52 lines
1.9 KiB
Plaintext
52 lines
1.9 KiB
Plaintext
# Example 13: Variables & Context
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#
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# This example demonstrates using let/const bindings to capture session
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# outputs and pass them as context to subsequent sessions.
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# Define specialized agents for the workflow
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agent researcher:
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model: sonnet
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prompt: "You are a thorough research assistant who gathers comprehensive information on topics."
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agent analyst:
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model: opus
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prompt: "You are a data analyst who identifies patterns, trends, and key insights."
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agent writer:
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model: opus
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prompt: "You are a technical writer who creates clear, well-structured documents."
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# Step 1: Gather initial research (captured in a variable)
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let research = session: researcher
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prompt: "Research the current state of quantum computing, including recent breakthroughs, major players, and potential applications."
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# Step 2: Analyze the research findings (using research as context)
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let analysis = session: analyst
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prompt: "Analyze the key findings and identify the most promising directions."
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context: research
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# Step 3: Get additional perspectives (refreshing context)
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let market-trends = session: researcher
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prompt: "Research market trends and commercial applications of quantum computing."
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context: []
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# Step 4: Combine multiple contexts for final synthesis
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const report = session: writer
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prompt: "Write a comprehensive executive summary covering research, analysis, and market trends."
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context: [research, analysis, market-trends]
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# Step 5: Iterative refinement with variable reassignment
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let draft = session: writer
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prompt: "Create an initial draft of the technical deep-dive section."
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context: research
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# Refine the draft using its own output as context
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draft = session: writer
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prompt: "Review and improve this draft for clarity and technical accuracy."
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context: draft
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# Final polish
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draft = session: writer
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prompt: "Perform final editorial review and polish the document."
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context: draft
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