ADu2021/cgpt-cluster-guided-partial-tables-with-llm-genera
skills/skillxiv-v0.0.2-claude-opus-4.6/cgpt-cluster-guided-partial-tables-with-llm-genera/SKILL.md
Implement techniques from CGPT: Cluster-Guided Partial Tables with LLM-Generated Supervision for Table Retrieval. General-purpose embedding models have demonstrated strong performance in text retrieval but remain suboptimal for table retrieval, where highly structured content leads to semantic compression and query-table mismatch
$ install --global
skillsauthnpx skillsauth add ADu2021/skillXiv cgpt-cluster-guided-partial-tables-with-llm-generaInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- cgpt-cluster-guided-partial-tables-with-llm-genera
- title:
- CGPT: Cluster-Guided Partial Tables with LLM-Generated Supervision for Table Retrieval
- version:
- 0.0.2
- engine:
- skillxiv-v0.0.2-claude-opus-4.6
- license:
- MIT
- url:
- https://arxiv.org/abs/2601.15849
- keywords:
- ["model"]
- description:
- Implement techniques from CGPT: Cluster-Guided Partial Tables with LLM-Generated Supervision for Table Retrieval. General-purpose embedding models have demonstrated strong performance in text retrieval but remain suboptimal for table retrieval, where highly structured content leads to semantic compression and query-table mismatch
Overview
This skill implements concepts from the research paper [2601.15849].
When to Use
- When you need to implement techniques described in this paper
- When working on problems that this research addresses
- When you want to understand the core concepts and methodology
When NOT to Use
- This skill provides research-level insights; production implementations may require additional engineering
- Some concepts may require significant tuning for specific use cases
- Always evaluate applicability to your specific problem domain
Key Concepts
The paper addresses: General-purpose embedding models have demonstrated strong performance in text retrieval but remain suboptimal for table retrieval, where highly structured content leads to semantic compression and query-table mismatch. Recent LLM-based retrieval augm...
For detailed methodology, refer to the full paper.
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