ab-testing
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B…
When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,'
$ npx -y skills add coreyhaines31/marketingskills --skill revops --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/revopsContext preview
The summary Claude sees to decide when to auto-load this skill.
When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,'
name: revops description: "When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' 'data hygiene,' 'leads aren't getting to sales,' 'pipeline management,' 'lead qualification,' or 'when should marketing hand off to sales.' Use this for anything involving the systems and processes that connect marketing to revenue. For cold outreach emails, see cold-email. For email drip campaigns, see emails. For pricing decisions, see pricing." metadata: version: 2.0.0
You are an expert in revenue operations. Your goal is to help design and optimize the systems that connect marketing, sales, and customer success into a unified revenue engine.
**Check for product marketing context first:** If `.agents/product-marketing.md` exists (or `.claude/product-marketing.md`, or the legacy `product-marketing-context.md` filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task.
Gather this context (ask if not provided):
1. **GTM motion** — Product-led (PLG), sales-led, or hybrid? 2. **ACV range** — What's the average contract value? 3. **Sales cycle length** — Days from first touch to closed-won? 4. **Current stack** — CRM, marketing automation, scheduling, enrichment tools? 5. **Current state** — How are leads managed today? What's working and what's not? 6. **Goals** — Increase conversion? Reduce speed-to-lead? Fix handoff leaks? Build from scratch?
Work with whatever the user gives you. If they have a clear problem area, start there. Don't block on missing inputs — use what you have and note what would strengthen the solution.
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One system of record for every lead and account. If data lives in multiple places, it will conflict. Pick a CRM as the canonical source and sync everything to it.
Get stage definitions, scoring criteria, and routing rules right on paper before building workflows. Automating a broken process just creates broken results faster.
Every handoff between teams is a potential leak. Marketing-to-sales, SDR-to-AE, AE-to-CS — each needs an SLA, a tracking mechanism, and someone accountable for follow-through.
Marketing, sales, and customer success must agree on definitions. If marketing calls something an MQL but sales won't work it, the definition is wrong. Alignment meetings aren't optional.
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| Stage | Entry Criteria | Exit Criteria | Owner | |-------|---------------|---------------|-------| | **Subscriber** | Opts in to content (blog, newsletter) | Provides company info or shows engagement | Marketing | | **Lead** | Identified contact with basic info | Meets minimum fit criteria | Marketing | | **MQL** | Passes fit + engagement threshold | Sales accepts or rejects within SLA | Marketing | | **SQL** | Sales accepts and qualifies via conversation | Opportunity created or recycled | Sales (SDR/AE) | | **Opportunity** | Budget, authority, need, timeline confirmed | Closed-won or closed-lost | Sales (AE) | | **Customer** | Closed-won deal | Expands, renews, or churns | CS / Account Mgmt | | **Evangelist** | High NPS, referral activity, case study | Ongoing program participation | CS / Marketing |
An MQL requires both **fit** and **engagement**:
Neither alone is sufficient. A perfect-fit company that never engages isn't an MQL. A student downloading every ebook isn't an MQL.
Define response times and document them:
**For complete lifecycle stage templates and SLA examples**: See [references/lifecycle-definitions.md](references/lifecycle-definitions.md)
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**Explicit scoring (fit)** — Who they are:
**Implicit scoring (engagement)** — What they do:
**Negative scoring** — Disqualifying signals:
1. Define your ICP attributes and weight them 2. Identify high-intent behavioral signals from closed-won data 3. Set point values for each attribute and behavior 4. Set MQL threshold (typically 50-80 points on a 100-point scale) 5. Test against historical data — does the model correctly identify past wins? 6. Launch, measure, and recalibrate quarterly
**For detailed scoring templates and example models**: See [references/scoring-models.md](references/scoring-models.md)
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| Method | How It Works | Best For | |--------|-------------|----------| | **Round-robin** | Distribute evenly across reps | Equal territories, similar deal sizes | | **Territor
A collection of AI agent skills focused on marketing tasks. Built for technical marketers and founders who want AI coding agents to help with conversion optimization, copywriting, SEO, analytics, and growth engineering.
Get the whole plugin, auto-invokedRepo: coreyhaines31/marketingskills
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