A project template that turns your repository into stable infrastructure for AI-assisted development.
attractor-guided-engineering-template is easy to set up with strong trust signals. Check agent compatibility and use-case fit before adding it to your workflow.
gh repo clone entropy-cloud/attractor-guided-engineering-templateOpen or clone the template repository.
Move the relevant AGENTS.md content into your project root.
Update project-specific commands, environment rules, and sensitive areas.
Repository setup guidance
Strong trust signals; still review the README and permissions before production use.
Last commit was about 99 days ago.
18 GitHub stars indicate community interest.
0 open issues signal maintenance load.
Codebase intelligence layer for AI coding agents: code health, git analytics, dead code detection, and architectural decisions via MCP.
Local code intelligence MCP server and CLI for AI coding agents, providing semantic search and call graph analysis.
Deep code indexing MCP server for AI agents with hybrid FTS5 + embedding search, call graphs, and multi-repo workspaces.
This template helps you structure your code repository so that both humans and AI agents can work together efficiently. It uses a set of durable documentation files as a 'stable attractor' that the project always returns to, preventing drift and confusion across multiple sessions and contributors.
The Attractor-Guided Engineering (AGE) Template is a lightweight project scaffold designed for AI-assisted product development. It is intended for ordinary business applications such as admin systems, portals, workflow apps, dashboards, internal tools, and CRUD-heavy domain products. The template does not include generated product code; instead, it provides a durable structure for humans and AI to share requirements, owner-doc baselines, plans, verification, and project memory without heavyweight process overhead. AGE starts from the question: 'What should this repository keep converging toward as humans and AI change it over time?' The answer is a small set of durable owner files in the docs/ directory: context, backlog, requirements, design, and architecture. These files form the attractor that the project should keep returning to during fast AI-assisted iteration. Plans, tests, audits, logs, and bug notes are engineering harnesses that help prove a change moved the repo toward the attractor. AGE is not spec-driven development, nor is it a skill library. It emphasizes routing reusable skills through project-specific documentation rather than relying on a large library of skills without context. The template includes an AGENTS.md file for AI agent configuration.
A project template that turns your repository into stable infrastructure for AI-assisted development.
Developers building AI-assisted applications who want a structured project template, Tech leads managing teams that use AI coding agents, Product owners who need to maintain consistent project documentation
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
Template guidance should be adapted to the repository instead of applied mechanically. Agent instruction files can shape automation behavior; review them for overbroad permissions before use.
Similar or complementary options to evaluate include repowise, codanna, srclight.
Aug 20, 2026
MIT license detected.
2 security/trust notes recorded.
Setup difficulty is 2/5.
18
Stars
3
Forks
0
Issues
MIT
License