disciplined-agentic-engineering
Structures agentic code generation around acceptance tests before implementation, reducing rework cycles and ensuring deliverables meet requirements before e…
Acceptance Test Driven Development for Claude Code — inspired by Uncle Bob's approach from empire-2025
- Generate acceptance tests before writing production code to clarify requirements
- Automate verification that Claude-written code meets business acceptance criteria
- Define test scenarios upfront to guide AI code generation toward correct behavior
Structures agentic code generation around acceptance tests before implementation, reducing rework cycles and ensuring deliverables meet requirements before engineering effort is invested.
Development teams using Claude for code generation who need predictable, requirement-driven outputs with measurable acceptance criteria.
https://github.com/swingerman/disciplined-agentic-engineerin...
By swingerman
How to Get It
claude plugins install swingerman/disciplined-agentic-engineering
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me generate acceptance tests before writing production code to clarify requirements
Trust Signals Auto-scanned
Community Pulse Growing
Discussed on Reddit
- I built a strict engineering discipline extension for the pi coding agent — Reddit · 8 pts
- Why AI coding agents spiral — and how I fixed it with engineering discipline — Reddit · 1 pts
- Adapting Big Tech Development disciplines with Draft - Agentic Engineering Disci — Reddit · 1 pts
3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 98 GitHub stars; contributors unknown; last commit 1d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
- Single maintainer. Consider the risk if this person stops maintaining the project.
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Evaluation
Scored from trust signals (evidence-eval-v1): 98 GitHub stars; contributors unknown; last commit 1d ago; license MIT.