A pre-flight cost estimation and approval gate system designed to prevent API overspend during multi-agent deployments.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-cost-governor
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install openclaw-cost-governor using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Cost Governor is a critical safety layer for AI agent workflows, specifically designed to mitigate the risk of runaway API costs when spawning subagents. By implementing pre-flight checks and mandatory approval gates for high-cost operations, it ensures that developers maintain full visibility into their spending. This skill is an essential component of professional Openclaw Skills environments, providing a structured way to manage budgets across various LLM providers including Anthropic and OpenAI.
It transforms the unpredictable nature of autonomous agents into a controlled, budget-conscious process. By using historical data and task-specific multipliers, Cost Governor provides highly accurate estimates before a single token is spent, protecting users from unexpected billing spikes during complex research or creative tasks.
# Create the necessary tracking directory within your workspace
mkdir -p ~/.openclaw/workspace/notes
# Initialize the cost tracking log file
touch ~/.openclaw/workspace/notes/cost-tracking.md
# Optionally set a daily budget in your environment configuration
echo "DAILY_BUDGET=20.00" >> ~/.openclaw/workspace/.env
The skill maintains data primarily within a dedicated tracking file using a structured Markdown table format to track lifecycle metrics and budget performance.
| Field | Description |
|---|---|
| Date | The timestamp of the subagent operation |
| Task | Name or description of the spawned task |
| Model | The specific LLM provider used (e.g., Claude Opus, GPT-4) |
| Est. | The pre-flight cost estimate generated by the skill |
| Actual | The final cost reported after task completion |
| Ratio | The accuracy factor used to tune future multipliers |
It also monitors environment variables like DAILY_BUDGET for global cap enforcement across the Openclaw Skills ecosystem.
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