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Research a target vertical’s pain points using deep research APIs. Distill findings into a numbered hypothesis set. Output is pure industry education - no email generation, no company matching.

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Trigger Phrases

“deep research”, “hypothesis set”, “research pain points”, “research vertical”, “sourcing research”, “pain mapping”, “industry problems”, “market research”, “educate me on”

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Environment

Provider selection and credentials are handled in Step 0 of the workflow.

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Workflow

1

Confirm provider and learn API

  1. Ask the user which deep research provider they want to use. If they’re unsure, Perplexity is a common choice.
  2. Fetch or read the provider’s API documentation and identify:
    • Chat/completions or search endpoint
    • Available models (pick the one with web search / citations)
    • Authentication method and credentials
    • Rate limits
  3. Ask for their API credentials and confirm access before proceeding
2

Define research scope

Read the company context file if it exists (claude-code-gtm/context/{company}_context.md) for ICP and existing hypotheses.Ask the user for:
InputRequiredExample
Target verticalyes”Mid-market logistics companies”
Specific sub-verticalsyes”3PL, freight brokerage, cold chain”
What we solve for themyes”Find potential partners and customers in fragmented markets”
Existing hypotheses to testnoFrom context file or user input
3

Run hypothesis-driven research

Do NOT run generic research. Run 3-4 focused queries, each targeting a different angle of the same problem. The queries should be specific enough to return actionable data points, not overviews.
Query design principles:
  • Each query should target ONE specific aspect of the pain
  • Ask for concrete data points, numbers, timelines, tool names
  • Ask for workflow descriptions, not abstractions
  • Ask for failure modes and workarounds
  • Keep queries vertical-agnostic in structure - the vertical comes from Step 1
Standard 3-query framework:Query 1 - Workflow pain: “What is the specific day-to-day workflow for [role] at [company type] when they [task we solve]? What tools do they use? Where do those tools fail? How long does each step take? Give concrete examples and data points.”Query 2 - Tool/database gaps: “How well do [existing tools] cover [target segment]? What percentage of the market do they miss? Why do [target companies] fall through the cracks? What data is wrong or stale? Give specific numbers.”Query 3 - Scaling problems: “What happens when [company type] tries to scale [process] beyond the initial [easy phase]? What breaks? What are the real-world failure stories? How do they work around it? What does it cost?”Optional Query 4 - Industry leaders: “Who are the recognized thought leaders in [vertical]? What have they said publicly about [pain area] in the last 12 months? Include quotes, conference talks, blog posts, LinkedIn posts. Focus on practitioners, not analysts.”Run each query through the chosen provider’s API.
4

Distill into numbered hypothesis set

Read all research responses and extract distinct, non-overlapping pain points. Each hypothesis should be:
  • Specific: tied to a concrete workflow step, tool failure, or scaling problem
  • Quantified: includes at least one data point (hours, percentages, dollar amounts)
  • Verifiable: the recipient can confirm it from their own experience
  • Non-obvious: teaches them something they may not have measured
Target: 5-7 hypotheses per vertical.
5

Industry Leaders (optional)

If Query 4 was run, compile an industry leaders section:
## Industry Leaders: [Vertical]

### [Leader Name] - [Title, Company]
- **Public stance on [pain area]:** [summary of their position]
- **Key quote:** "[direct quote]" - [source, date]
- **Relevance:** [why this matters for outreach or positioning]
This section helps with:
  • Email personalization (referencing what a leader said)
  • Positioning (aligning with or contrasting industry voices)
  • Content creation (informed takes on industry problems)
6

Save outputs

Save to the vertical context directory:
claude-code-gtm/context/{vertical-slug}/sourcing_research.md   ← full research output
claude-code-gtm/context/{vertical-slug}/hypothesis_set.md      ← distilled hypotheses
claude-code-gtm/context/{vertical-slug}/industry_leaders.md    ← leaders section (if Query 4 ran)
Create the directory if it doesn’t exist.

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Hypothesis Set Format

## Hypothesis Set: [Vertical]

### #1 [Short name]
[2-3 sentence description with data points]
Best fit: [what type of company this applies to most]

### #2 [Short name]
...

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Output Consumers

The hypothesis set is consumed by:
  • enrichment-design - to design enrichment columns that score/confirm hypotheses
  • list-segmentation - to match companies to hypotheses and assign tiers
  • email-generation - to personalize P1 openers per hypothesis
  • email-response-simulation - to evaluate whether email copy aligns with research

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vs. hypothesis-building

hypothesis-building generates hypotheses from your own knowledge (context file + user input) - fast, no API. This skill validates and enriches those hypotheses with external research. If a hypothesis set already exists at claude-code-gtm/context/{vertical-slug}/hypothesis_set.md, use it to focus research queries instead of starting from scratch.Typical flow: hypothesis-building first (define what you think) → market-research (validate with data). Or skip this skill entirely if you know the vertical well.

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When NOT to Use This Skill

  • If you already have a hypothesis set for the vertical - update it, don’t recreate
  • If you just need quick hypotheses from existing knowledge - use hypothesis-building
  • If the user just wants to write emails - use email-generation skill
  • If the user wants to find companies - use list-building skill
  • If the user wants to enrich a table - use list-enrichment skill
  • If the user wants to match companies to hypotheses - use list-segmentation skill

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