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From llm-router
Tracks and reports cost savings from routing LLM tasks to cheaper models. View cross-session savings, send weekly digests to Slack/Discord, and monitor routing quality.
How this skill is triggered — by the user, by Claude, or both
Slash command
/llm-router:savingsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Track and report how much you've saved by routing tasks to cheaper models.
Track and report how much you've saved by routing tasks to cheaper models.
llm_savings — Cross-session savings by period (today / week / month / all time)
llm_usage("all") — Full dashboard: subscription %, Codex status, savings, providers
llm_dashboard — Open web dashboard at localhost:7337
Send a weekly savings summary to your team channel:
llm_digest(period="week") — format digest, print only
llm_digest(period="week", send=True) — format + push to LLM_ROUTER_WEBHOOK_URL
Set LLM_ROUTER_WEBHOOK_URL=https://hooks.slack.com/... in your environment.
Auto-detects Slack, Discord, or generic JSON webhook from the URL.
The digest automatically flags when today's spend is > 2× the 7-day average. No config needed — it's always on.
llm_policy — Show active org/repo routing policy + last 10 audit decisions
Set policy in ~/.llm-router/org-policy.yaml:
block_models:
- "o3*" # never use o3 series (too expensive)
- "gpt-4o"
allow_models:
- "gemini*" # prefer Gemini (allow overrides block)
task_caps:
image: 2.00 # max $2/day on image generation
llm_benchmark — Per-task routing accuracy from your 👍/👎 feedback
llm_rate — Rate the last response to improve future routing
llm_quality_report — Full routing stats, classifier accuracy, savings metrics
npx claudepluginhub ypollak2/llm-router --plugin llm-routerGuides capturing AI tool costs from Anthropic and OpenAI dashboards quarterly, records structured Markdown data for health snapshots and MODEL_ROUTING.md updates.
Routes tasks to the cheapest capable model via llm-router MCP tools (Ollama, Codex, paid APIs in free-first order). Maps task types to cost-optimized calls.
Tracks and optimizes LLM costs using Langfuse analytics, Metrics API, model routing, and budget alerts for spending analysis and anomaly detection in AI apps.