AUTO-BUILDER
From idea to verified software.
An autonomous engineering system for Claude Code.
AUTO-BUILDER transforms high-level project goals into structured engineering workflows. It discovers requirements, researches solutions, designs architecture, coordinates specialist agents, implements features, verifies results, reviews security and iterates toward completion.
Don't just generate code. Engineer the complete solution.
The problem with ordinary AI coding workflows
AI coding tools have made implementation faster. But building complete software involves much more than writing code.
Developers still need to:
- Translate vague ideas into clear requirements.
- Research technologies and evaluate alternatives.
- Design architecture before implementation.
- Break complex work into manageable tasks.
- Coordinate frontend, backend and AI development.
- Identify bugs and prevent regressions.
- Review security-sensitive code.
- Maintain project context across long sessions.
- Verify that the finished product actually meets its requirements.
Without a structured engineering process, an AI agent can start coding too early, miss important requirements, overlook edge cases or declare completion after a successful build.
AUTO-BUILDER addresses this coordination and verification problem.
It gives Claude Code a structured engineering process that adapts to the task instead of treating every request as a code-generation exercise.
What is AUTO-BUILDER?
AUTO-BUILDER is an open-source Claude Code skill supported by nine specialist subagents, reusable engineering playbooks, project-state templates and an evidence-driven quality protocol.
You provide a goal:
/auto-builder Build an AI research platform where users
can upload research papers, ask grounded questions,
explore citations and organize their findings.
AUTO-BUILDER determines the appropriate workflow, discovers requirements, plans the work, delegates where useful, implements the solution and verifies the result.
You remain in control of consequential decisions while routine engineering work can proceed autonomously.
The core idea
| Traditional AI coding | AUTO-BUILDER |
|---|
| Start with code | Start with the problem |
| Follow the initial prompt | Discover and refine requirements |
| Make architecture decisions ad hoc | Evaluate and document architecture |
| Handle everything in one conversation | Delegate bounded specialist tasks |
| Run tests near the end | Verify at every milestone |
| Self-review the implementation | Use independent review workflows |
| Treat security as an optional extra | Apply a dedicated security gate |
| Lose context in long sessions | Maintain persistent project state |
| Declare completion after a build | Report against verified acceptance criteria |
How it works
AUTO-BUILDER uses an adaptive, milestone-based engineering lifecycle.
flowchart TD
A["Your project goal"] --> B["Intelligent Task Router"]
B --> C["Discovery & Requirements"]
C --> D["Research & Architecture"]
D --> E["Milestone Planning"]
E --> F["Specialist Agent Execution"]
F --> G["Testing & Verification"]
G --> H["Independent Review"]
H --> I{"Acceptance criteria verified?"}
I -->|"No: fixable"| F
I -->|"Blocker or retry limit"| J["Report blocker and decision needed"]
I -->|"Yes"| K["Delivery & Evidence Report"]
K --> L["Learning & Recovery"]
1. Intelligent task routing
Not every task requires a complete project lifecycle.
AUTO-BUILDER classifies requests across 12 categories:
- New project.
- Feature implementation.
- Bug fixing.
- Investigation.
- Refactoring.
- Performance optimization.
- Security assessment.
- Code review.
- Research.
- Testing.
- Documentation.
- Release.
A trivial change can take a lightweight route. A complex application receives a more comprehensive engineering workflow.
2. Product discovery
Before implementation, AUTO-BUILDER identifies:
- Target users.
- Core problems.
- Functional requirements.
- Non-functional requirements.
- User journeys.
- Scope boundaries.
- Acceptance criteria.
- Assumptions and risks.
Its product interrogation workflow helps uncover missing requirements before they become expensive implementation mistakes.
3. Architecture and research
The system researches relevant technologies, evaluates implementation options and develops an architecture.
For important decisions, the architecture arena can compare competing approaches and document trade-offs before implementation begins.
4. Specialist agent execution
AUTO-BUILDER delegates bounded responsibilities to specialist subagents.
Tasks are organized around dependencies, ownership and integration requirements. Parallel execution is used when work is genuinely independent.
5. Milestone-based implementation
Instead of treating an entire project as one large coding task, AUTO-BUILDER divides implementation into verifiable milestones.
Each milestone has:
- Defined tasks.
- Owners.
- Dependencies.
- Acceptance criteria.
- Verification commands.
- A completion gate.
6. Independent verification
The implementation is not considered complete simply because the build succeeds.
The quality protocol includes:
- Relevant test execution.
- Regression checks.
- User-journey and API verification.
- Independent review.
- Evidence recording.
- Bounded failure recovery.
7. Security review
Projects involving authentication, user data, payments, external APIs, file uploads, secrets, integrations or AI tools receive a dedicated security review.
Security findings are classified by severity, with explicit rules for blocking, fixing and accepting residual risks.
8. Delivery and recovery
AUTO-BUILDER maintains project state and records decisions, progress, test results and unresolved issues.
If a session is interrupted, its recovery workflow compares recorded progress against the actual repository state before continuing.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
