10 virtual experts. Zero approval gates. Zero meetings. Ship production software in minutes — AI makes every design decision and documents why.
Every build follows a structured loop designed so AI makes confident decisions — not guesses. Each phase has a defined output. Each output feeds the next.
Reads your project, conventions, stack. Self-answers every clarifying question it would ask you. Documents assumptions in a decisions log.
Evaluates 2-3 implementation approaches internally. Picks the best using a decision principle (simplest, fastest to value, easiest to change). Writes a spec.
Creates bite-sized TDD tasks with exact code. Every task: write failing test, verify fail, implement, verify pass, commit. No placeholders.
Dispatches subagents per task with two-stage code review (spec compliance first, then code quality). Production-grade output with tests.
10 virtual expert perspectives score improvements: Impact x (6 - Effort). Security, Performance, UX, DevOps, QA, and 5 more. Only scores >= 15 trigger iteration.
High-scoring improvements trigger automatic redesign loops (max 3 rounds). Low-scoring improvements go to next.md. Stops when returns diminish.
No senior engineer reviews your code from only one angle. Neither does rapidbuildr. Ten domain experts weigh in before anything ships.
Evaluates flows, friction points, and usability across all skill levels.
Validates architecture, data models, API contracts, and scalability.
Hunts attack surfaces — injection, auth bypasses, data exposure, supply chain.
Reviews deployability, observability, CI/CD compatibility, and ops burden.
Aligns technical choices with business value, market fit, and ROI.
Probes edge cases, error paths, and defines testability requirements.
Ensures WCAG compliance, keyboard navigation, and screen reader support.
Identifies bottlenecks, unnecessary allocations, and latency sources.
Applies industry-specific knowledge — regulations, conventions, and norms.
Represents the actual human using the product. Cuts through internal assumptions.
Impact and Effort are rated 1 to 5. A score of 15 or higher triggers an iteration cycle. Lower scores are documented as known tradeoffs but do not block shipping. This keeps the build loop focused on changes that materially move the needle.
Choose based on how well-defined your task is. Both modes are fully autonomous — the difference is how improvement decisions are made.
The expert panel drives all decisions. Each of the 10 experts evaluates approaches, scores improvements, and the highest-scoring path is selected and implemented without asking you.
Inspired by Andrej Karpathy's approach to systematic improvement. Defines a measurable success metric upfront, then autonomously researches, iterates, and optimizes until the metric is maximized.
Most AI coding tools make you the decision-maker and the bottleneck. rapidbuildr removes you from the loop entirely — by design.
| Capability | Manual AI Coding | Superpowers | rapidbuildr |
|---|---|---|---|
| Human approval gates | Every step | Review checkpoints | None |
| Structured build phases | No | Yes | Yes |
| Multi-expert critique | No | No | 10 experts, every build |
| Autonomous design decisions | No — you decide everything | Partial — gates remain | Fully autonomous |
| Scored improvement loop | No | No | Impact x (6 - Effort) |
| Metric-driven research mode | No | No | Autoresearch mode |
| Decision audit trail | No | Manual notes | Auto-generated, every build |
| Time to working output | Hours (with iteration) | Hours (with review cycles) | Minutes |
rapidbuildr is a Claude Code skill. Install it globally and it is available in every project from the first prompt.
You need Claude Code installed and authenticated with your Anthropic account.
Clone the repository into your Claude skills directory.
Open any project in Claude Code and invoke the skill directly.
The skill handles the rest. No follow-up questions. No approval needed.