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jev pilot
Optimize Claude Code costs by automatically selecting reasoning effort and subagent models per prompt.
Free
jev pilot
Example session with this skill installed
How do I install jev-pilot to save on my Claude Code token usage?
- Read your context and instructions
- Compiled the jev pilot
I can help you set up jev-pilot to optimize your Claude Code usage limits and costs. It uses a fast decision model to pick the right reasoning effort and subagent for each task.
Installation Instructions
To get started, you will need an OpenRouter API key. Once you have it, run these commands in your terminal:
# 1. Add and install the plugin
claude plugin marketplace add Akramovic1/jev-pilot
claude plugin install jev-pilot@jev-pilot --config openrouterApiKey=<your_key_here> --config timeoutMs=1500
# 2. Enable function hooks in your environment
export CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1
# 3. Launch Claude Code
claude
Alternatively, you can make the function hooks permanent by adding them to your ~/.claude/settings.json:
{
"env": {
"CLAUDE_CODE_ENABLE_FUNCTION_HOOKS": "1"
}
}
Once running, you will see a status indicator above your prompt. You can use /jev to see active switches or /jev-pilot:report to view recent optimization data.
Next steps
- Generate an API key at openrouter.ai if you haven't already.
- Run the installation commands provided above.
- Verify the
ready · openrouterstatus appears in your next Claude Code session.
Connects securely to your tools. The creator never sees your data.
About this skill
Running Claude Code on xhigh reasoning for every task quickly exhausts usage limits and inflates costs. This skill integrates jev-pilot to automate model selection and reasoning effort, ensuring you only pay for Opus when the task actually requires it.
What it does
- Automates reasoning levels by setting effort from
lowtoxhighbased on task complexity. - Optimizes subagent selection by routing simple lookups to Haiku and well-defined coding tasks to Sonnet.
- Handles failed tool calls by automatically raising reasoning effort when a subagent struggles.
- Smart skill attachment by identifying and attaching only the specific SKILL.md required for the current prompt.
- Usage reporting via
/jev-pilot:reportto visualize decision records and tuning suggestions.
How it works
- Installation of the jev-pilot plugin via the Claude Code marketplace using an OpenRouter API key.
- Environment configuration to enable function hooks in Claude Code settings or via the
claude-jevlauncher. - Automatic classification where a fast decision model analyzes each prompt to set parameters before execution.
- Command-line management using
/jevto toggle features or check status mid-session.
Frameworks & tools
This skill is purpose-built for Claude Code (CLI) version 2.1.278 or newer. It requires an OpenRouter API key to access the Jev decision model and utilizes Claude Code function hooks for real-time intervention.
Why this beats prompting it yourself
Manually switching models or effort levels for every command is friction-heavy and prone to human error. This skill automates the decision logic at the hook level, reducing token waste without requiring the user to think about the underlying infrastructure.
Use cases
- Cost reduction when running high-volume terminal tasks that don't all need Opus.
- Rate limit management by offloading read-only operations to faster, lighter models.
- Workflow optimization by automatically attaching relevant skills to the context.
Known limitations
Requires Claude Code 2.1.278+. Needs an OpenRouter API key. Only prompt text and metadata are sent to the classifier, not full code files. Function hooks must be enabled.
How to use
Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.
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