Fix campaign_review: make Tool_WebSearcher steps optional so pipeline continues when tool is offline
This commit is contained in:
@@ -1,112 +1,114 @@
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name: campaign_review
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description: "CLM performance review -- Leo checks real-world campaign response and dispatches learnings to CL if the book is flopping."
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debug: true
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model: power
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system: agent_prompt
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agent_prompt:
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- "= identity.md"
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sections:
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- agent
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- project
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- deliverables
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- rag
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- message
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- instructions
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steps:
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- type: tool
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capability: Tool_WebSearcher
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query: "'{project.name}' romance novel reviews sales Amazon Goodreads TikTok BookTok"
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- type: tool
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capability: Tool_WebSearcher
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query: "'{project.name}' romance book social media engagement Instagram Facebook author"
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- type: think
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max_tokens: 4000
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output_key: performance_analysis
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hint: |
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You are Leo, Marketing Director at Crimson Leaf Marketing.
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Review the web search results above for the "{project.name}" campaign.
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Also review the campaign deliverables in the DELIVERABLES section.
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Analyze campaign performance:
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1. REACH: Are people finding the book? Any search results, reviews, mentions?
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2. ENGAGEMENT: Any social media traction (TikTok, Instagram, Facebook)?
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3. REVIEWS: Any reader reviews on Amazon, Goodreads, or BookTok?
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4. SALES SIGNALS: Any sales rank data, bestseller mentions, or download counts?
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5. SENTIMENT: Is the reception positive, mixed, or negative?
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If the book is too new (less than 2 weeks since campaign launch), note that and
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recommend checking again in 1 week.
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Rate overall performance:
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- GREEN: Exceeding expectations -- organic traction, positive reviews, good engagement
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- YELLOW: Mixed results -- some traction but underperforming potential
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- RED: Minimal traction -- the book is not getting discovered
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End with exactly:
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PERFORMANCE_RATING: GREEN|YELLOW|RED
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WEEKS_SINCE_LAUNCH: <number or "unknown">
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- type: think
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max_tokens: 100
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output_key: performance_rating
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hint: |
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Read the PERFORMANCE_RATING line from the analysis above.
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Output ONLY the value: GREEN, YELLOW, or RED. Nothing else.
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- type: think
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max_tokens: 600
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output_key: learnings_message
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hint: |
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Read the performance analysis above.
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If performance_rating is GREEN: output exactly: none
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If performance_rating is YELLOW or RED, write a MARKET LEARNINGS report for
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Crimson Leaf Holdings strategic team. This report will inform the next book.
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Include:
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- What did NOT work in this campaign (specific observations)
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- What the market data suggests about genre/audience fit
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- What competitors or similar books are doing that we are not
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- Concrete recommendations for the next book (genre, themes, marketing channels,
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pricing strategy, platform focus)
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- Should we continue this book's campaign or pivot resources to the next book?
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Format as a professional post-mortem. Be specific and data-driven.
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Output ONLY the report text. No preamble.
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- type: tool
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action: enqueue_strategy
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optional: true
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params:
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company_slug: "crimson_leaf"
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project_slug: "incubation"
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task_type: "market_intelligence"
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content: "{learnings_message}"
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- type: document
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filename: "campaign_performance_review"
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- type: close
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rag_update: true
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adjudication:
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enabled: true
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pass_threshold: 60
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deliverable_type: analysis
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criteria:
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completeness:
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weight: 40
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description: "All performance dimensions assessed (reach, engagement, reviews, sentiment)"
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actionability:
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weight: 35
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description: "Learnings are specific and actionable for future books"
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accuracy:
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weight: 25
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description: "Assessment is grounded in the search evidence, not speculation"
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name: campaign_review
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description: "CLM performance review -- Leo checks real-world campaign response and dispatches learnings to CL if the book is flopping."
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debug: true
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model: power
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system: agent_prompt
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agent_prompt:
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- "= identity.md"
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sections:
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- agent
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- project
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- deliverables
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- rag
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- message
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- instructions
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steps:
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- type: tool
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capability: Tool_WebSearcher
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optional: true
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query: "'{project.name}' romance novel reviews sales Amazon Goodreads TikTok BookTok"
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- type: tool
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capability: Tool_WebSearcher
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optional: true
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query: "'{project.name}' romance book social media engagement Instagram Facebook author"
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- type: think
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max_tokens: 4000
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output_key: performance_analysis
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hint: |
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You are Leo, Marketing Director at Crimson Leaf Marketing.
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Review the web search results above for the "{project.name}" campaign.
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Also review the campaign deliverables in the DELIVERABLES section.
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If no web search results are available (tool offline or no data found), note that
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and rate the campaign YELLOW with a recommendation to check again in 1 week.
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Analyze campaign performance:
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1. REACH: Are people finding the book? Any search results, reviews, mentions?
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2. ENGAGEMENT: Any social media traction (TikTok, Instagram, Facebook)?
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3. REVIEWS: Any reader reviews on Amazon, Goodreads, or BookTok?
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4. SALES SIGNALS: Any sales rank data, bestseller mentions, or download counts?
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5. SENTIMENT: Is the reception positive, mixed, or negative?
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If the book is too new (less than 2 weeks since campaign launch), note that and
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recommend checking again in 1 week.
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Rate overall performance:
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- GREEN: Exceeding expectations -- organic traction, positive reviews, good engagement
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- YELLOW: Mixed results -- some traction but underperforming potential, or too early to assess
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- RED: Minimal traction -- the book is not getting discovered
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End with exactly:
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PERFORMANCE_RATING: GREEN|YELLOW|RED
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WEEKS_SINCE_LAUNCH: <number or "unknown">
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- type: think
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max_tokens: 100
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output_key: performance_rating
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hint: |
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Read the PERFORMANCE_RATING line from the analysis above.
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Output ONLY the value: GREEN, YELLOW, or RED. Nothing else.
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- type: think
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max_tokens: 600
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output_key: learnings_message
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hint: |
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Read the performance analysis above.
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If performance_rating is GREEN or YELLOW: output exactly: none
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If performance_rating is RED, write a MARKET LEARNINGS report for
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Crimson Leaf Holdings strategic team. This report will inform the next book.
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Include:
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- What did NOT work in this campaign (specific observations)
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- What the market data suggests about genre/audience fit
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- What competitors or similar books are doing that we are not
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- Concrete recommendations for the next book (genre, themes, marketing channels,
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pricing strategy, platform focus)
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- Should we continue this book's campaign or pivot resources to the next book?
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Format as a professional post-mortem. Be specific and data-driven.
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Output ONLY the report text. No preamble.
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- type: tool
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action: enqueue_strategy
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optional: true
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params:
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company_slug: "crimson_leaf"
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project_slug: "incubation"
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task_type: "market_intelligence"
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content: "{learnings_message}"
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- type: document
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filename: "campaign_performance_review"
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- type: close
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rag_update: true
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adjudication:
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enabled: true
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pass_threshold: 60
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deliverable_type: analysis
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criteria:
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completeness:
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weight: 40
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description: "All performance dimensions assessed (reach, engagement, reviews, sentiment)"
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actionability:
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weight: 35
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description: "Learnings are specific and actionable for future books"
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accuracy:
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weight: 25
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description: "Assessment is grounded in the search evidence, not speculation"
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