Skill: zkid18/extruct-list-building (version 1.0.0)

--- name: list-building description: > Build targeted company lists for outbound campaigns using Extruct. Use when the user wants to: (1) find companies matching an ICP, (2) build a prospect or outbound list, (3) search for companies by description, (4) discover companies in a market segment, (5) create a target account list, (6) research a competitive landscape, (7) find lookalike companies from a seed list. Triggers on: "find companies", "build a list", "company search", "prospect list", "target accounts", "outbound list", "discover companies", "ICP search", "lookalike search", "seed company". --- # 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 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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)