market-research

name

market-research

description

Research a target vertical's pain points using deep research APIs and distill findings into a numbered hypothesis set. Pure industry education tool — decoupled from email generation. Use when the user wants to understand a market before outreach, form hypotheses about a vertical, or build an industry knowledge base. Triggers on: "deep research", "hypothesis set", "research pain points", "research vertical", "sourcing research", "pain mapping", "industry problems", "market research", "educate me on".

Market Problems Deep Research

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.

Environment

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

Workflow

Step 0: 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 — see workflow below for query design patterns.
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

Step 1: Define the 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:

Input	Required	Example
Target vertical	yes	"Mid-market logistics companies"
Specific sub-verticals	yes	"3PL, freight brokerage, cold chain"
What we solve for them	yes	"Find potential partners and customers in fragmented markets"
Existing hypotheses to test	no	From context file or user input

Step 2: 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

Run each query through the chosen provider's API (from Step 0).

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 and public statements: "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."

Step 3: 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

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]
...

Target: 5-7 hypotheses per vertical.

Step 4 (optional): Industry Leaders

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)

Step 5: 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.

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

Relationship to 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.

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