openclaw-agent-token-optimizer
DEPRECATED — duplicate listing. Please use the canonical "openclaw-agent-optimize" skill instead.
Why use this skill?
Learn how to optimize your OpenClaw agent token usage. This guide directs you to the canonical optimization skill to reduce costs and improve context window efficiency.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/phenomenoner/openclaw-agent-token-optimizerWhat This Skill Does
This skill is officially deprecated and serves as a placeholder to redirect users to the canonical version. The primary purpose of the associated functionality (now located at phenomenoner/openclaw-agent-optimize) is to perform intelligent token reduction for OpenClaw agents. By analyzing context windows and stripping redundant metadata, formatting, or conversational filler, this skill ensures that your AI agents remain within their operational limits while maintaining semantic integrity. It functions as a pre-processing layer that optimizes token consumption, effectively lowering costs and reducing latency in high-volume agent tasks.
Installation
Do not use the deprecated path. To install the functional and supported version of the optimization skill, run the following command in your terminal:
clawhub install phenomenoner/openclaw-agent-optimize
Ensure that you have the latest version of the ClawHub CLI installed to avoid pathing errors. If you have previously installed the deprecated openclaw-agent-token-optimizer package, please remove it to prevent configuration conflicts.
Use Cases
This skill is intended for users managing complex AI workflows. Common scenarios include:
- Large-scale document summarization where the source text exceeds typical input limits.
- Maintaining long-running chat sessions where the agent starts to lose context due to token saturation.
- Optimizing API calls for agents that rely on high-cost models where every token represents a financial expense.
- Preparing dataset batches for fine-tuning, ensuring only high-value information is retained in the training set.
Example Prompts
- "Optimize the current context window by removing redundant system headers and repetitive conversational filler from the last ten messages."
- "Review the provided markdown documentation and compress it by 30% while retaining all critical technical instructions for the agent."
- "Summarize the long-form output from my recent web-scraping task to ensure it fits within a 4k token limit before passing it to the analyzer."
Tips & Limitations
- Lossy Compression: Always review the output. While the optimization algorithm is designed to preserve meaning, aggressive token reduction can occasionally remove nuanced instructions.
- Compatibility: This skill is designed for OpenClaw native agents. Integration with third-party frameworks may require custom wrappers.
- Updates: Since this toolset evolves rapidly, ensure you keep the
phenomenoner/openclaw-agent-optimizepackage updated via theclawhub updatecommand to receive the latest improvements in compression efficiency.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-phenomenoner-openclaw-agent-token-optimizer": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Related Skills
openclaw-agent-optimize
Use when: you want to optimize an OpenClaw setup (cost/quality tradeoffs, model routing, context discipline, delegation, reliability) and you’re okay with a structured audit → options → recommended plan. Don’t use when: you want immediate config mutations without review, or the question is unrelated to OpenClaw operations. Output: a prioritized plan + exact change proposals (with rollback) if approved.
context-scope-tags
Use when: you need strict context boundaries in chat (Telegram/Discord/Slack/etc.) and want to prevent topic bleed using explicit tags like [ISO], [SCOPE], [GLOBAL], [NOMEM], [REM]. Don’t use when: you want normal free-form conversation with automatic carry-over. Output: a copy/paste tag cheat sheet + routing rules.
cron-worker-guardrails
Use when hardening OpenClaw cron workers (especially isolated agentTurn jobs) against quoting failures, brittle shell patterns, SIGPIPE false failures, and cwd/env drift. Output: a scripts-first hardening checklist + portable patterns.
context-clean-up
Use when: you suspect OpenClaw prompt context is bloating (slow replies, high cost, repeated transcript noise) and you want a ranked offender list + a reversible clean-up plan. Don’t use when: you want the assistant to apply fixes automatically, or you’re asking for unrelated troubleshooting. Output: an audit summary + 3–8 concrete fix steps + rollback notes (no automatic changes).
openclaw-agent-optimize-skill
DEPRECATED — duplicate listing. Please use the canonical "openclaw-agent-optimize" skill instead.