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Software-Engineer-AI-Agent-Atlas

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Multi-Agent Platforms & Frameworks
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Basic Information

This repository provides ATLAS, an AI Software Engineer Agent designed to collaborate with developers on real projects. It is a template and working environment that asks users to place their codebases into a REPOS folder so ATLAS can learn project structure and update PROJECT_STRUCTURE.md. The README outlines session initialization prompts to get ATLAS into the right context and describes persistent memory files and logs to store knowledge across sessions. Key files and directories include CLAUDE.md, SELF/, REPOS/, WORKING_LOG/, MEMORY/, FRESH_COMPACT_MEMORY.md, and IMPORTANT_NOTES.md. The repo is intended as an engineering partner framework that retains institutional knowledge, manages context limits, and adapts to project workflows. Users are instructed to delete the README after reading because ATLAS will remember the collaboration procedure.

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App URL
https://github.com/syahiidkamil/Software-Engineer-AI-Agent-Atlas
Github Repository
https://github.com/syahiidkamil/Software-Engineer-AI-Agent-Atlas/blob/main/README.md

App Details

Features
Structured workspace with a REPOS folder for user projects and automated project learning that updates PROJECT_STRUCTURE.md. Persistent memory system including MEMORY/ for long-term knowledge and FRESH_COMPACT_MEMORY.md for concise session summaries. Session initialization guidance and recommended starter prompts to align ATLAS with development beliefs and role. Context management tools and practices such as instructing ATLAS to summarize and store compact memory, and use of /clear to reset conversations. Daily working logs stored under WORKING_LOG/YYYY/MM-mon to maintain continuity. Special files for priorities like IMPORTANT_NOTES.md and a SELF/ directory for ATLAS"s idAgent and operating instructions. Emphasis on customizable architecture and developer workflows, plus high-level principles like KISS, YAGNI, and DRY.
Use Cases
ATLAS streamlines onboarding and ongoing engineering work by learning repository layouts and keeping an up-to-date PROJECT_STRUCTURE.md so developers spend less time re-explaining projects. The memory and compact summary files help manage conversation context limits and restore session state efficiently. Daily logs provide a chronological working history and support knowledge continuity across days. IMPORTANT_NOTES.md centralizes critical constraints and priorities so the agent follows high-value instructions. Guidance on session initialization encourages predictable, professional interactions and principle-driven decisions. System customization options let teams adapt the template to their workflows. Overall, it functions as a persistent technical collaborator that preserves institutional knowledge and helps maintain momentum on software projects.

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