startup-positioning

# Startup Positioning

Market positioning strategy that produces a complete positioning document, Moore + Neumeier positioning statements, competitive alternatives map, and market category analysis. Built on April Dunford's framework, enriched with JTBD discovery and stress-tested with Neumeier's Onliness Test.

## How It Works

```
INTAKE → RESEARCH (2 sequential waves) → POSITIONING SYNTHESIS
```

The process: understand the product and its customers, research competitive alternatives and market context, then build positioning through Dunford's 5+1 components. Typical runtime: 10-15 minutes in Claude Code (parallel agents), 20-30 minutes in Claude.ai (sequential).

### Language

Default output language is **English**. If the user writes in another language or explicitly requests one, use that language for all outputs instead.

---

## Phase 0: Resume Check

Before anything else, check if a `PROGRESS.md` created by this skill exists in the working directory or a project subdirectory (the skill name field says `startup-positioning`). If it does, read it and resume from the last incomplete phase. Tell the user: "I found progress from a previous session. You completed [phases]. Picking up from [next phase]."

If no progress file exists — or the one found belongs to a different skill — start from Phase 1.

---

## Phase 1: Intake

Short and focused — 1-2 rounds of questions. The goal is enough context to research alternatives and build positioning.

### Check for Prior Work

Before asking questions, check if prior sessions have been completed. Look for these files in the working directory or subdirectories:

**From startup-design:**
- `00-intake/brief.md` — product description and context
- `01-discovery/competitor-landscape.md` — competitor profiles
- `01-discovery/target-audience.md` — customer personas, pain points
- `02-strategy/positioning.md` — initial positioning work

**From startup-competitors:**
- `intake.md` — product and market context
- `competitors-report.md` — strategic competitive analysis
- `battle-cards/` — per-competitor profiles
- `pricing-landscape.md` — pricing analysis

If these files exist, read them and use the data as a head start:
- Extract the product description, known competitors, and customer pain points
- Use competitor profiles and battle cards to seed the competitive alternatives map
- Pull any existing positioning work as a starting hypothesis to test, not a conclusion to keep
- Use customer language and pain points to inform JTBD discovery

Tell the user: "I found data from a previous session. I'll use it as a starting point for positioning analysis."

Skip redundant intake questions. Go straight to research if prior data is sufficient.

### What to Ask (if no prior data exists)

**Round 1 — Core context:**
- What's your product? (one sentence is fine)
- What problem does it solve and for whom?
- What do your customers do today instead of using you? (alternatives, workarounds, doing nothing)
- Who are your best existing customers? (if any — describe them, not demographics)

**Round 2 — Sharpening (only if needed):**
- How is your product different from the alternatives you mentioned?
- Have you tried positioning before? What didn't work?
- Are there competitors you're often compared to?

Don't over-interview. If the user gives a clear description upfront, move to research. The positioning process itself will surface what matters.

### Output

Save to `{project-name}/intake.md` — a brief summary of the product, problem, alternatives, and customers. If built on prior session data, note the source files used. Project name: kebab-case (e.g., `ai-email-assistant`).

Create `{project-name}/PROGRESS.md` with: project name, skill name (`startup-positioning`), start date, language, research mode (Live / Knowledge-Based), and a phase checklist. Update it after each phase completes. If PROGRESS.md already exists from a previous session, resume from the last incomplete phase.

---

## Phase 1.5: Research Depth Assessment

After intake, assess market complexity and present the Research Depth recommendation to the user.

> **Reference:** Read `references/research-scaling.md` for the complexity scoring matrix, tier definitions, wave configurations, and the user communication template.

### Process

1. Score three factors from the intake: market breadth (1-3), known competitors (1-3), geographic scope (1-3)
2. Sum the scores (range 3-9) and map to a tier: Light (3-4), Standard (5-7), Deep (8-9)
3. Present the Research Depth table to the user (see `research-scaling.md` for the exact template)
4. Wait for user response: **light**, **deep**, or **ok** to accept the recommendation
5. Record the selected tier in PROGRESS.md

The selected tier determines the number of agents per wave and search rounds per agent in Phase 2. See `research-scaling.md` for exact wave configurations per tier.

---

## Phase 2: Research

Two sequential research waves exploring competitive alternatives and market context — agents within a wave run in parallel, and Wave 2 builds on Wave 1's findings. Together they provide the raw material for Dunford's 5+1 positioning components.

### Environment Detection

Check if the `Agent` tool is available:

- **Agent tool available (Claude Code):** Spawn all agents within each wave in parallel. This is faster.
- **Agent tool NOT available (Claude.ai, web):** Execute research sequentially, following the same templates. Same depth, just slower.

### Web Search

This skill requires WebSearch for real data. If WebSearch is unavailable or denied, fall back to **Knowledge-Based Mode**: use training data, mark all findings with **[Knowledge-Based — verify independently]**, and reduce confidence ratings by one level. Note the mode in PROGRESS.md.

> **Reference:** Read `references/research-principles.md` before starting any wave. It defines source quality tiers, cross-referencing rules, and how to handle data gaps.

### Wave 1: Competitive Alternatives & Customer Cont