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    Token Cost Estimator

    1

    Project LLM API costs and token usage by analyzing prompts, datasets, and traffic volumes.

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    Token Cost Estimator

    Token Cost Estimator

    Example session with this skill installed

    Estimate the cost for 10k daily requests using gpt-4o. System prompt is prompts/system.md, average user input is 800 tokens, and expected output is 400 tokens. Use prices.json.

    • Read your context and instructions
    • Compiled the token cost estimator

    I have processed the token counts for your system prompt and calculated the projected costs based on your traffic volume of 10,000 daily requests.

    LLM Cost Projection: GPT-4o

    MetricValue
    System Prompt (prompts/system.md)1,250 tokens
    Avg. User Input800 tokens
    Avg. Model Output400 tokens
    Total Tokens per Request2,450 tokens

    Financial Breakdown (USD)

    PeriodInput CostOutput CostTotal Spend
    Per Request$0.01025$0.00600$0.01625
    Daily (10k req)$102.50$60.00$162.50
    Monthly (30 days)$3,075.00$1,800.00$4,875.00

    Context & Cache Optimization

    • Context Usage: 2,450 / 128,000 (1.9%) - Low risk of overflow.

    Cache Potential: 51% of your input is the static system prompt. Enabling prompt caching could reduce daily input costs by up to $25.60.

    Next steps

    1. Review prices.json to ensure the $5.00/$15.00 per 1M tokens rate is current.
    2. Run a batch count on conversations.jsonl to see if real user inputs vary from the 800-token estimate.
    3. Compare these figures against a gpt-4o-mini price profile to see potential savings.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Predicting LLM expenses shouldn't be a guessing game. This skill eliminates the manual math and spreadsheet errors when budgeting for AI features. It provides technical estimates for token counts and total API spend based on real files and traffic projections.

    What it does

    • Token counting calculates volumes across prompts, documents, datasets, and codebases.
    • Cost projection estimates daily and monthly spend using custom traffic volumes.
    • Context auditing flags requests that risk exceeding specific model context windows.
    • Model comparison side-by-sides different providers and pricing tiers for the same workload.
    • Caching analysis models the financial impact of prompt caching and batch discounts.

    How it works

    1. Source analysis where the skill counts tokens in your local .md, .jsonl, or .csv files.
    2. Pricing configuration where you provide a JSON file with current per-million-token rates.
    3. Parameter input including expected output lengths and daily request volume.
    4. Report generation showing the breakdown of input, output, and cached costs.

    Frameworks & tools

    The skill utilizes Python scripts for processing. It supports the tiktoken library for cl100k_base accuracy or uses a calibrated heuristic for prose, code, and CJK text.

    Why this beats prompting it yourself

    General LLMs often hallucinate token counts and fail at complex multi-step financial arithmetic. This tool uses structured scripts and real file parsing to ensure math remains consistent and verifiable across your entire dataset.

    Use cases

    • Estimate the monthly burn for a new RAG feature before implementation.
    • Compare GPT-4o vs Claude 3.5 Sonnet costs for a specific document processing pipeline.
    • Audit a .jsonl fine-tuning dataset to check for context window violations.
    • Calculate potential savings from implementing prompt caching on static system instructions.

    Known limitations

    Does not ship with built-in prices; requires a prices.json file. Heuristic counts are estimates and vary by model unless tiktoken is installed and specified.

    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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