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

1. Locate your openclaw.json config.
2. In the cron.jobs array, set sessionTarget: "isolated" for any task that doesn't need to be part of the main chat history.
3. 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

1. Check Context: Run 📊 session_status to see current token usage
2. Scan History: Review the current session for new facts, preferences, or project updates
3. Update MEMORY.md: Append these new facts to your long-term memory file
4. Daily Log: Ensure memory/YYYY-MM-DD.md is up to date with today's events
5. 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:

1. Try the search with different phrasing (semantic variation)
2. Search for exact keywords you remember (BM25 behavior)
3. 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"

1. Check cron jobs: Are they isolated?
2. Check heartbeat frequency: Too frequent = more tokens
3. Are you loading large files unnecessarily?

"memory_search returns nothing"

1. Verify memorySearch is configured in openclaw.json
2. Check that the embedding model is downloaded
3. 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. 🦦😸