This skill provides the procedural knowledge to keep your OpenClaw instance lean and efficient.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontoken-optimizerExecute the skills CLI command in your project's root directory to begin installation:
Fetches token-optimizer from d4kooo/openclaw-token-memory-optimizer and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate token-optimizer. Access via /token-optimizer in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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This skill provides the procedural knowledge to keep your OpenClaw instance lean and efficient.
| 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 |
To prevent background tasks from bloating your main conversation context, always isolate them.
openclaw.json config.cron.jobs array, set sessionTarget: "isolated" for any task that doesn't need to be part of the main chat history.message tool within the task's payload if human intervention is required.{
"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
}
}
]
}
}
sessionTarget: "isolated" runs the task in a separate, transient sessiondeliver: true to send results back to the main channelWhen your context usage (visible via 📊 session_status) exceeds 100k tokens, perform a manual consolidation.
📊 session_status to see current token usagememory/YYYY-MM-DD.md is up to date with today's eventsopenclaw gateway restart to clear the active historyFor efficient recall without token burn, configure local embeddings.
openclaw.json){
"memorySearch": {
"embedding": {
"provider": "local",
"model": "hf:second-state/All-MiniLM-L6-v2-Embedding-GGUF"
},
"store": "sqlite",
"paths": ["memory/", "MEMORY.md"],
"extraPaths": []
}
}
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.
Index your session transcripts (.jsonl files) for searchable conversation history.
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.
Add transcript paths to memorySearch.extraPaths:
{
"memorySearch": {
"extraPaths": [
"~/.openclaw/sessions/*.jsonl"
]
}
}
Combine semantic search with keyword matching for more accurate retrieval.
| 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 |
When memory_search returns low-confidence results:
OpenClaw's RAG system may support native hybrid search in future versions. For now, run multiple queries when precision matters.
memorySearch is configured in openclaw.jsonThe restart clears the session history, but:
Built for the OpenClaw community. 🦦😸
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
token-optimizer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
token-optimizer has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: token-optimizer is focused, and the summary matches what you get after install.
Keeps context tight: token-optimizer is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for token-optimizer matched our evaluation — installs cleanly and behaves as described in the markdown.
token-optimizer reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in token-optimizer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added token-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added token-optimizer from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: token-optimizer is the kind of skill you can hand to a new teammate without a long onboarding doc.
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