hypothesis-building

Hypothesis Building

Generate testable pain hypotheses from what you already know — ICP, win cases, product value prop, and user knowledge of the target vertical. No API keys, no external research. Pure reasoning from context + conversation.

When to Use

• After context-building, before list-building
• When entering a new vertical and need to define what to search for
• When you know the vertical well enough to form hypotheses without deep research
• When you want a fast starting point before (optionally) validating with market-research

Inputs

Input	Source	Required
Context file	claude-code-gtm/context/{company}_context.md	yes
Target vertical	User input	yes
Additional knowledge	User input — industry experience, known pain points	recommended
Existing hypothesis set	claude-code-gtm/context/{vertical-slug}/hypothesis_set.md	no (for refine mode)

Output

claude-code-gtm/context/{vertical-slug}/hypothesis_set.md

Same path and format as market-research output — all downstream skills work unchanged.

Workflow

Step 1: Read context file

Read claude-code-gtm/context/{company}_context.md and extract:

• ICP profiles — who buys, company size, roles, geographies
• Win cases — why past customers bought, what pain triggered the purchase
• Product value prop — what the product does, key numbers
• Active hypotheses — any existing hypotheses already in the context file

Step 2: Gather vertical context from user

Ask the user:

Question	Why
What vertical are you targeting?	Defines the slug and scope
What geographies are you targeting?	Shapes search filters and regional pain points
What do you know about how these companies operate?	Seeds the hypothesis reasoning
What problems do you think your product solves for them?	Grounds hypotheses in real value
Any specific signals or patterns you've noticed?	Captures practitioner knowledge

Keep it conversational — don't force all questions if the user gives rich context upfront.

Step 3: Extract patterns from win cases

For each win case in the context file, identify:

1. Trigger — what event or pain made them look for a solution?
2. Workflow gap — what were they doing before? What broke?
3. Value delivered — what specific outcome did the product provide?
4. Transferability — does this pattern apply to the target vertical?

Map win case patterns to potential hypotheses for the new vertical.

Step 4: Draft hypotheses

Generate 3-7 hypotheses. Each hypothesis must have:

• Short name — 3-5 word label
• Description — 2-3 sentences explaining the pain, why it exists, and why the product fits
• Best fit — what type of company within the vertical this applies to most
• Search angle — 1-2 specific search queries or Discovery criteria to find companies matching this pain

Quality checks per hypothesis:

• Is it specific to a workflow or decision, not a vague industry trend?
• Can the recipient confirm it from their own experience?
• Does it connect to a product capability (not just a random pain)?
• Is the search angle concrete enough to drive a list-building query?

Step 5: Review with user

Present the full hypothesis set and ask:

• "Do these match your understanding of the vertical?"
• "Any hypotheses to add, merge, or remove?"
• "Are the search angles specific enough?"

Refine based on feedback. This is interactive — expect 1-2 rounds.

Step 6: Save

Save to claude-code-gtm/context/{vertical-slug}/hypothesis_set.md. Create the directory if it doesn't exist.

Output Format

## Hypothesis Set: [Vertical]

### #1 [Short name]
[2-3 sentence description — the pain, why it exists, why the product fits]
Best fit: [company type within the vertical]
Search angle: [1-2 search queries or Discovery criteria to find these companies]

### #2 [Short name]
[2-3 sentence description]
Best fit: [company type]
Search angle: [search queries or criteria]

...

The Search angle field is what makes this skill useful before list-building — it directly tells list-building what to search for.

Refine Mode

When a hypothesis set already exists at the output path, enter refine mode:

1. Read the existing hypothesis set
2. Ask what changed — new win cases, campaign results, vertical knowledge
3. Update, merge, or add hypotheses
4. Preserve hypothesis numbering where possible (downstream references use #N)

Key Difference from market-research

	hypothesis-building	market-research
Speed	Fast — minutes	Slow — external research queries
Source	Your own knowledge + context file	External research (e.g. Perplexity)
API keys	None	Requires API key for chosen provider
Best for	Verticals you know well, fast starts	Verticals you're entering blind
Output	hypothesis_set.md	hypothesis_set.md + sourcing_research.md

They're complementary: hypothesis-building first (define what you think), market-research later (validate with external data). Or skip market-research entirely if you know the vertical well.

Output Consumers

The hypothesis set is consumed by:

• list-building — search angles guide query design
• enrichment-design — hypotheses drive segmentation column design
• list-segmentation — matches companies to hypotheses for tiering
• email-prompt-building — hypotheses become P1 email angles
• email-generation — personalized openers per hypothesis
• email-response-simulation — evaluates copy alignment with hypotheses