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| # Spec: tmux Agent Panes | |
| > Fork Claude agents into observable tmux panes | |
| ## Overview | |
| This skill enables Claude to spawn parallel agents, each working in their own tmux pane. The user can observe all agents working simultaneously in a single terminal window. | |
| ``` | |
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| # Claude Code Skills Reference | |
| **Version**: 2.5 | |
| **Claude Code Version**: 2.1.7 (fully tested) | |
| **Date**: 2026-01-15 | |
| **Methodology**: Systematic testing of 167 skill configurations + diagnostic introspection | |
| --- | |
| ## Executive Summary |
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| # Analysis of "Verbalized Sampling: How to Mitigate Mode Collapse and Unlock LLM Diversity" | |
| ## Response 1 (Probability: 0.23) | |
| ### 1. Core Claim Contradicting Common Assumptions (Section 3) | |
| The paper argues mode collapse is fundamentally a **data-level problem** rather than an algorithmic one. Existing work attributed mode collapse to inadequate reward models or majority-favoring optimization. The authors prove typicality bias (Ξ± = 0.57Β±0.07, p<10^-14 in HELPSTEER dataset) exists in preference data itself, meaning "even with a perfect reward model and optimization process, inherent bias within preference datasets may still drive mode collapse." | |
| This matters because it implies you can't fix mode collapse by improving RLHF algorithms alone - the preference collection process itself needs reconsideration. | |
| ### 2. Magnitude Finding (Section 5, Figure 4) |
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| # π¨ Complete Visual UI Development Guide for Claude Code | |
| ## π The Game-Changing Discovery | |
| **Claude Code can SEE screenshots!** This completely transforms UI development from blind coding to visual design with real-time feedback. | |
| ### The Transform | |
| - **Old Way**: Write code β Hope it looks good β Fix bugs when reported | |
| - **New Way**: Build β **SEE** β Judge β Refine β Repeat until beautiful |