That particular task didn't need parallel agents or any of the advanced features. The prompt was: <prompt> Research claude pricing with caching and then review

That particular task didn't need parallel agents or any of the advanced features. The prompt was: <prompt> Research claude pricing with caching and then review a conversation history to calculate the cost. First, search online for pricing for anthropic api with and without caching enabled for all of the models: claude-3-haiku, claude-3-opus and claude-3.5-sonnet (sonnet 3.5). Create a json file with ALL the pricing data. from the llm history db, fetch the response.response_json.usage for each result under conversation_id=01j7jzcbxzrspg7qz9h8xbq1ww llm_db=$(llm logs path) schema=$(sqlite3 $llm_db '.schema') example usage: { "input_tokens": 1086, "output_tokens": 1154, "cache_creation_input_tokens": 2364, "cache_read_input_tokens": 0 } Calculate the actual costs of each prompt by using the usage object for each response based the actual token usage cached or not. Also calculate/simulate what it would have cost if the tokens where not cached. Create interactive graphs of different kinds to show the real cost of conversation, the cache usage, and a comparison to what it would have costed without caching. Write to intermediary files along the way. Ask me if anything is unclear. </prompt> I just gave it your task and I'll share the results tomorrow (I'm off to bed).