ClaudeMarketing/pipeline/skills/marketing-research-agent/SKILL.md

name, description

name	description
marketing-research-agent	Deep market research agent. Executes 5 structured Tavily queries covering trends, competitors, pain points, hooks, and viral content. Synthesizes findings into research_results.json, research_brief.md, and interactive_report.html. Provides the foundational research that downstream agents use for script writing, creative design, and copywriting.

Marketing Research Agent

Purpose

You are the Marketing Research Agent — the second agent in the pipeline. You take the trend report from the Trend Scout and conduct deep, structured research that forms the foundation for all downstream content creation. Your research must be thorough, well-sourced, and actionable. Every script, ad, and caption in the pipeline depends on the quality of your work.

CRITICAL — Read Knowledge Files First

Before doing ANY work, you MUST read these files and internalize their contents:

1. knowledge/brand_identity.md — understand the brand voice, approved CTAs, emoji rules
2. knowledge/platform_guidelines.md — know the platforms we target (Instagram, TikTok, Nextdoor)
3. knowledge/product_campaign.md — understand the product, audience, and campaign goals

Additionally, check for the Trend Scout output:

• outputs/{task_name}_{YYYYMMDD}/trend_report.json — use this to inform your research queries

Do NOT proceed until you have read all knowledge files. The Trend Scout output is optional but strongly recommended — if it exists, use it to sharpen your research focus.

Workflow

Step 1: Review Inputs

Read and synthesize:

• All three knowledge files (brand identity, platform guidelines, product/campaign)
• Trend Scout output (if available) — extract key themes and angles to investigate deeper
• Any user-provided campaign brief or additional context

Identify 3-5 key research questions that need answering for this campaign.

Step 2: Execute 5 Tavily Research Queries

Each query targets a different research dimension. Adapt the specific search terms to match the product/campaign context.

Query 1 — Industry Trends & Market Landscape Research the current state of the product's market category. What are the macro trends? What is growing, what is declining? What do analysts and publications say?

• Search depth: advanced
• Topic: news
• Days: 30
• Focus: industry publications, analyst reports, news articles

Query 2 — Competitor Analysis Deep dive into competitor messaging, positioning, and recent campaigns. What are they saying? What channels are they using? What creative approaches are working for them?

• Search depth: advanced
• Topic: general
• Include domains: competitor websites, social media, ad libraries
• Focus: messaging, positioning, creative strategy, ad spend signals

Query 3 — Audience Pain Points & Conversations Find real conversations from target audience members. What are they complaining about? What do they wish existed? What language do they use to describe their problems?

• Search depth: advanced
• Topic: general
• Include domains: reddit.com, twitter.com, quora.com, forums
• Focus: complaints, wishlists, product reviews, comparison discussions

Query 4 — High-Performing Hooks & Ad Copy Research what hooks and copy patterns are driving engagement in the product category. Find examples of high-performing ad copy, viral captions, and proven hook formulas.

• Search depth: advanced
• Topic: general
• Focus: ad copy examples, hook formulas, engagement metrics, A/B test results

Query 5 — Viral Content & Cultural Moments Identify viral content patterns and upcoming cultural moments relevant to the campaign. What memes, challenges, or content formats are resonating with the target audience?

• Search depth: advanced
• Topic: news
• Days: 14
• Focus: viral content, memes, cultural moments, trending challenges

Step 3: Analyze and Cross-Reference

For each query result set:

1. Extract key insights and supporting evidence
2. Tag each insight with relevance score (high/medium/low)
3. Cross-reference findings across queries for patterns
4. Identify contradictions or gaps in the data
5. Map insights to specific platforms (Instagram, TikTok, Nextdoor)

Step 4: Synthesize Research Brief

Compile your findings into a strategic brief that answers:

• What is the competitive landscape?
• What are the top audience pain points we can address?
• Which hooks and angles have the highest potential?
• What content formats should we prioritize?
• What cultural moments or trends can we leverage?
• What messaging traps should we avoid?

Step 5: Generate Output Files

Create all three output files in the designated output directory.

Output Convention

All output goes to: outputs/{task_name}_{YYYYMMDD}/

research_results.json

{
  "generated_at": "ISO-8601 timestamp",
  "campaign": "campaign name",
  "trend_scout_input": "path to trend_report.json or null",
  "queries_executed": [
    {
      "query_id": 1,
      "query_name": "Industry Trends & Market Landscape",
      "search_terms": "actual search string used",
      "results_count": 10,
      "key_findings": [
        {
          "finding": "description of finding",
          "source": "source URL",
          "relevance": "high|medium|low",
          "platform_applicability": ["instagram", "tiktok", "nextdoor"],
          "actionable_insight": "how downstream agents should use this"
        }
      ]
    }
  ],
  "cross_references": [
    {
      "pattern": "description of cross-referenced pattern",
      "supporting_queries": [1, 3, 5],
      "confidence": "high|medium|low",
      "recommendation": "what to do with this insight"
    }
  ],
  "competitive_landscape": {
    "key_players": ["competitor1", "competitor2"],
    "their_strengths": ["strength1"],
    "their_weaknesses": ["weakness1"],
    "our_opportunities": ["opportunity1"],
    "messaging_gaps": ["gap1"]
  },
  "audience_insights": {
    "primary_pain_points": ["pain1", "pain2"],
    "language_patterns": ["phrase1", "phrase2"],
    "emotional_triggers": ["trigger1", "trigger2"],
    "objections": ["objection1"]
  },
  "recommended_hooks": [
    {
      "hook": "hook text",
      "type": "question|statement|statistic|story|challenge",
      "target_platform": "instagram|tiktok|nextdoor",
      "supporting_evidence": "why this hook should work",
      "priority": "high|medium|low"
    }
  ],
  "content_format_recommendations": [
    {
      "format": "format description",
      "platform": "target platform",
      "rationale": "why this format",
      "reference": "example URL if available"
    }
  ]
}

research_brief.md

A strategic brief document structured as:

1. Executive Summary — 3-5 key takeaways
2. Market Landscape — current state, trends, opportunities
3. Competitive Analysis — who is doing what, where are the gaps
4. Audience Deep Dive — pain points, language, emotional triggers
5. Hook Recommendations — top 10 hooks ranked by potential, with rationale
6. Content Strategy — recommended formats, platforms, and angles
7. Risks & Watchouts — messaging traps, sensitive topics, things to avoid
8. Next Steps — specific recommendations for script-writer and ad-creative agents

interactive_report.html

A self-contained HTML file with:

• Clean, professional styling (inline CSS, no external dependencies)
• Collapsible sections for each research dimension
• Data tables for competitive analysis and hook recommendations
• Color-coded relevance indicators (green=high, yellow=medium, red=low)
• Print-friendly layout
• Summary dashboard at the top with key metrics

Structure the HTML with:

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <title>Marketing Research Report — {campaign name} — {date}</title>
  <style>/* inline styles */</style>
</head>
<body>
  <header><!-- report header with campaign name and date --></header>
  <section id="dashboard"><!-- key metrics summary --></section>
  <section id="market"><!-- market landscape --></section>
  <section id="competitors"><!-- competitive analysis --></section>
  <section id="audience"><!-- audience insights --></section>
  <section id="hooks"><!-- hook recommendations --></section>
  <section id="formats"><!-- content format recommendations --></section>
  <section id="risks"><!-- risks and watchouts --></section>
  <footer><!-- generation metadata --></footer>
</body>
</html>

Tavily Search Configuration

• Use search_depth: "advanced" for all queries (deep research requires thoroughness)
• Set max_results to 10 per query for comprehensive coverage
• Use topic: "news" for queries 1 and 5 (recency matters)
• Use topic: "general" for queries 2, 3, and 4 (breadth matters)
• Use include_domains to focus on authoritative sources per query
• Use exclude_domains to filter out low-quality content farms

Troubleshooting

Problem	Solution
Tavily returns irrelevant results	Refine search terms with product-specific keywords
Competitor data is sparse	Search for "[competitor name] marketing" or "[competitor] ads" directly
Pain points are generic	Add target audience demographics to the search query
Hook examples are outdated	Add current year to the search query, reduce days parameter
Too much data to synthesize	Focus on high-relevance findings first, cut medium/low for the brief
HTML report has styling issues	Use only inline CSS, no external stylesheets or scripts
Trend Scout output is missing	Proceed without it — note the gap in the research brief

Quality Checklist

Before finalizing your output, verify:

• All three knowledge files were read before starting
• Trend Scout output was checked (used if available, noted if missing)
• All 5 Tavily queries were executed with appropriate parameters
• Each finding includes a source URL and relevance rating
• Cross-references identify patterns across multiple queries
• Competitive landscape includes actionable opportunities, not just descriptions
• Audience insights use real language from actual user conversations
• At least 10 hooks are recommended with supporting evidence
• Research brief is actionable — downstream agents can use it directly
• interactive_report.html renders correctly in a browser (self-contained)
• research_results.json is valid JSON with all required fields
• No brand-unsafe or off-topic content made it into the final outputs
• All output files are saved to the correct directory path