Context Compression
技能 已验证 活跃This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.
To provide agents with effective methods for managing and reducing token usage in long-running sessions by intelligently compressing conversation history and evaluating the quality of compression.
功能
- Strategies for context compression (Anchored Iterative, Opaque, Regenerative)
- Guidance on optimizing tokens-per-task vs. tokens-per-request
- Detailed explanation of artifact trail preservation
- Methodologies for probe-based evaluation of compression quality
- Implementation steps for structured summaries and compression triggers
使用场景
- When agent sessions exceed context window limits
- Designing conversation summarization strategies
- Debugging agents that 'forget' previous information
- Optimizing token usage in long-running agent sessions
非目标
- Replacing prompt engineering entirely
- Handling context compression for raw code blocks without summarization
- Providing a generic text summarization tool outside of agent context engineering
工作流
- Identify need for context compression due to session length or token limits.
- Select an appropriate compression strategy (Anchored Iterative, Opaque, Regenerative).
- Implement the chosen strategy, focusing on structured summaries and appropriate triggers.
- Evaluate compression quality using the provided probe-based framework.
- Monitor re-fetching frequency as a key quality signal.
实践
- Context Engineering
- LLM Evaluation
- Agent Design Patterns
安装
请先添加 Marketplace
/plugin marketplace add muratcankoylan/Agent-Skills-for-Context-Engineering/plugin install Agent-Skills-for-Context-Engineering@context-engineering-marketplace质量评分
已验证类似扩展
Session Compression
91AI session compression techniques for managing multi-turn conversations efficiently through summarization, embedding-based retrieval, and intelligent context management.
Mcp Setup
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Deepinit
100Deep codebase initialization with hierarchical AGENTS.md documentation
Agent Worker Specialist
100Agent skill for worker-specialist - invoke with $agent-worker-specialist
Orchestrate
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Init
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