Designs revenue operations workflows: lead scoring, routing, handoffs, lifecycle rules, and CRM automation for marketing-sales-customer success alignment.
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evals/evals.jsonreferences/automation-playbooks.mdreferences/lifecycle-definitions.mdreferences/routing-rules.mdreferences/scoring-models.mdDesigns and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Enables AI agents to execute x402 payments with per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents pay for APIs, services, or other agents.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
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-context.md exists (or .claude/product-marketing-context.md 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):
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.
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.
| 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
Explicit scoring (fit) — Who they are:
Implicit scoring (engagement) — What they do:
Negative scoring — Disqualifying signals:
For detailed scoring templates and example models: See references/scoring-models.md
| Method | How It Works | Best For |
|---|---|---|
| Round-robin | Distribute evenly across reps | Equal territories, similar deal sizes |
| Territory-based | Assign by geography, vertical, or segment | Regional teams, industry specialists |
| Account-based | Named accounts go to named reps | ABM motions, strategic accounts |
| Skill-based | Route by deal complexity, product line, or language | Diverse product lines, global teams |
Response time is the single biggest factor in lead conversion:
Build routing rules that prioritize speed. Alert reps immediately. Escalate if SLA is missed.
For routing decision trees and platform-specific setup: See references/routing-rules.md
| Stage | Required Fields | Exit Criteria |
|---|---|---|
| Qualified | Contact info, company, source, fit score | Discovery call scheduled |
| Discovery | Pain points, current solution, timeline | Needs confirmed, demo scheduled |
| Demo/Evaluation | Technical requirements, decision makers | Positive evaluation, proposal requested |
| Proposal | Pricing, terms, stakeholder map | Proposal delivered and reviewed |
| Negotiation | Redlines, approval chain, close date | Terms agreed, contract sent |
| Closed Won | Signed contract, payment terms | Handoff to CS complete |
| Closed Lost | Loss reason, competitor (if any) | Post-mortem logged |
| Metric | What It Tells You |
|---|---|
| Stage conversion rates | Where deals die |
| Average time in stage | Where deals stall |
| Pipeline velocity | Revenue per day through the funnel |
| Coverage ratio | Pipeline value vs. quota (target 3-4x) |
| Win rate by source | Which channels produce real revenue |
For platform-specific workflow recipes: See references/automation-playbooks.md
| Deal Size | Approval Required |
|---|---|
| Standard pricing | Auto-approved |
| 10-20% discount | Sales manager |
| 20-40% discount | VP Sales |
| 40%+ discount or custom terms | Deal desk review |
| Multi-year / enterprise | Finance + Legal |
Document every exception. Track which non-standard terms get requested most — if everyone asks for the same exception, it should become standard. Review quarterly.
| Tool | Strength |
|---|---|
| Clearbit | Real-time enrichment, good for tech companies |
| Apollo | Contact data + sequences, strong for prospecting |
| ZoomInfo | Enterprise-grade, largest B2B database |
| Metric | Formula / Definition | Benchmark |
|---|---|---|
| Lead-to-MQL rate | MQLs / Total leads | 5-15% |
| MQL-to-SQL rate | SQLs / MQLs | 30-50% |
| SQL-to-Opportunity | Opportunities / SQLs | 50-70% |
| Pipeline velocity | (# deals x avg deal size x win rate) / avg sales cycle | Varies by ACV |
| CAC | Total sales + marketing spend / new customers | LTV:CAC > 3:1 |
| LTV:CAC ratio | Customer lifetime value / CAC | 3:1 to 5:1 healthy |
| Speed-to-lead | Time from form fill to first rep contact | < 5 minutes ideal |
| Win rate | Closed-won / total opportunities | 20-30% (varies) |
Build three views:
When delivering RevOps recommendations, provide:
Format each as a standalone document the user can implement directly. Include platform-specific guidance when the CRM is known.
For implementation, use the CRM, scheduling, enrichment, and automation tools available in the current environment. Key RevOps tools:
| Tool | What It Does | Guide |
|---|---|---|
| HubSpot | CRM, marketing automation, lead scoring, workflows | Use available HubSpot integrations |
| Salesforce | Enterprise CRM, pipeline management, reporting | Use available Salesforce integrations |
| Calendly | Meeting scheduling, round-robin routing | Use available scheduling integrations |
| SavvyCal | Scheduling with priority-based availability | Use available scheduling integrations |
| Clearbit | Real-time lead enrichment and scoring | Use available enrichment integrations |
| Apollo | Contact data, enrichment, and outbound sequences | Use available outbound data integrations |
| ActiveCampaign | Marketing automation for SMBs, lead scoring | Use available marketing automation integrations |
| Zapier | Cross-tool automation and workflow glue | Use available workflow automation integrations |