Aegis
Enforces structured, evidence-driven development practices for AI coding agents, reducing rework and improving code review cycles by establishing baseline ex…
让AI编码更严谨、更可控的架构驱动开发方法包，一键安装，提升代码质量！An upgraded Superpowers-based Architecture-Driven Development (ADD) Method Pack for AI coding agents: baseline-first, evidence-driven workflows, TLREF/DIVE/QA discipline, and dual-track governance across hosts.
- Enforce baseline architecture requirements before agent code generation starts
- Implement peer review discipline via TLREF/DIVE/QA checkpoints in pipelines
- Track evidence and decisions for compliance-sensitive codebases
Enforces structured, evidence-driven development practices for AI coding agents, reducing rework and improving code review cycles by establishing baseline expectations and governance checkpoints.
Teams using AI agents for code generation who need reproducible, auditable development workflows with quality gates before production.
https://github.com/GanyuanRan/Aegis
By GanyuanRan
How to Get It
claude plugins install GanyuanRan/Aegis
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 enforce baseline architecture requirements before agent code generation starts
Trust Signals Auto-scanned
Community Pulse New
No community discussions found yet. This doesn't mean the tool isn't good — it may be new or serve a niche use case.
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 795 GitHub stars; 1 contributors; last commit 0d 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.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 795 GitHub stars; 1 contributors; last commit 0d ago; license MIT.