minimax-m3-long-context
CommunityMaster long-context retention for 1M-token MSAs
Software Engineering#compression#retention#context-management#machine-learning#minimax#long-context#msa
Authormadebyaris
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Large-context tasks (repositories, transcripts, multi-file designs) overwhelm memory if context isn't managed; this skill defines when to load, summarize, or drop evidence and how to plan retention per slice to stay coherent across iterations.
Core Features & Use Cases
- Plan-based loading: write a loader plan before reading files to guide evidence ingestion.
- Retention discipline: choose verbatim vs summary per chunk and apply iterative compression.
- End-to-end workflows: supports multi-file refactors, transcripts, and large design docs requiring sustained context.
Quick Start
Define a loader plan and start applying the retention and compression rules to manage long-context evidence.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: minimax-m3-long-context Download link: https://github.com/madebyaris/advance-minimax-m3-cursor-rules/archive/main.zip#minimax-m3-long-context Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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