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