gherkin acceptance criteria tagged to NFR categories
  • Constrained with fully populated NFRs (no TBD entries permitted)
  • Governed by evaluation criteria validated against a JSON Schema
  • Planned through Architecture Preflight + Spec Planning + Implementation Plan skills
  • Bounded by hexagonal architecture contracts with FIRST + 7 Virtues prediction (average ≥ 4.0)
  • Enforced by governance CI jobs (artifact existence, eval-criteria schema, prediction thresholds, contract compatibility)

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.

Migrating from v0.6.x

The L3 / L4 maturity-model meaning changed in v0.7.0. If your .govkit marker says level: "3" (the v0.6 simpler 3-artifact model), run:

govkit upgrade --migrate-levels --target .

You'll be prompted to choose: migrate your existing 3-artifact features to the new L4 (with stub generation for the two new artifacts), or adopt new-L3 (no features/). See CHANGELOG.md for the full migration guide.

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 3
--type api, cli, ui-react, ui-angular api
--ci github, azure github

The --type choices are flat — one install configures one project shape. The legacy --ui cross-product was removed in v0.8. For fullstack monorepos see the monorepo pattern.

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.

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. govkit init requires Level 4 or higher — Level 3 (Foundations) ships agent rules and architecture contracts only and has no features/ directory model. Running govkit init at L3 errors with a pointer to govkit apply --level 4. At L4 the bundled starter has all 5 artifacts; at L5 the starter adds agent_topology.md for multi-agent features.

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.

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

Monorepo (Fullstack) Pattern

Each govkit apply configures one project shape. There is no --type fullstack. Teams that need both backend and UI in a single repo run govkit apply once per subdirectory:

govkit apply --agent claude-code --type api      --level 4 --ci github --target apps/api
govkit apply --agent claude-code --type ui-react --level 4 --ci github --target apps/web

Each subdir becomes a self-contained govkit install — separate .govkit marker, separate features/, separate CI gates. The three agents all support subpath governance natively:

  • Claude Code — recursive CLAUDE.md discovery picks up the right shape based on the open file's directory
  • Codex — directory-walk loader concatenates AGENTS.md from leaf to root
  • Copilot — applyTo: globs in each instructions file (one small post-install adjustment to prefix the app path so globs don't cross app boundaries)

For the complete setup — directory layout, CI workflow examples, the Copilot applyTo: prefix tweak, feature governance per app, upgrade flow, and gotchas — see docs/MONOREPO_PATTERN.md.

If your backend and UI live in separate repositories instead of subdirectories of a monorepo, see Multi-Repository Features below — different coordination problem.

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.

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

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.

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.

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)

Backend (GenAI Contracts — Level 5)

Backend (Practical Guides — Level 5)

UI (Shared)

React UI

Angular UI

Evaluation Reference

Standards (All Levels)

Schemas

UI Evaluation

Evaluation by Level

Level What's Evaluated Tools
L3 Architecture-boundary compliance + commit format + lint/security (codebase-wide; no per-feature artifacts) l3-quality-gate.yml (import-linter + sonar/snyk + commit-format)
L4 L3 + spec completeness, Gherkin structure, NFR coverage, FIRST + 7 Virtue scores, eval_criteria schema govkit validate + quality-gate.yml + eval-gate.yml
L5 L4 + LLM quality (DeepEval), adversarial safety (Promptfoo), retrieval quality (RAGAS), guardrails config, multi-agent topology govkit validate + deepeval-gate + promptfoo-gate + guardrails-check + multi-agent-gate

Getting Help

License

Copyright 2026 Accelerated Innovation

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

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

Resources

Video Demo (COMING SOON)

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

About

Governed AI Delivery (GovKit) is an open source toolkit for spec-driven, evaluation-aware AI-assisted software delivery.

Topics

open-source evaluation developer-tools governance codex software-delivery github-copilot ai-coding claude-code spec-driven-development

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Releases 3

v0.8.0 — flat project shape, L3↔L4 swap, Open Skills parity Latest
May 19, 2026
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