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List Building

  • 45 installs
  • 104 repo stars
  • Updated July 1, 2026
  • extruct-ai/gtm-skills

Helps with ai & agent building tasks.

About

list-building is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

  • list-building
  • AI & Agent Building
  • AI-coding skill

List Building by the numbers

  • 45 all-time installs (skills.sh)
  • Ranked #7,680 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Jul 31, 2026 (Skillselion catalog sync)
npx skills add https://github.com/extruct-ai/gtm-skills --skill list-building

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Listed on Skillselion
Installs45
repo stars104
Last updatedJuly 1, 2026
Repositoryextruct-ai/gtm-skills

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

List Building

Build company lists using Extruct, guided by a decision tree. Reads from the company context file for ICP and seed companies.

Extruct API Operations

This skill delegates all Extruct API calls to the extruct-api skill.

For all Extruct API operations, read and follow the instructions in skills/extruct-api/SKILL.md.

All company search, lookalike search, deep search, table creation, row uploads, and enrichment runs are handled by the extruct-api skill. This skill focuses on what to search for and why — the extruct-api skill handles the how.

Decision Tree

Before running any queries, determine the right approach:

Have a seed company from win cases or context file?
  YES → Lookalike Search (pass seed domain)
  NO  ↓

New vertical, need broad exploration?
  YES → Semantic Search (3-5 queries from different angles)
  NO  ↓

Need qualification against specific criteria?
  YES → Deep Search (criteria-scored async research)
  NO  ↓

Need maximum coverage?
  YES → Combine Search + Deep Search (~15% overlap expected)

Before You Start

Read the company context file if it exists:

claude-code-gtm/context/{company}_context.md

Extract:

  • ICP profiles — for query design and filters
  • Win cases — for seed companies in lookalike mode
  • DNC list — domains to exclude from results. If no DNC list exists in the context file, ask the user: (1) run an Extruct search for competitors to auto-populate, (2) accept a CSV of existing customers/partners, or (3) skip for now

Also check for a hypothesis set at claude-code-gtm/context/{vertical-slug}/hypothesis_set.md. If it exists, use the Search angle field from each hypothesis to design search queries — these are pre-defined query suggestions tailored to each pain point.

Method 1: Lookalike Search

Use when you have a seed company (from win cases, existing customers, or user input). Delegate to the extruct-api skill to run a lookalike search with the seed domain.

When to use:

  • You have a happy customer and want more like them
  • Context file has win cases with domains
  • User says "find companies similar to X"

Tips:

  • Run multiple lookalike searches with different seed companies for broader coverage
  • Combine with filters to constrain geography or size
  • Deduplicate across runs by domain

Method 2: Semantic Search — Fast, Broad

Delegate to the extruct-api skill to run semantic company search queries.

Query strategy:

  • Write 3-5 queries per campaign, each from a different angle on the same ICP
  • Describe the product/use case, not the company type
  • Deduplicate across queries by domain — overlap is expected
  • Target 200-800 companies total across all queries

Method 3: Deep Search — Deep, Qualified

Delegate to the extruct-api skill to create and run deep search tasks.

Query strategy:

  • Write queries like a job description — 2-3 sentences describing the ideal company
  • Use criteria to auto-qualify — each company gets graded 1-5 per criterion
  • Default 50 results for first pass; expand after quality review
  • Use up to 5 criteria per task; keep criteria focused and non-overlapping
  • Run separate tasks for different ICP segments

Upload to Table

After collecting results, delegate to the extruct-api skill to create a company table and upload domains. Extruct auto-enriches each domain with a Company Profile.

Re-run After Enrichment

After the list-enrichment skill adds data points to this list, consider re-running list building using enrichment insights as Deep Search criteria. For example:

  • If enrichment reveals that "companies using legacy ERP" are the best fit, create a Deep Search task with that as a criterion
  • If enrichment shows a geographic cluster, run a Search with tighter geo filters
  • This creates a feedback loop: list → enrich → learn → refine list

Result Size Guidance

Campaign stageTarget list sizeMethod
Exploration50-100Search (2-3 queries)
First campaign200-500Search (5 queries) + Deep Search
Scaling500-2000Deep Search (high result count) + multiple Search

Workflow

1. Read context file for ICP, seed companies, and DNC list 2. Follow the decision tree to pick the right method 3. Draft queries (3-5 for Search, 1-2 for Deep Search) 4. Delegate to the extruct-api skill to run queries and collect results 5. Deduplicate across all results by domain 6. Remove DNC domains 7. Delegate to the extruct-api skill to upload to a company table 8. Add agent columns if user needs custom research 9. Ask user for preferred output: Extruct table link, local CSV, or both

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