estimate-llm-costlisted

# Estimate LLM cost Cost surprises come from not doing the arithmetic. This skill ships a small, dependency-free calculator so you can price a call, project a monthly bill, and compare models with actual numbers. ## Use the bundled script [`scripts/llm_cost.py`](scripts/llm_cost.py) is pure Python, no install needed: ```python from llm_cost import estimate_cost, project_monthly estimate_cost(1500, 300, model="gpt-4o") # one call, USD project_monthly(1500, 300, calls_per_day=5000, model="gpt-4o") # monthly projection estimate_cost(2000, 200, model="gpt-4o", cached_input_tokens=1800) # with prompt caching ``` Run it directly to see a worked example: `python scripts/llm_cost.py`. Prices in the `PRICES` table are approximate and change often, so pass `input_price`/`output_price` explicitly when you need exact figures, or edit the table. The arithmetic (not the price table) is what the tests pin down. ## How to apply it 1. **Price the call** with realistic token counts (measure them from a trace, see `instrument-llm-observability`). 2. **Project the bill** with your real call volume. A cheap call at 10k/day beats an expensive one at 10/day. 3. **Compare models** by running the same tokens through two model ids. Pick the cheapest that still passes your evals (see `compare-llm-models`). 4. **Feed it into cost cutting** (see `reduce-llm-cost`). ## Validation Run the tests: `pytest skills/estimate-llm-cost/tests/`. They check the math is exact for exp