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