Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
Already linked to a challenge workflow.
Sign in to keep private prompt variations.
Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Implement the CrewAI agents and define their tasks. For each agent, set up their `role`, `goal`, `backstory`, and `verbose` level. Define specific `Task` objects for data gathering, analysis, and synthesis. Initialize GPT-4o as the LLM for your agents (e.g., `llm=ChatOpenAI(model='gpt-4o')`). Provide Python code snippets for setting up agents and tasks, and a basic `Crew` that runs these tasks sequentially or in parallel, ensuring clear output for evaluation.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Competitive Intelligence: Multi-Agent Strategic Analysis for IPO Readiness
Develop a CrewAI-powered multi-agent system designed to perform strategic competitive intelligence analysis, specifically focusing on a company preparing for an IPO (like SpaceX, in light of recent news). This system will orchestrate a team of specialized agents—such as a 'Market Analyst,' 'Financial Strategist,' and 'Competitive Researcher'—to gather, analyze, and synthesize information on market conditions, competitor activities and potential investor sentiment. The agents will collaborate to produce a comprehensive strategic readiness report for the IPO, including SWOT analysis and recommendations, demonstrating CrewAI's ability to tackle complex, role-based analytical tasks. The challenge also involves integrating a voice interface for presenting key findings and an AI gateway for efficient LLM management.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.