Build the brain before the agents.

Diagnose the foundation, fix the data, score accounts the way a system should, and make the change stick. The skills teams reach for when the GTM org goes AI-native.

8 skills, curated by hand.

RevOps12

Sangram Vajre

Co-founder & CEO

MOVE GTM diagnostic

Use this skill when a company wants to audit its go-to-market foundation before scaling or automating - it runs Sangram Vajre's WSJ-bestselling MOVE framework, the 4-question GTM diagnostic (Market, Operations, Velocity, Expansion), to find where GTM is misaligned, identify which of the three fit stages the company is actually in (Problem-Market / Product-Market / Platform-Market), and return a prioritised list of what to fix first. Trigger phrases: "GTM audit", "GTM diagnostic", "GTM health check", "MOVE assessment", "are we ready to scale", "why isn't our GTM working" - and before standing up any new outbound, inbound, or deal-process engine. This is a diagnosis, not a build - run it first.

Deals30

Amos Bar-Joseph

CEO & Co-Founder

Account tier scoring

Use this skill when you want a production-grade account scoring system instead of a vibes-based one: it tiers every account onto a Bronze/Silver/Gold/Diamond ladder, scoring pre-conversion accounts on intent signals + ACV potential and trials/customers on activation depth + expansion headroom. It encodes the judgment that makes scoring trustworthy — a hard self-serve gate with LinkedIn headcount verification, an enterprise persona gate, anonymous-signal and social-engagement caps, prior-relationship floors, and never-demote rules — with every threshold exposed as a tunable default. Each run produces a verified tier tag and ACV tag, a prepended signal-stack snapshot in account memory, a CRM lead-score sync, and correctly routed Gold/Diamond alerts with per-stakeholder engagement recommendations.

RevOps27

Rutger Katz

Founder @ Neon Triforce — AI-Ready Revenue Systems

GTM data architecture

GTM data architecture for revenue operators who are not data engineers; warehouse-native and zero-copy patterns that have won in the market. Use when designing GTM data stacks, planning data transformation layers, evaluating CDP vendors, building identity resolution and unified customer intelligence, implementing reverse ETL (Hightouch or Census), or assessing data readiness for AI agents. Also trigger on 'data architecture', 'ELT vs ETL', 'warehouse-native CDP', 'composable CDP', 'reverse ETL implementation', 'dbt for RevOps', 'unified health scores', 'customer data platform strategy', or 'data mesh for GTM'. BOUNDARY: Covers architecture and transformation layers only. Handoff to revops-data-governance for CRM data quality and governance; to revops-tech-stack for vendor selection frameworks; to gtm-planning for strategy implications. This skill addresses the data layer for a RevOps team scaling AI adoption.

RevOps23

Rutger Katz

Founder @ Neon Triforce — AI-Ready Revenue Systems

RevOps change management

Revenue change management: plan change, design enablement, make it stick, measure adoption. Trigger on change management, enablement design, adoption failure, rollout plan, communication plan, stakeholder management, training that doesn't stick, behavior change, coaching program, process change, system migration, comp plan change, territory change, Kotter, ADKAR, spaced repetition, forgetting curve, reverse salient, traffic light model, Bloom's taxonomy, 'nobody follows the new process,' 'we trained them but nothing changed,' 'the tool is built but nobody uses it,' 'how do we get buy-in,' 'people are pushing back,' AI change management, FOBO, shadow AI, AI adoption, works council AI, AI governance framework, AI rollout, or fear of becoming obsolete. BOUNDARY: For HubSpot adoption, see revops-hubspot. For GTM org change, see gtm-planning.

RevOps23

Rutger Katz

Founder @ Neon Triforce — AI-Ready Revenue Systems

Revenue tech stack architecture

Revenue technology stack architecture, value engineering, platform evaluation, and capability mapping for B2B GTM teams. Use when the user mentions tech stack, martech stack, sales tech, CRM evaluation, platform selection, tool consolidation, stack audit, build vs buy, integration architecture, composability, iPaaS, CDP, MAP, sales engagement platform, AI tools for GTM, AI maturity, AI orchestration, AI agents, knowledge layer, semantic retrieval, RAG stack, vector database, EU AI stack, GDPR AI tools, knowledge management platform, Glean, Langdock, Weaviate, Qdrant, Pinecone, LlamaIndex, LangChain, Dust.tt, Guru, or Notion AI. Also trigger on 'we have too many tools,' 'our tools don't talk to each other,' 'should we buy X or build it,' 'where should our knowledge live,' or 'how do we give AI access to our internal docs.' BOUNDARY: Covers TECHNOLOGY evaluation and architecture. For strategy framing, see revops-strategy. For HubSpot, see revops-hubspot. For data governance, see revops-data-governance.

Signals35

Alex Vacca

Founder & CEO @ Frontal

The signal sourcer

Use this skill when running signal-based selling — buying signals, intent data, signal scoring and stacking, website visitor tracking, job changes, hiring, funding, competitor and tech-stack signals, and signal-to-action GTM plays. Triggers on 'buying signals', 'intent data', 'signal scoring', 'website visitors', 'job change', 'hiring signal', 'funding signal', 'competitor signal', 'tech change', 'warm outbound', 'signal stacking', 'RB2B', 'Trigify', 'GTM plays'.

Sales28

Alex Vacca

Founder & CEO @ Frontal

GTM philosophy

Use this skill when defining outbound strategy, training sales teams, or establishing GTM fundamentals. Core principles — scale what top performers do, signal-based outreach, lead with pain, segment don't over-personalize — plus multi-channel coordination and key mindsets on personalization, volume, timing, and messaging.

RevOps21

Austin Hay

Operating Partner @ Khosla Ventures

Reverse ETL activation

Use this skill when you need to turn a scored, segmented audience sitting in the warehouse (BigQuery, Snowflake, Redshift) into live GTM motion, syncing accounts, contacts, and computed traits out to the CRM, ad platforms, and sequencing tools. It encodes the judgment that keeps reverse ETL from quietly polluting every downstream system: identity resolution before any write, a change-data-capture diff so only real deltas move, field-level mapping with type and format guards, suppression and consent gates, sync scheduling matched to each destination's rate limits, and a mandatory dry run plus row-count reconciliation before and after the live push. Every threshold (match-confidence floor, batch size, sync cadence, max-delete guard, suppression rules) is a tunable default. Each run produces a validated sync plan, a dry-run diff of adds, updates, and suppressions with counts, and a reconciliation report after the live sync.