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Bridge the gap between research hypotheses and table enrichment. Define WHAT to research about each company before running enrichment.

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Trigger Phrases

“data points”, “enrichment columns”, “column design”, “what to research”, “data point builder”, “build columns”, “segmentation columns”, “personalization columns”

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When to Use

  • After market-research has produced a hypothesis set
  • Before list-enrichment - this skill designs the columns, that skill runs them
  • When the user says “what should we research about these companies?”

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Two Modes

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Mode 1: Segmentation

Goal: Design columns that score or confirm hypothesis fit per company. Input: Hypothesis set (from market-research or context file) Process:
  1. Read the hypothesis set
  2. For each hypothesis, propose 1-2 columns that would confirm or deny fit
  3. Discuss with user - refine, add, remove
  4. Output final column_configs
Example: If hypothesis is “Database blind spot - 80-90% of targets invisible to standard tools”:
  • Column: “Data Infrastructure Maturity” (select: [“No CRM”, “Basic CRM”, “Full stack”])
  • Column: “Digital Footprint Score” (grade: 1-5)

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Mode 2: Personalization

Goal: Design columns that capture company-specific hooks for email personalization. Input: Target list + what the user wants to personalize on Process:
  1. Ask what hooks matter for this campaign (leadership quotes, recent launches, hiring signals, tech stack, etc.)
  2. Propose 2-4 columns with prompts
  3. Discuss with user - refine
  4. Output final column_configs
Example: For personalization hooks:
  • Column: “Recent Product Launch” (text: describe any product launched in last 6 months)
  • Column: “Leadership Public Statement” (text: find a public quote from CEO/CTO about [topic])

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Interactive Column Design

Do NOT just generate columns silently. Walk through this with the user.
1

Present the framework

Show the user the two modes and ask which applies (or both).
2

Propose initial columns

Based on hypotheses or user input, propose 3-5 columns. For each, show:
Column: [name]
Type: [output_format]
Agent: [research_pro | llm]
Prompt: [the actual prompt text]
Why: [what this tells us for segmentation/personalization]
3

Refine together

Ask:
  • “Any columns to add?”
  • “Any to remove or merge?”
  • “Should any prompts be more specific?”
4

Confirm column budget

Guidance:
  • 3-5 columns is the sweet spot
  • 6-7 is acceptable if each serves a clear purpose
  • 8+ adds noise - push back and suggest merging
5

Output column_configs

Generate the final column configs as a JSON array ready for list-enrichment.

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Column Config Format

[
  {
    "kind": "agent",
    "name": "Column Display Name",
    "key": "column_key_snake_case",
    "value": {
      "agent_type": "research_pro",
      "prompt": "Research prompt using {input} for domain...",
      "output_format": "text"
    }
  }
]

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Column Design Guidelines

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Agent Type Selection

Data point typeAgent typeWhy
Factual data from the web (funding, launches, news)research_proNeeds web research
Classification from company profilellmProfile data is enough
Nuanced judgment (maturity, fit score)research_reasoningNeeds chain-of-thought
People/org structurelinkedinLinkedIn-specific

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Output Format Selection

Data point typeFormatWhen
Free-form researchtextOpen-ended questions
Score/ratinggrade1-5 scale assessments
CategoryselectMutually exclusive buckets
Multiple tagsmultiselectNon-exclusive tags
Structured datajsonMultiple related fields
Yes/no with evidencejson{"match": bool, "evidence": str}

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Prompt Writing Tips

  • Always include {input} for the company domain
  • Be specific about output format in the prompt itself
  • Include fallback: “If not found, return N/A” or “If unclear, return ‘Unknown’”
  • For select/multiselect: list the labels in the prompt too
  • For hypothesis scoring: reference the specific hypothesis in the prompt
  • Keep prompts under 200 words

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Example Columns

{
  "kind": "agent",
  "name": "Hypothesis 1 Fit",
  "key": "h1_fit",
  "value": {
    "agent_type": "research_reasoning",
    "prompt": "Grade how well {input} matches this hypothesis: 'Database blind spot - relies on manual outreach to non-indexed partners'. Look for: (1) fragmented supplier/partner base, (2) limited online presence of targets, (3) reliance on events/intros. Return 1-5 where 5 = perfect fit.",
    "output_format": "grade"
  }
}

{
  "kind": "agent",
  "name": "Recent Product Launch",
  "key": "recent_launch",
  "value": {
    "agent_type": "research_pro",
    "prompt": "Find any product or feature launched by {input} in the last 6 months. Include: product name, launch date, brief description. If none found, return 'N/A'.",
    "output_format": "text"
  }
}

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Output Handoff

After column design is complete:
  1. Present the final column_configs JSON to the user
  2. Tell the user: “These configs are ready for list-enrichment. Run that skill with your table ID and these columns.”
  3. If the user wants to run immediately, hand off to list-enrichment workflow

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