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, beforelist-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.mdSame 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:
- Trigger — what event or pain made them look for a solution?
- Workflow gap — what were they doing before? What broke?
- Value delivered — what specific outcome did the product provide?
- 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:
- Read the existing hypothesis set
- Ask what changed — new win cases, campaign results, vertical knowledge
- Update, merge, or add hypotheses
- 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 designenrichment-design— hypotheses drive segmentation column designlist-segmentation— matches companies to hypotheses for tieringemail-prompt-building— hypotheses become P1 email anglesemail-generation— personalized openers per hypothesisemail-response-simulation— evaluates copy alignment with hypotheses