Product marketing skill for positioning, GTM strategy, competitive intelligence, and product launches. Covers April Dunford positioning, ICP definition, competitive battlecards, launch playbooks, and international market entry.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Product marketing patterns for positioning, GTM strategy, and competitive intelligence.
Before building the PMM artifact, confirm these inputs. If any is unknown or vague, ASK — do not assume:
Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
Define ideal customer profile for targeting:
| Dimension | Target Range | Rationale |
|---|---|---|
| Employees | 50-5000 | Series A sweet spot |
| Revenue | $5M-$500M | Budget available |
| Industry | SaaS, Tech, Services | Product fit |
| Geography | US, UK, DACH | Market priority |
| Funding | Seed to Growth | Willing to adopt |
Economic Buyer (signs contract):
Technical Buyer (evaluates product):
User/Champion (advocates internally):
Develop positioning using April Dunford methodology:
FOR [target customer]
WHO [statement of need]
THE [product] IS A [category]
THAT [key benefit]
UNLIKE [competitive alternative]
OUR PRODUCT [primary differentiation]
Template: [Product] helps [Target Customer] [Achieve Goal] by [Unique Approach]
Example: "Acme helps mid-market SaaS teams ship 2x faster by automating project workflows with AI"
| Level | Content | Example |
|---|---|---|
| Headline | 5-7 words | "Ship faster with AI automation" |
| Subhead | 1 sentence | "Automate workflows so teams focus on what matters" |
| Benefits | 3-4 bullets | Speed, quality, collaboration, cost |
| Features | Supporting evidence | AI automation → 10 hrs/week saved |
| Proof | Social proof | Customer logos, stats, case studies |
Build competitive knowledge base:
| Tier | Definition | Examples |
|---|---|---|
| 1 | Direct competitor, same category | [Competitor A, B] |
| 2 | Adjacent solution, overlapping use case | [Alt Solution C, D] |
| 3 | Status quo (what they do today) | Spreadsheets, manual, in-house |
COMPETITOR: [Name]
OVERVIEW: Founded [year], Funding [stage], Size [employees]
POSITIONING:
- They say: "[Their claim]"
- Reality: [Your assessment]
STRENGTHS:
1. [What they do well]
2. [What they do well]
WEAKNESSES:
1. [Where they fall short]
2. [Where they fall short]
OUR ADVANTAGES:
1. [Your advantage + evidence]
2. [Your advantage + evidence]
WHEN WE WIN:
- [Scenario where you win]
WHEN WE LOSE:
- [Scenario where they win]
TALK TRACK:
Objection: "[Common objection]"
Response: "[Your response]"
Track monthly:
Plan launches by tier:
| Tier | Scope | Prep Time | Budget |
|---|---|---|---|
| 1 | New product, major feature | 6-8 weeks | $50-100k |
| 2 | Significant feature, integration | 3-4 weeks | $10-25k |
| 3 | Small improvement | 1 week | <$5k |
Execute major product launch:
| Metric | Leading (Daily) | Lagging (Weekly) |
|---|---|---|
| Traffic | Landing page visitors | - |
| Engagement | Demo requests, signups | Feature adoption % |
| Pipeline | MQLs generated | SQLs, pipeline $ |
| Revenue | - | Deals closed, revenue |
Equip sales team with PMM assets:
| Slide | Content |
|---|---|
| 1-2 | Title, agenda |
| 3-4 | Company intro, problem statement |
| 5-7 | Solution, key benefits, demo |
| 8-10 | Differentiation, case study, pricing |
| 11-12 | Implementation, support, next steps |
1. Intro (2 min): Who we are, agenda
2. Discovery (5 min): Their needs, pain points
3. Demo (20 min): Product focused on their use case
4. Q&A (10 min): Objection handling
5. Next steps (3 min): Trial, POC, proposal
| Handoff | Frequency | Content |
|---|---|---|
| Weekly sync | 30 min | Win/loss, competitive, new assets |
| Monthly enablement | 60 min | Product updates, training |
| Quarterly review | Half-day | Results, strategy, planning |
Enter new markets systematically:
| Market | Timeline | Budget % | Target ARR |
|---|---|---|---|
| US | Months 1-6 | 50% | $1M |
| UK | Months 4-9 | 20% | $500k |
| DACH | Months 7-12 | 15% | $300k |
| France | Months 10-15 | 10% | $200k |
| Canada | Months 7-12 | 5% | $100k |
references/positioning-frameworks.md contains:
references/launch-checklists.md contains:
references/international-gtm.md contains:
references/messaging-templates.md contains:
| Metric | Target | Measurement |
|---|---|---|
| Product adoption | >40% in 90 days | Feature usage after launch |
| Win rate | >30% competitive | Deals won vs. competitors |
| Sales velocity | -20% YoY | Days from SQL to close |
| Deal size | +25% YoY | Average contract value |
| Launch pipeline | 3:1 ROMI | Pipeline $ : marketing spend |
| Week | Focus |
|---|---|
| 1 | Review metrics, update battlecards |
| 2 | Create assets, publish content |
| 3 | Support launches, optimize campaigns |
| 4 | Monthly report, plan next month |
| When you ask for... | You get... |
|---|---|
| "Position my product" | Positioning framework (April Dunford method) with completed output |
| "GTM strategy" | Go-to-market plan with channels, messaging, and timeline |
| "Competitive positioning" | Positioning map with competitive gaps and opportunities |
| "Sales enablement" | Sales deck structure, battlecards, and demo flow |
All output passes quality verification:
| Symptom | Likely Cause | Resolution |
|---|---|---|
| Positioning resonates internally but customers do not repeat it | Positioning built from company perspective, not customer language | Rerun April Dunford methodology starting from competitive alternatives, not from product features |
| Win rate against specific competitor below 25% | Battlecard outdated or sales team not using it | Run win_loss_analyzer.py to identify loss patterns; update battlecard monthly; validate 80%+ sales usage |
| GTM motion producing high MQLs but low pipeline conversion | Wrong GTM motion for ACV and buyer type; marketing-led when should be sales-led | Reassess motion using gtm_planner.py; for ACV >$25K, shift to sales-led or hybrid PLG+sales |
| Sales enablement assets gathering dust | Assets created without sales input; format does not match how sales actually works | Co-create assets with sales; survey sales on what they need; track asset usage in deal cycles |
| International expansion burning cash with zero pipeline | Market entered without validating demand (inbound signal, TAM) | Validate 3+ paying customers from market in first 90 days; if not, pause and reassess market priority |
| Competitive intelligence always reactive to lost deals | No proactive monitoring system; battlecards only updated post-loss | Set up monthly competitor monitoring (website, pricing, job postings, G2 reviews); update battlecards proactively |
| Messaging differs across website, sales deck, and ads | No messaging hierarchy documented; each team creates independently | Build messaging hierarchy (headline > subhead > benefits > features > proof) and enforce across all touchpoints |
In Scope: Product positioning (April Dunford methodology), ICP definition and validation, competitive intelligence and battlecards, GTM strategy and motion selection (PLG, sales-led, marketing-led, community-led), product launch planning, sales enablement, win/loss analysis, international expansion planning, messaging hierarchy, PMM KPIs.
Out of Scope: Brand identity and visual design (see brand-guidelines skill), demand generation execution (see marketing-demand-acquisition skill), content creation (see content-creator skill), pricing strategy optimization, sales process design.
Limitations: Positioning frameworks require real customer input to be effective — internally generated positioning is unreliable. Win/loss analysis requires honest deal outcome data from sales; incomplete data produces misleading patterns. GTM motion recommendations are based on ACV and buyer type heuristics; edge cases may require hybrid approaches. International expansion timelines assume US-first model and may not apply to non-US companies.
| Script | Purpose | Usage |
|---|---|---|
scripts/gtm_planner.py | Generate GTM plans with motion selection, channel strategy, and timeline | python scripts/gtm_planner.py config.json --demo |
scripts/win_loss_analyzer.py | Analyze deal outcomes by competitor, segment, and reason | python scripts/win_loss_analyzer.py deals.json --demo |
scripts/battlecard_generator.py | Generate competitive battlecards with feature comparison and objection handling | python scripts/battlecard_generator.py competitor.json --demo |
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
日本語の概要は準備中です。原文の説明を表示しています。
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
日本語の概要は準備中です。原文の説明を表示しています。
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
日本語の概要は準備中です。原文の説明を表示しています。