Can it detect broken development commands?

Yes, the skill extracts command references (like npm scripts) and validates them against your package.json to ensure the AI is using the correct workflows.

How does this skill improve AI performance?

It applies research-backed patterns like positive reinforcement, strong constraint language, and strategic content placement to ensure the AI follows instructions more reliably.

What are CLAUDE.md and AGENTS.md files?

These are project memory files that provide AI assistants with critical context, coding standards, and project architecture to ensure they generate accurate and relevant code.

Does this skill work with other AI tools?

Yes, it is designed for cross-platform compatibility, supporting memory files for Claude Code, OpenCode, Codex, and other AI-driven development tools.

What is the 'lost in the middle' problem?

LLMs tend to recall information at the beginning and end of a document better than the middle. This skill flags critical rules buried in the middle and suggests better placement.

Claude Memory Enhancer

Name: Claude Memory Enhancer
Author: avifenesh

byavifenesh

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311

Productivity & Workflow

Optimizes project memory files like CLAUDE.md and AGENTS.md to maximize AI instruction-following and context retention.

About

The Claude Memory Enhancer skill analyzes and audits your project's AI memory files to ensure your AI assistants operate with peak efficiency. By applying advanced prompt engineering principles—such as prioritizing positive instructions over negative ones and mitigating 'lost in the middle' retrieval issues—it transforms basic documentation into a high-performance roadmap. Whether you are using Claude Code, OpenCode, or Codex, this skill validates your structural integrity, cross-references your filesystem, and calculates token metrics to prevent context bloat.

Key Features

Cross-platform compatibility checks for multi-agent environments
Content positioning optimization to prevent LLM memory decay
Structural auditing for critical rules and architecture sections
Instruction effectiveness analysis using positive language and strong constraints
Automated validation of file references and package.json commands
311 GitHub stars

Use Cases

Standardizing project instructions for better AI coding reliability
Auditing oversized CLAUDE.md files to reduce token usage and cost
Synchronizing documentation between README.md and AI-specific memory files

About

Key Features

Cross-platform compatibility checks for multi-agent environments
Content positioning optimization to prevent LLM memory decay
Structural auditing for critical rules and architecture sections
Instruction effectiveness analysis using positive language and strong constraints
Automated validation of file references and package.json commands
311 GitHub stars

Use Cases

Standardizing project instructions for better AI coding reliability
Auditing oversized CLAUDE.md files to reduce token usage and cost
Synchronizing documentation between README.md and AI-specific memory files