Agent Skills: common-context-optimization

Priority: P1 (OPTIMIZATION)

Manage the Attention Budget. Treat context as a scarce resource.

1. Observation Masking (Noise Reduction)

Problem: Large tool outputs (logs, JSON lists) flood context and degrade reasoning. Solution: Replace raw output with semantic summaries after consumption.

1. Identify: outputs > 50 lines or > 1kb.
2. Extract: Read critical data points immediately.
3. Mask: Rewrite history to replace raw data with [Reference: <summary_of_findings>].
4. See: references/masking.md for patterns.

2. Context Compaction (State Preservation)

Problem: Long conversations drift from original intent. Solution: Recursive summarization that preserves State over Dialogue.

1. Trigger: Every 10 turns or 8k tokens.
2. Compact:
   • Keep: User Goal, Active Task, Current Errors, Key Decisions.
   • Drop: Chat chit-chat, intermediate tool calls, corrected assumptions.
3. Format: Update System Prompt or Memory File with compacted state.
4. See: references/compaction.md for algorithms.

3. KV-Cache Awareness (Latency)

Goal: Maximize pre-fill cache hits.

• Static Prefix: strict ordering: System -> Tools -> RAG -> User.
• Append-Only: Avoid inserting into the middle of history if possible.

References

• Observation Masking Patterns
• Compaction Algorithms

Anti-Patterns

• No raw tool dumps: Mask large outputs immediately after extracting data.
• No append-only growth: Compact every 10 turns to preserve intent over dialogue.
• No middle insertions: Append-only history maximizes KV cache hits.