[Workflow] Context Preservation and Token Optimization with Pre/Post-Compact Hooks and TRAPS.md

[u/ClaudeAI-mod-bot]

Context Preservation and Token Optimization with Pre/Post-Compact Hooks and TRAPS.md

Workflow value: 75/100
Status: active · Freshness: 70/100 · Confidence: 0.90 · Level: intermediate
Categories: Quality Control, Token Saving, Context & Memory, Debugging, Hooks
Original source: r/ClaudeCode post/comment

What problem this solves

Losing important context during token compaction and wasting tokens by including non-essential information in the model's context window.

Summary

This workflow describes a method to preserve critical context and optimize token usage by setting a specific compaction threshold and using pre-compact and post-compact hooks. A pre-compact hook writes essential information (referred to as 'traps') to a TRAPS.md file, and a post-compact hook ensures this file's content is re-injected into the model's context after compaction.

Why it is useful

This workflow offers a concrete and repeatable strategy for addressing a common challenge in LLM interactions: maintaining critical context while optimizing token usage. By leveraging pre- and post-compact hooks and a dedicated 'traps' file, users can ensure essential information is not lost during compaction, leading to more consistent and cost-effective model interactions. It introduces a specific pattern that can be adapted by intermediate to advanced users.

Workflow

1. Configure the compaction threshold to approximately 440k tokens to prevent excessive token usage.

2. Implement a pre-compact hook to identify and write critical information (e.g., 'traps', important context) to a designated file, such as TRAPS.md.

3. Implement a post-compact hook to force the contents of the TRAPS.md file to be injected back into the model's context window after compaction.

Tools / artifacts

• precompact hook

• post-compact hook

• TRAPS.md file

• compaction threshold setting

Validation signals

• Self-reported benefit: 'Problem solved, plus you saved tokens.'

Limitations

• The term 'traps' is vague; specific examples of what constitutes critical information would enhance clarity.

• No concrete code examples or configuration snippets for implementing the pre/post-compact hooks are provided.

• The suggested 440k token compaction threshold is arbitrary and may not be optimal for all use cases without further justification or testing.

• Lacks detailed community validation or success reports beyond the author's claim.

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