
startup-positioning
Market positioning strategy using the April Dunford framework, enriched with JTBD discovery, Moore positioning statement, and Neumeier's Onliness Test. Produces a complete positioning document, positioning statement, competitive alternatives map, and market category analysis. Use when the user wants to define or refine their market positioning, find their unique position, differentiate from competitors, craft a positioning statement, choose a market category, or figure out "how should we position this product." Triggers for "positioning", "how to position", "market position", "differentiation strategy", "positioning statement", "competitive positioning", "category strategy", "where do we fit in the market", "how are we different", "unique value proposition", or any request to define, sharpen, or rethink positioning. Works standalone — no prior startup-design or startup-competitors session needed, but leverages their output if available.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/ferdinandobons/startup-skill --skill startup-positioning- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads startup-positioning automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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 context01-discovery/competitor-landscape.md— competitor profiles01-discovery/target-audience.md— customer personas, pain points02-strategy/positioning.md— initial positioning work
From startup-competitors:
intake.md— product and market contextcompetitors-report.md— strategic competitive analysisbattle-cards/— per-competitor profilespricing-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.mdfor the complexity scoring matrix, tier definitions, wave configurations, and the user communication template.
Process
- Score three factors from the intake: market breadth (1-3), known competitors (1-3), geographic scope (1-3)
- Sum the scores (range 3-9) and map to a tier: Light (3-4), Standard (5-7), Deep (8-9)
- Present the Research Depth table to the user (see
research-scaling.mdfor the exact template) - Wait for user response: light, deep, or ok to accept the recommendation
- 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.mdbefore starting any wave. It defines source quality tiers, cross-referencing rules, and how to handle data gaps.
Wave 1: Competitive Alternatives & Customer Context
Reference: Read
references/research-wave-1-alternatives.mdfor agent templates.
Two agents (or two sequential blocks):
A1: Alternative Mapping (JTBD Lens) — Map ALL competitive alternatives, not just direct competitors. Include: direct competitors, adjacent tools competing for the same budget, manual processes, spreadsheets, hiring someone, doing nothing / status quo. For each: what job does the customer hire it for, where does it fall short, what triggers switching? The goal is the full set of things your product replaces.
A2: Customer Intelligence — Mine voice-of-customer data: reviews, forums, communities. Extract: pain points with current alternatives, exact language customers use, what "better" means to them, best-fit customer profile (who gets the most value fastest), switching triggers (what makes someone finally change). Build a language map — the words customers use to describe their problem and desired outcome.
Wave 2: Market Frame & Trends
Reference: Read
references/research-wave-2-market-frame.mdfor agent templates.
Two agents (or two sequential blocks):
B1: Market Category Analysis — Identify 3-5 candidate market categories. For each: what do buyers expect from this category, who are the leaders, what's the competitive dynamic, how mature is it? Apply Dunford's category types: head-to-head (existing category), big fish/small pond (subcategory), or category creation. Assess which frame makes your unique strengths matter most.
B2: Trend & Timing Analysis — Identify relevant trends: technology shifts, behavioral changes, regulatory moves. For each: is it real or hype, how does it affect buyer expectations, does it make your positioning stronger or weaker? Assess timing — are you early, on-time, or late to the trend? Only include trends that genuinely change how buyers evaluate solutions.
Post-Research Checkpoint
After both waves complete, before synthesis, briefly present what the research found to the user: the competitive alternative landscape (how many direct, adjacent, status quo), the strongest customer pains, and the most promising category candidates. Ask: "Does this align with your expectations? Anything to adjust before I synthesize the positioning?"
Keep it to one message — this is a quick alignment check, not a full report.
Phase 3: Positioning Synthesis
Reference: Read
references/research-synthesis.mdfor synthesis protocol and Dunford process details.
After the checkpoint, build positioning through Dunford's 5+1 components in order. The sequence matters — each step builds on the previous.
Positioning is a reasoning problem, not a fill-in-the-blanks exercise. The "only" in the Onliness Test has to be true, and finding a frame where it's both true and valuable takes real thought — you're searching for the angle that makes the product's strengths matter most to the right buyer. Before committing to a frame, think hard about how each candidate category changes what the product gets compared against and whether the differentiation still holds. If the model supports extended thinking, this is where to spend it; a forced or generic position is worse than none.
The 5+1 Components
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Competitive Alternatives — From Wave 1. What would customers use if your product didn't exist? This is the anchor — positioning is always relative.
-
Unique Attributes — What do you have that the alternatives lack? Be specific and honest. Features, architecture, team expertise, business model, speed — anything defensible.
⏸ PAUSE — User Input Required. Present the research-derived attributes to the user. Ask them to confirm, add, or remove before proceeding to Value Themes. The founder knows capabilities that research can't surface.
-
Value Themes — Translate each unique attribute into a customer outcome. Attribute → "so what?" → value. Group related attributes into 2-3 value themes. Use customer language from Wave 1's language map.
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Best-Fit Customers — From Wave 1 customer intelligence. Who cares most about your value themes? Define by characteristics that make them care, not demographics. These customers should be reachable, recognizable, and willing to pay.
-
Market Category — From Wave 2. Choose the category frame that makes your value obvious. Present 3-5 options with trade-offs. Recommend one. The right category triggers the right buyer expectations.
-
Trend Overlay (optional) — From Wave 2. Only include if a genuine trend makes your positioning stronger. Forced trend alignment is worse than none.
Validation
Two stress tests before finalizing:
Neumeier Onliness Test:
Basic form:
"Our [product] is the only [category] that [differentiator]."
Extended form (6 elements — WHAT/HOW/WHO/WHERE/WHY/WHEN):
"Our [product] is the only [category] that [differentiator] for [target] who [need] in [context]."
If you can't fill the basic form convincingly — if "only" feels like a stretch — the positioning is too weak. Iterate.
Ries/Trout Mental Ladder:
- Is it simple enough to remember?
- Does it claim one clear rung?
- Is that rung available (not owned by a competitor)?
- Can you explain it in one sentence?
If either test fails, revisit the 5+1 components. Don't ship weak positioning.
Output Files
Every deliverable file must start with a standardized header: # {Title}: {product} followed by *Skill: startup-positioning | Generated: {date}*. Every deliverable must end with Red Flags, Yellow Flags, and Sources sections (see templates in references/research-synthesis.md).
{project-name}/positioning-doc.md — The main deliverable:
- Executive summary (positioning in 3 sentences)
- The 5+1 components with supporting evidence
- Strength assessment per component (Strong / Moderate / Needs Work)
- Strategic recommendations and next steps
- Data gaps & limitations
{project-name}/positioning-statement.md — Statements and messaging:
- Moore template: "For [target] who [need], [product] is a [category] that [benefit]. Unlike [alternative], we [differentiator]."
- Neumeier Onliness Statement (basic + extended)
- Elevator pitch (30-second version)
- Tagline candidates with stress-tested "Possible Misread" column
- One-liner variants for different channels (GitHub, marketplace, social, elevator)
- Freemium positioning (if applicable)
{project-name}/competitive-alternatives.md — Complete alternatives map:
- All alternatives (direct, adjacent, manual, status quo)
- Per alternative: job hired for, strengths, shortcomings, switching triggers
- Your unique attributes vs. each alternative
{project-name}/market-category-analysis.md — Category strategy:
- 3-5 candidate categories with buyer expectations
- Category type assessment (head-to-head / subcategory / creation)
- Recommendation with reasoning
- Implementation (category label, tagline direction, buyer expectation alignment)
- Red flags and yellow flags
{project-name}/messaging-implications.md — Bridge from positioning to copy:
- Messaging hierarchy (what to communicate first, second, third)
- Category label (exact phrase to use everywhere)
- Value anchor (what to compare value to, separate from category)
- Customer language vs. category language map (which words are customer verbs, which are category nouns)
- Words to use / avoid
- Social proof guidelines
- Freemium positioning (if applicable)
Raw Data
Each agent saves its raw output to {project-name}/raw/. The synthesis phase reads these raw files and produces the polished deliverables above. Agents must NOT write directly to deliverable paths — raw and synthesized output are separate.
Raw research files:
alternative-mapping.mdcustomer-intelligence.mdmarket-categories.mdtrends-timing.md
Phase 3.5: Research Verification
After all positioning deliverables are written, run a verification pass.
Reference: Read
references/verification-agent.mdfor the full verification protocol, universal checks, and skill-specific checks.
Process
- Spawn agent V1: Verification — it reads all deliverable files and checks for: unlabeled claims, internal contradictions, confidence rating consistency, missing data gaps, missing flags, stale data, and duplicate-source false corroboration
- V1 also runs startup-positioning-specific checks: positioning statement vs. research data, JTBD vs. customer intelligence, cross-deliverable coherence, validation test integrity
- V1 produces
{project-name}/verification-report.md - If Critical issues found: Pause and present issues to the user. Ask: fix first, or proceed as-is?
- If only Warnings/Info: Show one-line summary
In Claude.ai or when Agent tool is unavailable, run the verification checks yourself in the main conversation following the same protocol.
Honesty Protocol
Reference: Read
references/honesty-protocol.mdfor full protocol and anti-pattern details.
Positioning is only useful if it's honest. Core rules apply (label claims, quantify, declare gaps), plus positioning-specific additions:
- No aspirational positioning. Position on what you ARE, not what you hope to become. Aspirational positioning crumbles at first customer contact.
- Challenge "we're unique." The Onliness Test must be genuinely convincing. If it reads like marketing fluff, iterate.
- Research wins over narrative. When customer data contradicts internal beliefs about positioning, the data wins.
- Flag category creation risk. Most startups can't afford to educate a market. Default to existing categories unless the evidence is overwhelming.
| Anti-Pattern | What It Looks Like | What to Say |
|---|---|---|
| "We're for everyone" | No target segment defined | "If you're for everyone, you're for no one. Who cares MOST?" |
| Feature-based positioning | Leading with features not outcomes | "Customers don't buy features. What outcome do they get?" |
| Aspirational positioning | "We'll be the AI-powered..." | "Position on what you deliver today, not the roadmap." |
| Category-of-one | Inventing a category to avoid comparison | "New categories cost millions. Is there an existing frame?" |
| Copycat positioning | Same message as the market leader | "Find genuinely different ground — you can't out-position the leader." |
See references/honesty-protocol.md for the full anti-pattern table (7 entries) and detailed protocol.
Reference Files
Read only what you need for the current phase.
| File | When to Read | ~Lines | Purpose |
|---|---|---|---|
honesty-protocol.md | Start of session | ~73 | Full honesty protocol with anti-patterns |
research-principles.md | Before starting Phase 2 | ~65 | Source quality, cross-referencing, data gaps |
research-wave-1-alternatives.md | When running Wave 1 | ~235 | Agent templates for alternatives + customer intel |
research-wave-2-market-frame.md | When running Wave 2 | ~210 | Agent templates for categories + trends |
research-synthesis.md | After both waves complete | ~419 | Synthesis protocol, Dunford process, validation tests, messaging implications |
frameworks.md | During Phase 3 | ~132 | Dunford/Moore/Neumeier/JTBD/Ries reference |
research-scaling.md | After intake, before Phase 2 | ~96 | Complexity scoring, tier definitions, wave configurations |
verification-agent.md | After synthesis | ~128 | Verification protocol, universal + skill-specific checks |
FAQ
What does the startup-positioning skill do?
Market positioning strategy using the April Dunford framework, enriched with JTBD discovery, Moore positioning statement, and Neumeier's Onliness Test. Produces a complete positioning document, positioning statement, competitive alternatives map, and market category analysis. Use when the user wants to define or refine their market positioning, find their unique position, differentiate from competitors, craft a positioning statement, choose a market category, or figure out "how should we position this product." Triggers for "positioning", "how to position", "market position", "differentiation strategy", "positioning statement", "competitive positioning", "category strategy", "where do we fit in the market", "how are we different", "unique value proposition", or any request to define, sharpen, or rethink positioning. Works standalone — no prior startup-design or startup-competitors session needed, but leverages their output if available.
How do I install startup-positioning?
Run npx skills add https://github.com/ferdinandobons/startup-skill --skill startup-positioning in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/startup-positioning/ (personal) or .claude/skills/startup-positioning/ (project).
Which AI agents can use startup-positioning?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is startup-positioning safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains startup-positioning?
It is published by ferdinandobons in ferdinandobons/startup-skill. It has 617 recorded installs.
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