by OnlyTerp · Codex Skill · ★ 279
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OpenClaw Optimization Guide Make Your OpenClaw AI Agent Faster, Smarter, and Actually Useful Speed optimization, memory architecture, context management, model selection, and one-shot development for OpenClaw By Terp — Terp AI Labs Table of Contents Speed — Trim context files, add fallbacks, manage reasoning mode Context Bloat — Quadratic scaling, built-in defenses Cron Session Bloat — Session file accumulation, cleanup Memory — 3-tier memory system, Ollama vector search Orchestration — Sub-agent delegation, CEO/COO/Worker model Models — Provider comparison, pricing, local models Web Search — Tavily, Brave, Serper, Gemini grounding One-Shotting Big Tasks — Research-first methodology Vault Memory System — Structured knowledge graph, MOCs, cross-session continuity Quick Checklist — 30-minute setup checklist The One-Shot Prompt — Copy-paste automation prompt The Pr
| Stars | 279 |
| Forks | 33 |
| Language | TypeScript |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 52.512/100 |
| Open Issues | 4 |
| Last Updated | 2026-04-30 |
| Created | 2026-03-13 |
| Platforms | claude-code, gemini, node |
| Est. Tokens | ~80k |
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openclaw-optimization-guide is Make your OpenClaw AI agent faster, smarter, and cheaper. Speed optimization, memory architecture, context management, model selection, and one-shot development guide.. It is categorized as a Codex Skill with 279 GitHub stars.
openclaw-optimization-guide is primarily written in TypeScript. It covers topics such as ai-agent, ai-assistant, best-practices.
You can find installation instructions and usage details in the openclaw-optimization-guide GitHub repository at github.com/OnlyTerp/openclaw-optimization-guide. The project has 279 stars and 33 forks, indicating an active community.
openclaw-optimization-guide is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to openclaw-optimization-guide on Agent Skills Hub include GoGogot, swarmclaw, Mysti. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.