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.opencode/skills/context-engineering/references/context-degradation.md

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+# Context Degradation Patterns
+
+Predictable degradation as context grows. Not binary - a continuum.
+
+## Degradation Patterns
+
+| Pattern | Cause | Detection |
+|---------|-------|-----------|
+| **Lost-in-Middle** | U-shaped attention | Critical info recall drops 10-40% |
+| **Context Poisoning** | Errors compound via reference | Persistent hallucinations despite correction |
+| **Context Distraction** | Irrelevant info overwhelms | Single distractor degrades performance |
+| **Context Confusion** | Multiple tasks mix | Wrong tool calls, mixed requirements |
+| **Context Clash** | Contradictory info | Conflicting outputs, inconsistent reasoning |
+
+## Lost-in-Middle Phenomenon
+
+- Information in middle gets 10-40% lower recall
+- Models allocate massive attention to first token (BOS sink)
+- As context grows, middle tokens fail to get sufficient attention
+- **Mitigation**: Place critical info at beginning/end
+
+```markdown
+[CURRENT TASK]              # Beginning - high attention
+- Critical requirements
+
+[DETAILED CONTEXT]          # Middle - lower attention
+- Supporting details
+
+[KEY FINDINGS]              # End - high attention
+- Important conclusions
+```
+
+## Context Poisoning
+
+**Entry points**:
+1. Tool outputs with errors/unexpected formats
+2. Retrieved docs with incorrect/outdated info
+3. Model-generated summaries with hallucinations
+
+**Detection symptoms**:
+- Degraded quality on previously successful tasks
+- Tool misalignment (wrong tools/parameters)
+- Persistent hallucinations
+
+**Recovery**:
+- Truncate to before poisoning point
+- Explicit note + re-evaluation request
+- Restart with clean context, preserve only verified info
+
+## Model Degradation Thresholds
+
+| Model | Degradation Onset | Severe Degradation |
+|-------|-------------------|-------------------|
+| GPT-5.2 | ~64K tokens | ~200K tokens |
+| Claude Opus 4.5 | ~100K tokens | ~180K tokens |
+| Claude Sonnet 4.5 | ~80K tokens | ~150K tokens |
+| Gemini 3 Pro | ~500K tokens | ~800K tokens |
+
+## Four-Bucket Mitigation
+
+1. **Write**: Save externally (scratchpads, files)
+2. **Select**: Pull only relevant (retrieval, filtering)
+3. **Compress**: Reduce tokens (summarization)
+4. **Isolate**: Split across sub-agents (partitioning)
+
+## Detection Heuristics
+
+```python
+def calculate_health(utilization, degradation_risk, poisoning_risk):
+    """Health score: 1.0 = healthy, 0.0 = critical"""
+    score = 1.0
+    score -= utilization * 0.5 if utilization > 0.7 else 0
+    score -= degradation_risk * 0.3
+    score -= poisoning_risk * 0.2
+    return max(0, score)
+
+# Thresholds: healthy >0.8, warning >0.6, degraded >0.4, critical <=0.4
+```
+
+## Guidelines
+
+1. Monitor context length vs performance correlation
+2. Place critical info at beginning/end
+3. Implement compaction before degradation
+4. Validate retrieved docs before adding
+5. Use versioning to prevent outdated clash
+6. Segment tasks to prevent confusion
+7. Design for graceful degradation
+
+## Related Topics
+
+- [Context Optimization](./context-optimization.md) - Mitigation techniques
+- [Multi-Agent Patterns](./multi-agent-patterns.md) - Isolation strategies