enrichment-design

name

enrichment-design

description

Design enrichment columns that bridge research hypotheses to list enrichment. Two modes: segmentation (columns that score hypothesis fit per company) and personalization (columns for company-specific hooks). Interactive column design with the user. Outputs ready-to-run column_configs for list-enrichment. Triggers on: "data points", "enrichment columns", "column design", "what to research", "data point builder", "build columns", "segmentation columns", "personalization columns".

Data Points Builder

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

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?"

Two Modes

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)

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])

Interactive Column Design

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

Step 1: Present the framework

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

Step 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]

Step 3: Refine together

Ask:

• "Any columns to add?"
• "Any to remove or merge?"
• "Should any prompts be more specific?"

Step 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

Step 5: Output column_configs

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

[
  {
    "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"
    }
  }
]

Column Design Guidelines

Agent Type Selection

Data point type	Agent type	Why
Factual data from the web (funding, launches, news)	research_pro	Needs web research
Classification from company profile	llm	Profile data is enough
Nuanced judgment (maturity, fit score)	research_reasoning	Needs chain-of-thought
People/org structure	linkedin	LinkedIn-specific

Output Format Selection

Data point type	Format	When
Free-form research	text	Open-ended questions
Score/rating	grade	1-5 scale assessments
Category	select	Mutually exclusive buckets
Multiple tags	multiselect	Non-exclusive tags
Structured data	json	Multiple related fields
Yes/no with evidence	json	{"match": bool, "evidence": str}

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

Reference Library

See references/data-point-library.md for ~20 pre-built column configs organized by use case.

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