> common-context-optimization

Techniques to maximize context window efficiency, reduce latency, and prevent ''lost in middle'' issues through strategic masking and compaction. (triggers: *.log, chat-history.json, reduce tokens, optimize context, summarize history, clear output)

fetch
$curl "https://skillshub.wtf/HoangNguyen0403/agent-skills-standard/common-context-optimization?format=md"
SKILL.mdcommon-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

🚫 Anti-Patterns

  • Do NOT use standard patterns if specific project rules exist.
  • Do NOT ignore error handling or edge cases.

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first seenMar 17, 2026
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HoangNguyen0403/agent-skills-standard
by HoangNguyen0403
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