loading
loading
Route agent calls to the cheapest capable model via OpenRouter dynamically.
Stops paying GPT-tier prices for tasks a cheap model can do — it classifies each request and routes to the cheapest model that clears the bar, logging the savings.
Listed for review
No verified public repo for this skill yet, so this page does not give you an install command. Skills with a verified source install in one command — or fully manual: copy the skill folder into .claude/skills/ and your agent picks it up.
Boostor Quality Score
84/100 · B
OpenRouter Model Router classifies each incoming task by complexity and required capabilities, then selects the most cost-effective model available on OpenRouter that meets the bar. Fallback chains ensure availability even during provider outages. Cost savings are logged per session so you can track ROI.
Review an agent's logging so a production failure can be replayed without exposing secrets.
Sets the bar at replaying a real failure from the trace without leaking secrets — and catches teams logging cost but not per-step cause.
Transparent + deterministic: every point above is computed from this skill's real fields plus a prompt-injection safety scan. No black box, no pay-to-rank.
Keep long-running agents under token limits without losing critical state.
The fix for the wall every long-running agent hits — it summarizes old turns before the context window blows, instead of crashing mid-task.
Scaffold, test, and publish a Model Context Protocol server in minutes.
Writing an MCP server by hand means hours of boilerplate before the first tool runs. This validates against the spec so you skip the silent-failure stage.