Compression Strategy

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 and /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-context) to spawn continuation agent
2. Agent receives fresh context with saved state

Option C: Archive and Summarize

Best when:

• Context 40-60% full
• Some stale content mixed with active
• Not ready for full clear

Process:

1. Archive old decisions/errors to .claude/context-archive/
2. Summarize completed work in memory
3. Continue with leaner context

Option D: Delegate to Subagent

Best when:

• Parallel work possible
• Independent subtasks exist
• Context pressure moderate

Process:

1. Identify delegatable tasks
2. Spawn specialized agents via Task tool
3. Main context stays lean

Step 3 – Estimate Savings (estimate-savings)

For the recommended strategy, estimate:

The CCR row is per oversized tool output, not whole-context: a large Bash, Read, or Grep result is archived to a handle and replaced by a digest for future turns. Savings are content-type-dependent (logs compress hard, prose barely at all). See modules/reversible-compression.md.

Context Archive Location

Preserved context is saved to:

.claude/context-archive/pre-compact-YYYYMMDD-HHMMSS-SESSIONID.md

This is automatically created by the pre_compact_preserve hook before any /compact operation.

Integration Points

• PreCompact hook: Automatically preserves context before compression
• Tool output summarizer: Warns when tool outputs accumulate
• Context warning hook: Three-tier alerts at 40%/50%/80%

Specialized Modules

Load modules/log-debugging-hygiene.md when the bloat source is pasted log output (debug traces, CI failures, hook logs, JSONL). That module documents a three-tier filter-first workflow with benchmarked snippets and an honest framing of when compression is and is not warranted. On the committed intake_queue.jsonl fixture, tail -n 100 beats lossless compression by 25 percentage points; the module formalizes that asymmetry.

Load modules/reversible-compression.md when large tool outputs (code search, log dumps, file reads) are the bloat source. That module documents the CCR pattern: the tool_output_summarizer hook archives any oversized output to a content-addressed handle under .claude/context-archive/, and context_retrieve.py fetches the original (or a slice) on demand, so the original survives /clear without staying resident.

Example Usage

/compression-strategy

Output:

Context Analysis:
- Current usage: 52%
- Tool output: ~15KB (3 tool results)
- Stale content: ~40% (decisions from 8+ turns ago)

Recommendation: Option C - Archive + Summarize
- Archive old decisions to context-archive
- Keep active files and recent decisions
- Estimated savings: 25-35%

Commands:
1. Read .claude/context-archive/ to see what's preserved
2. Summarize completed work
3. Continue with leaner context

Exit Criteria

• Context analyzed: current usage and tool-output share estimated
• A single strategy recommended (A-D, or reversible compression) with a stated reason
• Savings estimated with the named risk from the Step 3 table
• For large tool outputs, the CCR handle and context_retrieve.py retrieval command are surfaced (not just a warning)
• Recommendation refused or downgraded when the bloat source is dense prose (compresses by roughly nothing)