| name | swe-pruner-self-adaptive-context-pruning-for-codin |
| title | SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2601.16746 |
| keywords | ["agent"] |
| description | Implement techniques from SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents. LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts, which incur high API costs and latency |
Overview
This skill implements concepts from the research paper [2601.16746].
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: LLM agents have demonstrated remarkable capabilities in software development, but their performance is hampered by long interaction contexts, which incur high API costs and latency. While various context compression approaches such as LongLLMLingua have emerged to tackle this challenge, they typical...
For detailed methodology and implementation details, refer to the full paper.
