Origin: This skill was extracted from Claude Code's internal implementation and rules. Claude Code openly exposes its safety mechanisms (hooks, system prompts, skill definitions) in the ~/.claude/ directory. The tiered compression strategy, layer classification system, and memory file structure were reverse-engineered from Claude Code's PreCompact/PostCompact hooks and session memory handling, then adapted for OpenClaw's multi-channel environment.

Context Compression

Why Tiered Compression Is Needed

A one-size-fits-all approach to dropping context either leaves the agent without memory or burns through context too quickly. The core principle of tiered strategy is: different information has different lifecycles. A user's statement "I prefer concise responses" is worth remembering forever; but a resolved error message from three days ago has no value today.

Before Compression: Identify the Scenario

Before starting compression, determine which conversation scenario you're in, as retention strategies differ:

Three-Layer Compression Strategy

Layer 1: Must Preserve (Keep Original)

These contents remain in original or near-original form after compression:

• User's explicitly stated goals, requirements, deadlines
• Incomplete tasks (in-progress, interrupted)
• Confirmed important decisions ("We decided to go with Plan B")
• User-expressed explicit preferences ("I don't like X", "Always use Y format going forward")
• Key credentials or config (accounts, paths, special settings)

Layer 2: Compress to Summary (Extract and Keep)

Keep conclusions, discard process:

• Completed tasks → One-sentence conclusion ("Completed X, result was Y")
• Long explanations → Core point in 1-2 sentences
• Tool call outputs → Keep only final results, discard intermediate steps
• Repeated topics → Merge into one record

Layer 3: Discard Directly

• Small talk, thanks, acknowledgment messages ("ok", "thanks", "got it")
• Rejected or obsolete proposals
• Multiple attempts at the same question (keep only the final effective one)
• Pure transitional content ("let me think", "hold on")
• Resolved error messages that won't be needed again

Compression Execution Steps

Step 1: Scan All Conversation Identify all Layer 1 content, make a checklist — this cannot be discarded.

Step 2: Process Layer 2 For each conversation segment, judge: Is there a conclusion worth keeping? If yes, distill into one sentence.

Step 3: Generate Compressed Summary In chronological order, combine Layer 1 content + Layer 2 extractions into a compact context summary, typically no more than 600 characters.

Step 4: Update Memory File Write high-persistence-value information (user preferences, long-term goals, important decisions) to the memory file. See memory-template.md for format.

Step 5: Inform the User Briefly explain what was compressed and what key information was preserved, so the user knows the context has been updated.

OpenClaw Multi-Channel Supplementary Rules

OpenClaw runs across multiple messaging platforms, pay extra attention:

Group chat scenarios: Other members' messages default to Layer 3 (discard) unless the user explicitly responds to or quotes that message.

Cross-day conversations: Judge by topic unit, not time unit. An unfinished task from yesterday belongs to Layer 1; a completed topic from yesterday drops one level today.

Channel switching: If the user asks similar questions on different channels (WhatsApp vs Telegram), it indicates genuine concern — promote priority to Layer 1.

Optional: Auto-Trigger (Hook Configuration)

If you use Claude Code or an agent that supports PreCompact hooks, you can configure auto-trigger. See setup-hook.md for details.

Without hook configuration, you can trigger manually: just tell the agent "compress my context" or "context is getting long, clean it up".