io.github.helphub369/model-ruler
name:io.github.helphub369/model-ruler
Deterministic AI/LLM cost calculators for tokens, providers, RAG, agents, evals, and automation.
- transport:
- remote
- credential class:
- self-provisionable
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- agent-loop-cost-calculatorshallow
Use when a user is running multi-step LLM agents and needs cost per successful task. Accounts for failure overhead and context growth across turns.
- agent-workflow-cost-calculatorshallow
Use when a user needs to estimate automation-platform cost for an agent workflow, including app-action fan-out and MCP tool-call accounting, separate from LLM token spend.
- automation-cost-calculatorshallow
Use when a user needs to compare workflow automation platform cost across Zapier task billing, Make credit billing, and n8n execution billing for a recurring workflow shape.
- context-window-plannershallow
Use when a user needs to know whether a document plus prompt plus output fits within a model's context window, or wants a strategy recommendation (truncate/summarize/rag/chunk).
- eval-cost-calculatorshallow
Use when a user needs to budget an LLM evaluation run. Given samples/models/trials, returns total cost, per-run cost, and parallel time estimate.
- fine-tune-roi-calculatorshallow
Use when a user is considering fine-tuning vs prompt engineering. Returns training cost, monthly inference savings, months-to-ROI, and breakeven volume.
- observability-cost-calculatorshallow
Use when a user needs to budget LLM observability tooling. Returns monthly cost at given request volume with retention adjustment.
- provider-cost-calculatorshallow
Use when a user asks what an LLM workload costs on a specific provider/model, or wants to compare cost across providers. Given tokens per call and call volume, returns monthly cost plus a tier comparison table.
- quantization-calculatorshallow
Use when a user is planning to quantize an LLM to fit on smaller hardware. Given parameter count and precision transition, returns VRAM requirement, speedup estimate, and approximate quality delta.
- rag-pipeline-cost-calculatorshallow
Use when a user needs end-to-end RAG cost estimation (embedding + vector store + generation). Returns monthly cost with breakdown and dominant-component identification.
- self-host-breakeven-calculatorshallow
Use when a user is deciding between API usage and self-hosted GPU inference at a given volume. Returns breakeven token volume, monthly cost comparison, and go/no-go recommendation.
- token-countershallow
Use when a user asks how many tokens a given text will consume, or needs to estimate prompt size before pricing a workload. Given text and tokenizer family, returns low/high token range and byte-level measurements.
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