Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Master optimization system - APPLIES TO EVERY RESPONSE. Before responding, classify task complexity (simple question vs analysis vs coding). Use Haiku for simple/navigation/extraction/status. Use Sonnet ONLY for writing/analysis/planning/debugging. Monitor context size - if >50k tokens, recommend /compact. For automations, use scheduler wrapper. Never load full conversation history for simple tasks. Heartbeats always Haiku, single-line only. Never use Opus. This skill MUST run before every response to prevent 100k+ token bloat.
Master optimization system - APPLIES TO EVERY RESPONSE. Before responding, classify task complexity (simple question vs analysis vs coding). Use Haiku for simple/navigation/extraction/status. Use Sonnet ONLY for writing/analysis/planning/debugging. Monitor context size - if >50k tokens, recommend /compact. For automations, use scheduler wrapper. Never load full conversation history for simple tasks. Heartbeats always Haiku, single-line only. Never use Opus. This skill MUST run before every response to prevent 100k+ token bloat.
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
The OpenClaw Optimizer is a comprehensive performance and cost optimization skill designed to enhance the efficiency of Clawdbot's subagent workflows.
Intelligent model selection based on task complexity Automatic routing between Haiku, Sonnet, and Opus models Cost prediction and optimization
Robust task execution with retry mechanism Configurable preflight and postflight hooks Timeout and exponential backoff handling
Browser tab serialization Concurrent tab management Circuit breaker for preventing runaway processes
Automatic token tracking Summarization at 50,000 token threshold Preserves critical context
Monitor daily budget Track task execution Visualize model distribution Circuit breaker status
Before Optimization: $90/day After Optimization: $3-5/day Savings: 95%
npm install @startclaw/openclaw-optimizer
const { TaskRouter, OptimizerScheduler, BrowserGovernor } = require('@startclaw/openclaw-optimizer'); const router = new TaskRouter(); const scheduler = new OptimizerScheduler(); const browserGovernor = new BrowserGovernor(); // Automatic model and cost optimization const modelSelection = router.selectModel(taskDescription); await scheduler.execute(task, modelSelection);
# Real-time dashboard python3 scripts/dashboard.py watch
StartClaw Internal Use License
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Largest current source with strong distribution and engagement signals.