const agentDefinition = {
  id: "etl-manager",
  displayName: "ETL Pipeline Manager",
  publisher: "brandon",
  version: "0.0.2",
  model: "openai/gpt-5",
  toolNames: [
    "spawn_agents",
    "think_deeply",
    "add_message"
  ],
  spawnableAgents: [
    "brandon/extract-agent@0.0.2",
    "brandon/transform-agent@0.0.2",
    "brandon/load-agent@0.0.2"
  ],
  inputSchema: {
    params: {
      type: "object",
      properties: {
        domain: {
          type: "string",
          description: "Data domain for ETL processing, e.g. places, events, projects"
        },
        loadParams: {
          type: "object",
          description: "Any special parameters for load agent"
        },
        extractParams: {
          type: "object",
          description: "Any special parameters for extract agent"
        },
        transformParams: {
          type: "object",
          description: "Any special parameters for transform agent"
        }
      }
    },
    prompt: {
      type: "string",
      description: "The data processing request to execute through ETL pipeline"
    }
  },
  includeMessageHistory: true,
  outputMode: "last_message",
  spawnerPrompt: `Use this agent to execute a complete ETL pipeline for data processing requests`,
  systemPrompt: `You are an ETL pipeline manager that coordinates sequential data processing through extract, transform, and load stages.`,
  instructionsPrompt: ``,
  stepPrompt: ``,
  handleSteps: function* ({ prompt, params }) {
    const extractPrompt = `Analyzing user request "${prompt}" to generate optimal extraction strategy. Consider: data domain (${params?.domain || "unknown"}), specific search terms needed, target sources, and query refinement for maximum relevance.`, { toolResult: extractResults } = yield {
      toolName: "spawn_agents",
      input: {
        agents: [
          {
            agent_type: "extract-agent",
            prompt: extractPrompt,
            params: params?.extractParams || {}
          }
        ]
      }
    };
    if (!extractResults || extractResults.length === 0) {
      yield {
        toolName: "add_message",
        input: {
          role: "user",
          content: "Extract step failed."
        }
      };
      return;
    }
    const extractResult = extractResults[0]?.type === "json" ? extractResults[0].value : extractResults[0], transformPrompt = "Processing extracted data from previous step. Need to transform raw data into canonical schema. Consider: data quality, normalization needs, deduplication strategy, and enrichment opportunities based on extracted content.", { toolResult: transformResults } = yield {
      toolName: "spawn_agents",
      input: {
        agents: [
          {
            agent_type: "transform-agent",
            prompt: transformPrompt,
            params: {
              ...params?.transformParams,
              extractResult
            }
          }
        ]
      }
    };
    if (!transformResults || transformResults.length === 0) {
      yield {
        toolName: "add_message",
        input: {
          role: "user",
          content: "Transform step failed."
        }
      };
      return;
    }
    const transformResult = transformResults[0]?.type === "json" ? transformResults[0].value : transformResults[0], loadPrompt = `Final filtering and ranking phase for user request "${prompt}". Need to apply user constraints, score relevance, and rank results. Consider: user preferences, contextual relevance, quality metrics, and practical constraints.`, { toolResult: loadResults } = yield {
      toolName: "spawn_agents",
      input: {
        agents: [
          {
            agent_type: "load-agent",
            prompt: loadPrompt,
            params: {
              ...params?.loadParams,
              transformResult
            }
          }
        ]
      }
    };
    if (!loadResults || loadResults.length === 0) {
      yield {
        toolName: "add_message",
        input: {
          role: "user",
          content: "Load step failed."
        }
      };
      return;
    }
    const loadResult = loadResults[0]?.type === "json" ? loadResults[0].value : loadResults[0];
    yield {
      toolName: "add_message",
      input: {
        role: "user",
        content: typeof loadResult === "string" ? loadResult : JSON.stringify(loadResult)
      }
    };
  }
}