Context Compression
Skill Verified ActiveThis 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.
Features
- 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
Use Cases
- 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
Non-Goals
- Replacing prompt engineering entirely
- Handling context compression for raw code blocks without summarization
- Providing a generic text summarization tool outside of agent context engineering
Workflow
- 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.
Practices
- Context Engineering
- LLM Evaluation
- Agent Design Patterns
Installation
First, add the marketplace
/plugin marketplace add muratcankoylan/Agent-Skills-for-Context-Engineering/plugin install Agent-Skills-for-Context-Engineering@context-engineering-marketplaceQuality Score
VerifiedTrust Signals
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