Update roadmap

Author: codeaholicguy

web/content/roadmap/4-local-memory.md

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 ---
 title: Local Memory & Knowledge based
-status: planned
+status: in-progress
 order: 4
 ---
 

web/content/roadmap/5-skill-management.md

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+---
+title: Skill Management
+status: planned
+order: 5
+---
+
+Stop copy-pasting prompts and scripts between projects. Skill Management will provide a centralized registry to version, install, and share your custom AI capabilities (like specific refactoring workflows or testing patterns) across all your projects and teams with a single command.

web/content/roadmap/6-hooks.md

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+---
+title: Hooks
+status: planned
+order: 6
+---
+
+Streamline your workflow by automatically configuring agent-specific hooks, such as those for Claude Code or Cursor. This feature will enable you to define "chain-reactions"—like automatically triggering a `/code-review` command immediately after an implementation step is completed—ensuring quality checks happen without you needing to remember to run them manually.

web/content/roadmap/7-memory-management.md

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+---
+title: Memory Management
+status: planned
+order: 7
+---
+
+Keep your AI's knowledge base clean and relevant. We are building tools that allow you to easily browse, edit, merge, and delete stored rules and patterns, ensuring your agents don't get confused by outdated or conflicting information as your project evolves.

web/content/roadmap/8-memory-evaluation.md

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+---
+title: Memory Evaluation
+status: planned
+order: 8
+---
+
+Trust that your AI is remembering the right things. This feature will introduce a testing framework for your memory bank, allowing you to define "retrieval tests" that verify whether the correct coding standards and documentation are being prioritized when your AI agent searches for context.

web/content/roadmap/9-context-compaction.md

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+---
+title: Context Compaction
+status: planned
+order: 9
+---
+
+Reduce costs and stay within token limits without losing context. Context Compaction will implement intelligent summarization algorithms and "context packaging" to compress large documentation or conversation histories into concise, token-efficient formats that can be easily passed to new agents or threads.