Performance Marketer
Structure paid acquisition, landing pages, tests, and attribution so a solo founder can scale ads without guessing which channel actually converts.
Overview
performance-marketer is an agent skill most often used in Grow (also Launch distribution, Validate pricing) that structures paid media, landing optimization, testing, and attribution for measurable customer acquisition.
Install
npx skills add https://github.com/ncklrs/startup-os-skills --skill performance-marketerWhat is this skill?
- Seven CRITICAL-to-medium impact areas: paid strategy, creative, landing, testing, analytics, budget, retargeting
- Attribution modeling section with impact HIGH—model comparison and decision hygiene
- Platform-ready creative and copy guidance tied to campaign structure
- Landing page message match, CRO, and speed called out as CRITICAL
- Testing frameworks with statistical significance and prioritization for lean teams
- 7 section organization areas from paid strategy through retargeting
- Attribution modeling tagged impact HIGH
Adoption & trust: 1 installs on skills.sh; 27 GitHub stars; 3/3 security scanners passed (skills.sh audits); trending (+100% hot-view momentum).
What problem does it solve?
You are spending on ads but cannot tell which creative, landing page, or channel deserves budget because tests and attribution are ad hoc.
Who is it for?
Founders and tiny teams launching SaaS, ecommerce, or content products who need a full-funnel paid checklist without hiring a performance agency day one.
Skip if: Pure organic-only brands with zero paid budget, or enterprises with dedicated ad-ops and bespoke BI—this skill is framework-level, not a live ads API integration.
When should I use this skill?
Planning or optimizing paid acquisition, landing conversion, and marketing measurement for a startup offer.
What do I get? / Deliverables
You get a prioritized paid-growth operating map—campaign structure, creative rules, LPO checks, test queue, and attribution choices—so scaling decisions follow evidence.
- Campaign and creative brief aligned to skill sections
- Test backlog with prioritization
- Attribution and reporting recommendations
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Paid performance marketing is shelved under Grow → lifecycle because the playbook centers on CAC, LTV, retention loops, and budget scaling after you have something to sell. Lifecycle covers measurement, retargeting, and efficiency systems that compound users—not one-off launch posts alone.
Where it fits
Align ad offer copy and landing proof points before you scale spend on a new price tier.
Pick initial paid channels and campaign structure for a product hunt or launch-week burst.
Tune retargeting sequences and frequency caps while watching CAC against LTV cohorts.
Choose an attribution model and reporting cadence so channel credit matches how you actually decide budget.
How it compares
Use as a structured marketing playbook in skill form, not as a replacement for your ad platform’s native reporting or an MCP analytics connector.
Common Questions / FAQ
Who is performance-marketer for?
Solo builders and indie startups who run or are about to run paid acquisition and need channel, creative, landing, and measurement guidance in one agent-invokable skill.
When should I use performance-marketer?
Use it at Launch when planning distribution and paid tests, during Validate when stress-testing pricing and offer-message fit on landing pages, and in Grow when optimizing CAC, retargeting, and budget scale.
Is performance-marketer safe to install?
It provides strategic and copy frameworks only; confirm repo trust and review the Security Audits panel on this Prism page before installing.
SKILL.md
READMESKILL.md - Performance Marketer
## 1. Paid Advertising Strategy (paid) **Impact:** CRITICAL **Description:** Channel selection, campaign structure, audience targeting, and overall paid media strategy. ## 2. Creative & Copy (creative) **Impact:** CRITICAL **Description:** Ad creative best practices, copy formulas, visual guidelines, and platform-specific formats. ## 3. Landing Page Optimization (landing) **Impact:** CRITICAL **Description:** Landing page structure, message match, conversion optimization, and page speed. ## 4. Testing & Experimentation (testing) **Impact:** HIGH **Description:** A/B testing frameworks, statistical significance, test prioritization, and learning systems. ## 5. Analytics & Attribution (analytics) **Impact:** HIGH **Description:** Attribution modeling, conversion tracking, CAC/LTV analysis, and reporting. ## 6. Budget & Scaling (budget) **Impact:** MEDIUM-HIGH **Description:** Budget allocation, scaling strategies, bid optimization, and efficiency. ## 7. Retargeting (retargeting) **Impact:** MEDIUM-HIGH **Description:** Retargeting strategy, audience segmentation, frequency capping, and sequential messaging. --- title: Attribution Modeling impact: HIGH tags: attribution, analytics, tracking, measurement --- ## Attribution Modeling **Impact: HIGH** Attribution determines which channels get credit for conversions. Wrong attribution leads to wrong decisions and wasted budget. ### Attribution Models Overview | Model | How It Works | Best For | |-------|--------------|----------| | **Last Click** | 100% credit to final touchpoint | Direct response, simple funnels | | **First Click** | 100% credit to first touchpoint | Understanding awareness | | **Linear** | Equal credit across all touchpoints | Multi-channel, long cycles | | **Time Decay** | More credit to recent touchpoints | Consideration stage analysis | | **Position-Based** | 40% first, 40% last, 20% middle | Balanced view | | **Data-Driven** | ML-based, platform-specific | High volume, sophisticated | ### Model Comparison Example Customer journey: Google Ad → Blog Post → LinkedIn → Demo → Close | Model | Google | Blog | LinkedIn | Demo | |-------|--------|------|----------|------| | Last Click | 0% | 0% | 0% | 100% | | First Click | 100% | 0% | 0% | 0% | | Linear | 25% | 25% | 25% | 25% | | Time Decay | 10% | 15% | 25% | 50% | | Position-Based | 40% | 10% | 10% | 40% | ### When to Use Each Model | Situation | Recommended Model | Why | |-----------|------------------|-----| | Short sales cycle (<7 days) | Last Click | Few touchpoints | | Long sales cycle (30+ days) | Linear or Position-Based | Many touchpoints matter | | Brand building focus | First Click | See what drives awareness | | Retargeting heavy | Time Decay | Recent > distant | | High volume, good data | Data-Driven | Let ML optimize | | Just getting started | Position-Based | Balanced, forgiving | ### Multi-Touch Attribution Setup ``` 1. Define conversion events └── Primary: Trial signup, demo request, purchase └── Secondary: Pricing page, feature page, content DL 2. Identify touchpoints └── Paid: Google, Meta, LinkedIn └── Organic: SEO, direct, referral └── Owned: Email, blog, social 3. Connect data sources └── Ad platforms → Analytics → CRM 4. Set lookback window └── B2C: 7-30 days └── B2B: 30-90 days 5. Choose model └── Start with position-based └── Move to data-driven when mature ``` ### Tracking Implementation | Layer | Tools | Purpose | |-------|-------|---------| | **Web Analytics** | GA4, Mixpanel, Amplitude | User journey | | **Ad Tracking** | Platform pixels, CAPI | Channel performance | | **CRM** | HubSpot, Salesforce | Lead → Customer | | **Data Warehouse** | BigQuery, Snowflake | Unified view | | **Attribution Tool** | Segment, Triple Whale | Cross-channel model | ### UTM Parameter Standards ``` utm_source = where traffic comes from (google, linkedin, email) utm_medium = marketing medium (cpc, social, email