菜单
Use when compressing agent context, implementing conversation summarization, reducing token usage in long sessions, or asking about "context compression", "conversation history", "token optimization", "context limits", "summarization strategies"
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | context-compression |
| description | Use when compressing agent context, implementing conversation summarization, reducing token usage in long sessions, or asking about "context compression", "conversation history", "token optimization", "context limits", "summarization strategies" |
| version | 1.0.0 |
When agent sessions generate millions of tokens, compression becomes mandatory. Optimize for tokens-per-task (total tokens to complete a task), not tokens-per-request.
## Session Intent
[What the user is trying to accomplish]
## Files Modified
- auth.controller.ts: Fixed JWT token generation
- config/redis.ts: Updated connection pooling
## Decisions Made
- Using Redis connection pool instead of per-request
- Retry logic with exponential backoff
## Current State
- 14 tests passing, 2 failing
- Remaining: mock setup for session service tests
## Next Steps
1. Fix remaining test failures
2. Run full test suite
3. Update documentation
| Strategy | Trigger | Trade-off |
|---|---|---|
| Fixed threshold | 70-80% context | Simple but may compress early |
| Sliding window | Last N turns + summary | Predictable size |
| Importance-based | Low-relevance first | Complex but preserves signal |
| Task-boundary | At task completions | Clean but unpredictable |
File tracking is the weakest dimension (2.2-2.5/5.0 in evaluations). Coding agents need:
Solution: Separate artifact index or explicit file-state tracking.
Test compression quality with probes:
| Probe Type | Tests | Example |
|---|---|---|
| Recall | Factual retention | "What was the original error?" |
| Artifact | File tracking | "Which files have we modified?" |
| Continuation | Task planning | "What should we do next?" |
| Decision | Reasoning chain | "What did we decide about Redis?" |
| Method | Compression | Quality | Trade-off |
|---|---|---|---|
| Anchored Iterative | 98.6% | 3.70 | Best quality |
| Regenerative | 98.7% | 3.44 | Moderate |
| Opaque | 99.3% | 3.35 | Best compression |
The 0.7% extra tokens buys 0.35 quality points—worth it when re-fetching costs matter.
Use when evaluating agent performance, building test frameworks, measuring quality, or asking about "agent evaluation", "LLM-as-judge", "agent testing", "quality metrics", "evaluation rubrics", "agent benchmarks"
Use when "codex", "use gpt", "gpt-5", "openai codex", "let openai", "full-auto", "autonomous code generation"
Use when diagnosing agent failures, debugging lost-in-middle issues, understanding context poisoning, or asking about "context degradation", "lost in middle", "context poisoning", "attention patterns", "context clash", "agent performance drops"
Use when optimizing agent context, reducing token costs, implementing KV-cache optimization, or asking about "context optimization", "token reduction", "context limits", "observation masking", "context budgeting", "context partitioning"
Use when "CrewAI", "multi-agent systems", "agent orchestration", "AI crews", or asking about "autonomous agents", "agent collaboration", "role-based agents", "agent workflows", "AI team coordination"
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies