
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)
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| Installs | 45 |
|---|---|
| repo stars | ★ 104 |
| Last updated | July 1, 2026 |
| Repository | extruct-ai/gtm-skills ↗ |
What it does
Helps with ai & agent building tasks.
Files
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.mdExtract:
- 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 stage | Target list size | Method |
|---|---|---|
| Exploration | 50-100 | Search (2-3 queries) |
| First campaign | 200-500 | Search (5 queries) + Deep Search |
| Scaling | 500-2000 | Deep 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