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 6 active lines to adapt.
Already linked to a challenge workflow.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Begin by setting up your CrewAI environment. Define three core agents: a 'PromptMonitor' to ingest and filter raw prompts, a 'BrandAnalyzer' to extract brands and determine sentiment using DeepSeek R1, and a 'ReportGenerator' to synthesize findings. Each agent should have distinct roles and goals. Configure tools for `ZapierInterfaces` to receive new prompts (simulate via webhook) and `KoreAIAssistant` for custom workflow automation (e.g., triggering alerts). Integrate DeepSeek R1 via its API for the BrandAnalyzer's core reasoning. Use the following snippet to start your agent definitions: ```python from crewai import Agent, Task, Crew, Process from langchain_community.llms import DeepSeekLLM # Example integration class CustomZapierTool: # Placeholder for actual Zapier integration # ... methods for Zapier interactions pass class CustomKoreAITool: # Placeholder for actual Kore.ai interactions # ... methods for Kore.ai interactions pass deepseek_llm = DeepSeekLLM(model='DeepSeek R1', api_key='YOUR_DEEPSEEK_API_KEY') prompt_monitor = Agent( role='Prompt Monitor', goal='Ingest and pre-process raw AI-generated prompts.', backstory='Expert in data ingestion and filtering, ensuring only relevant prompts are passed for analysis.', tools=[CustomZapierTool()], llm=deepseek_llm, verbose=True ) # Define other agents and their tasks here... ``` Focus on defining the agents' roles, goals, and the initial set of tools they will use. Make sure the BrandAnalyzer explicitly uses DeepSeek R1 for sentiment analysis.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.
Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.
Check whether the prompt asks for the right evidence, confidence signal, and escalation path.
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 already mixes executable detail with instructions, so the safest path is to tune examples and interfaces before you rewrite the overall scaffold.
Agents for Prompt-Driven Brand Sentiment & Affinity
This challenge focuses on building a sophisticated multi-agent system using CrewAI to analyze brand mentions and sentiment within a stream of AI-generated prompts. The system will orchestrate specialized agents to monitor, categorize, and report on brand affinity. The core idea is to simulate an intelligent monitoring platform that provides actionable insights into brand perception and recommendation patterns from diverse data sources, leveraging CrewAI's role-playing architecture. Developers will design a collaborative agent team, where each agent has a distinct role, tools, and goals. For instance, a 'Prompt Ingestor' agent, a 'Brand Analyst' agent, and a 'Report Generator' agent will work in concert. The system will use DeepSeek R1 for its advanced reasoning capabilities to accurately interpret nuanced sentiment and complex brand associations. Integration with Zapier Interfaces will enable seamless data ingestion from various sources, while tool can facilitate custom workflow automation for alert triggers and data processing. Voiceflow will provide a conversational interface for real-time query and status updates, making the system highly interactive. Blaxel will serve as the underlying agent orchestration backbone, ensuring robust agent 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.