🤖 refactor: simplify hash skill rendering

[ammar-agent]

Summary

• Replace brittle inline hash-skill chunking with a rehype-based highlighter so markdown is parsed once and preserved.

Background

• A review flagged the previous UserMessageContent rendering as brittle because it split markdown into chunks, which can break lists/code blocks and required multiple render branches.

Implementation

• Add rehypeHighlightTerms to wrap matched #skill mentions in a styled span after markdown parsing and skip code/pre.
• Extend MarkdownCore/MarkdownRenderer with optional highlightTerms and simplify UserMessageContent to a single markdown render path.
• Add .skill-mention-badge styles to match command prefix styling.

Validation

• make static-check

Risks

• Low. Highlighting now operates at the AST level and explicitly skips code/pre/script/style nodes to avoid altering code blocks.

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Generated with mux • Model: openai:gpt-5.2-codex • Thinking: xhigh • Cost: $8.67

[chatgpt-codex-connector]

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[chatgpt-codex-connector]

Preserve original #skill text for display/highlight

Here the message is rewritten via formatHashSkillInvocationText and only hashSkillMentions metadata is stored. Because user-message display falls back to the stored message text when there’s no rawCommand, the chat history will show “Using skill …” instead of the original #skill tokens. That means the new highlight path never actually matches (no # remains) and users lose the exact input they typed. Consider preserving the original text (e.g., add a raw text field in metadata for hash-skill messages) or only rewrite the text sent to the model while keeping displayed content unchanged.

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