token-optimizer

SKILL.md

Token Optimizer Skill

This skill provides the procedural knowledge to keep your OpenClaw instance lean and efficient.

Quick Reference

Problem
Solution

Background tasks bloating context
Cron isolation (sessionTarget: "isolated")

Reading entire history every turn
Local RAG with memory_search

Context exceeds 100k tokens
Reset & Summarize protocol

Finding old conversations
Session transcript indexing

Workflow 1: Periodic Task Isolation

To prevent background tasks from bloating your main conversation context, always isolate them.
Steps

Locate your openclaw.json config.

In the cron.jobs array, set sessionTarget: "isolated" for any task that doesn't need to be part of the main chat history.

Use the message tool within the task's payload if human intervention is required.

Example Config

{
  "cron": {
    "jobs": [
      {
        "name": "Background Check",
        "schedule": { "kind": "every", "everyMs": 1800000 },
        "sessionTarget": "isolated",
        "payload": {
          "kind": "agentTurn",
          "message": "Check for updates. If found, use message tool to notify user.",
          "deliver": true
        }
      }
    ]
  }
}

Key Points

sessionTarget: "isolated" runs the task in a separate, transient session

Use deliver: true to send results back to the main channel

Isolated sessions don't pollute your main context with heartbeat/check history

Workflow 2: Reset & Summarize (The "Digital Soul" Protocol)

When your context usage (visible via 📊 session_status) exceeds 100k tokens, perform a manual consolidation.

Steps

Check Context: Run 📊 session_status to see current token usage

Scan History: Review the current session for new facts, preferences, or project updates

Update MEMORY.md: Append these new facts to your long-term memory file

Daily Log: Ensure memory/YYYY-MM-DD.md is up to date with today's events

Restart: Run openclaw gateway restart to clear the active history

When to Trigger

Context > 100k tokens

Session running for several days

Noticeably slower responses

User explicitly requests a "fresh start"

Workflow 3: Local RAG Configuration

For efficient recall without token burn, configure local embeddings.

Configuration (openclaw.json)

{
  "memorySearch": {
    "embedding": {
      "provider": "local",
      "model": "hf:second-state/All-MiniLM-L6-v2-Embedding-GGUF"
    },
    "store": "sqlite",
    "paths": ["memory/", "MEMORY.md"],
    "extraPaths": []
  }
}

Usage

Use memory_search to retrieve context from your logs instead of loading everything:

memory_search(query="what did we decide about the API design")

The tool returns relevant snippets with file paths and line numbers. Use memory_get to pull specific sections.

Workflow 4: Session Transcript Indexing (Advanced)

Index your session transcripts (.jsonl files) for searchable conversation history.

How It Works

OpenClaw stores session transcripts in ~/.openclaw/sessions/. These can be indexed for semantic search, allowing you to find old conversations without loading them into context.

Configuration

Add transcript paths to memorySearch.extraPaths:

{
  "memorySearch": {
    "extraPaths": [
      "~/.openclaw/sessions/*.jsonl"
    ]
  }
}

Best Practices

Index selectively (recent sessions, important conversations)

Use date-based filtering to limit search scope

Archive old transcripts to cold storage after indexing

Workflow 5: Hybrid Search (Vector + BM25)

Combine semantic search with keyword matching for more accurate retrieval.

Why Hybrid?

Search Type
Strengths
Weaknesses

Vector (semantic)
Finds conceptually similar content
May miss exact terms

BM25 (keyword)
Finds exact matches
Misses synonyms/paraphrases

Hybrid
Best of both worlds
Slightly more compute

How to Use

When memory_search returns low-confidence results:

Try the search with different phrasing (semantic variation)

Search for exact keywords you remember (BM25 behavior)

Combine results manually if needed

Future Enhancement

OpenClaw's RAG system may support native hybrid search in future versions. For now, run multiple queries when precision matters.

Troubleshooting

"My context is growing too fast"

Check cron jobs: Are they isolated?

Check heartbeat frequency: Too frequent = more tokens

Are you loading large files unnecessarily?

"memory_search returns nothing"

Verify memorySearch is configured in openclaw.json

Check that the embedding model is downloaded

Ensure memory files exist and have content

"Restart didn't clear context"

The restart clears the session history, but:

System prompt is always loaded

Workspace files (MEMORY.md, etc.) are injected fresh

This is by design for continuity

Credits

Pépère (shAde) — Original concept and documentation

Zayan (Clément) — Implementation and testing

Built for the OpenClaw community. 🦦😸