compression-strategy

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Analyze current context and recommend compression strategies for bloated or quota-heavy sessions

AI & Automation 308 stars 27 forks Updated today MIT

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Skill Content

# Compression Strategy Analyze current context usage and recommend optimal compression strategies. ## When To Use - Context feels bloated or sluggish - Before major task phase transitions (plan complete, starting implementation) - Token quota burning faster than expected - After large tool output accumulations ## When NOT To Use - Context-optimization skill already handling the scenario - Simple queries with minimal context - Freshly cleared context ## Required TodoWrite Items 1. `compression-strategy:analyze-context` 2. `compression-strategy:recommend-strategy` 3. `compression-strategy:estimate-savings` ## Step 1 – Analyze Context (`analyze-context`) Run `/context` to check current usage. Then estimate: 1. **Tool output accumulation**: How much context is from tool results vs. conversation? 2. **Stale content age**: How many turns since critical decisions were made? 3. **Active files**: Which files are still relevant vs. historical? ## Step 2 – Recommend Strategy (`recommend-strategy`) Based on analysis, recommend one of: ### Option A: `/clear` + `/catchup` Best when: - Task phase complete (planning done, implementation starting) - Context > 60% full - Most content is stale Process: 1. Save critical state to `.claude/session-state.md` 2. Run `/clear` 3. Run `/catchup` to reload active files ### Option B: Spawn Continuation Agent Best when: - Context > 80% full - Work in progress, can't stop - Delegatable tasks remain Process: 1. Run `Skill(conserve:clear-co...

Details

Author
athola
Repository
athola/claude-night-market
Created
6 months ago
Last Updated
today
Language
Python
License
MIT

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