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GovZero Kit: A Development Covenant for Human-AI Collaboration

Project description

gzkit

GovZero Kit: A Development Covenant for Human-AI Collaboration

Version Python 3.13+ License: MIT Docs CI Docs Code Style: Ruff Package Manager: uv

Documentation Release Notes Roadmap

gzkit is cognitive infrastructure for extended human-AI collaboration—a protocol that preserves human intent across agent context boundaries, gives agents constraints to reason against, creates verification loops both parties trust, and reserves final judgment for humans.

Why gzkit?

Modern AI-assisted development faces a structural problem: agents are powerful but context-bound. They drift without constraints, forget across sessions, and can't distinguish between "should do" and "could do." Humans provide intent and judgment but can't scale execution.

gzkit bridges this gap by formalizing the collaboration:

Role Human Agent
Intent Originates Interprets, clarifies
Constraints Defines, enforces Operates within, flags violations
Exploration Guides Executes, surfaces options
Judgment Final authority Proposes, explains tradeoffs
Memory Long-term, cross-project Session-bound, needs scaffolding
Verification Attests Generates evidence

Three Concerns

gzkit spans three distinct but interrelated concerns:

Concern Purpose Primary Audience
Specification Invariants, constraints, acceptance criteria Agent (grounding)
Methodology Phases, workflows, checkpoints Process (structure)
Governance Authority, attestation, audit Human (oversight)

Specification is agent-native: explicit constraints, declarative intent, immutable canon.

Methodology is process-native: phases, gates, verification loops.

Governance is human-native: attestation rituals, authority boundaries, audit ceremonies.

All three are necessary. Specification without governance drifts. Governance without methodology is theater. Methodology without specification is arbitrary.

The Covenant

gzkit implements a development covenant—a binding agreement between human and agent:

  1. Human defines intent through canon, ADRs, and acceptance criteria
  2. Agent operates within constraints and flags potential violations
  3. Verification is mutual through tests, checks, and evidence
  4. Human attests completion after observing artifacts
  5. Artifacts survive sessions preserving intent across context boundaries

This is not "AI governance" in the compliance sense. It's a protocol for productive partnership.

Lineage

gzkit evolved from:

  • GitHub spec-kit — the constitute → specify → plan → implement → analyze phase model
  • GovZero — governance framework developed in AirlineOps through ~100 work items of iterative learning
  • Claude Code conventions — CLAUDE.md patterns for agent-native constraint specification

See docs/lineage.md for full heritage.

Five Gates

Work flows through five gates, adapted by lane (Lite or Heavy):

Gate Name Purpose
1 ADR Record intent and tradeoffs before implementation
2 TDD Verify correctness through automated tests
3 Docs Ensure documentation describes actual behavior
4 BDD Verify external contracts through acceptance tests
5 Human Human observes artifacts and attests completion

Lite lane (internal changes): Gates 1, 2

Heavy lane (external contracts): Gates 1, 2, 3, 4, 5

Installation

# Using uv (recommended)
uv add gzkit

# Or pip
pip install gzkit

Quick Start

# Initialize gzkit in a project
gz init

# Create a new ADR
gz plan feature --title "Feature description"

# Check gate status
gz status

# Run verification
gz check

Configuration

gzkit uses .gzkit.json for project configuration:

{
  "mode": "lite",
  "paths": {
    "canon": "docs/canon",
    "adrs": "docs/adr",
    "specs": "docs/specs"
  }
}

Documentation

  • Charter — The covenant itself
  • Lineage — Heritage from spec-kit and GovZero
  • Concepts — The three concerns explained
  • Genesis — Origin story and founding conversation

Philosophy

Governance is verification, not celebration.

gzkit treats governance as executable documentation. All state lives in Markdown, validated by a Python CLI. The framework is human-centric, auditable, and version-controlled.

Prompts are code. Constraints are first-class. Human attestation is the final gate.

License

MIT

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