Operators now have a longer AI stack than they did six months ago. MCP servers, agents, custom skills, Claude workflows, Cursor loops. The list keeps growing.
The uncomfortable question sitting underneath all of it is whether the operator can truly explain when to use any of these things.
Ozan Dagdeviren has been measuring it. What he's finding should reset how every GTM team thinks about AI hiring, training, and internal fluency.
Your forecast is lying to you.
On July 28th at 11:00 AM ET, Thang Nguyen (VP of Sales, Airspeed) and Doug May (SVP, Harness) get into why forecasting actually breaks inside revenue orgs.
It isn't a data problem, you have more data than ever. It's a context problem: activity and sentiment dressed up as buying signals. They'll walk through what changed when they stopped treating forecasts as a reporting exercise and started reading buyer behavior instead.
Show up live and get early access to Airspeed's forecasting module plus your first month free.
What Started as a Cursor Changelog
Jason Mellet posted the July Cursor changelog in #chat-gtm. Loop skills. Shared canvases. iOS agents. Auto-review mode. The full sweep of what Cursor shipped last month.
Sana Choudary picked up the thread with a specific question: was Jason building GTM or RevOps skills the community could use, and could he share them?
The answer surprised her.
Jason clarified. He runs a lot of MCP servers and connectors. His most-used skill is /grillme for ideation. Everything else he wires directly into his workflow through MCP rather than building reusable skills on top.
That answer is the entry point to the more interesting problem underneath.
Skills Survive What Prompts Don't
Sana runs Emagino, does voice-of-customer intelligence for DTC brands, and is helping shape the RevGenius skills directory as it gets built out. Her follow-up to Jason:
"But to get repeatable results outside of a project, skills can be helpful. So if you take a type of run from one client to another."
That's the case for skills as a concept. The value of a skill is that it survives a change of context. A prompt lives inside a project. A skill travels between projects and produces the same result each time.
Which is exactly the problem the community's skills directory is trying to solve, and it's the exact problem that has an unspoken constraint underneath it.
AI Proficiency Doesn't Survive Self-Reporting
Ozan Dagdeviren posted a reply that named the problem underneath the thread:
That paragraph names a problem that most GTM teams have been avoiding by not asking the question directly.
Every operator with an AI-adjacent role has spent the last twelve months adding tools to their stack. MCP. Claude Code. Cursor loops. Custom agents. Model routing. Vector databases. Fine-tuned agents. Skills. Prompts. Workflows.
The stack shows up on a LinkedIn profile as a list of tools the operator has "used." What the stack does not show is whether the operator can explain the moment inside their workflow where each tool earns its place.
The line between the two levels of fluency is where hiring decisions, training investments, and internal role definitions are getting decided right now. And the industry has almost no infrastructure for measuring the difference honestly.
Why Self-Reporting Fails Here
The failure mode is structural, not personal.
The path to a modern AI stack looks the same for almost everyone in GTM. A LinkedIn post, a demo, one workflow that half-worked, and then the tool goes on the resume as something the operator "uses." Exposure turns into familiarity, familiarity turns into a checkbox, and the checkbox becomes a claim of proficiency.
None of that maps to knowing when to reach for the tool inside a real workflow. That kind of knowledge only develops from friction, and friction only shows up when the operator has tried and failed with the same tool across slightly different scenarios. The AI space moves fast enough that operators abandon tools before the friction has taught them anything durable.
Sana's framing was correct. Skills survive a change of context. But the directory only earns its place if the people submitting to it can vouch for when their skill should be used and when it shouldn't and that vouching is exactly the judgment layer Ozan is naming.
The calibration problem shows up everywhere the same claim of AI fluency gets made: skills directories, hiring pipelines, internal training budgets, GTM job descriptions.
What It Means for the Way You Hire and Train
The practical takeaway is a set of shifts every revenue leader can make this quarter.
Stop asking candidates what tools they've used.
Ask them to describe the last three times they picked a specific tool over an alternative, and why. If the answer is generic, the fluency is generic. If the answer is specific to a workflow, the fluency is real.Stop training your team on tools they've read about.
Train them on the judgment problems that come up when the tool is in production. When does MCP earn its place versus a direct API call? When does a custom skill save time versus a well-written prompt inside the project? When does an agent do better work than a well-scoped prompt chain? These are the questions that separate operators who can execute from operators who have added items to a stack.Stop hiring on stack overlap.
Two candidates can list Claude, MCP, Cursor, and Codex on their resume. One can explain the reasoning behind each choice in fifteen seconds. The other cannot. The gap between the two is the entire hiring signal.
The Real Problem Is Judgment
The AI skills gap is not a training problem. It is a judgment problem.
Judgment develops from friction. Friction develops from time inside a workflow. Time inside a workflow does not happen when the operator is constantly rebuilding the workflow around whatever tool got hyped that week.
The teams that pull ahead in the next twelve months will not be the ones with the longest AI stacks. They will be the ones whose operators can articulate why each tool in the stack is doing the specific job they picked it for, and what would have to be true to swap it out.
That capacity does not come from a skills directory alone. It comes from operators who have earned their judgment the slow way, and from teams that stop rewarding stack expansion and start rewarding stack coherence.
The RevGenius skills directory is designed to be part of that shift. Sana's framing, Jason's honesty about how he actually works, and Ozan's data are all pointing at the same conclusion. The gap between "I've heard of it" and "I know when to reach for it" is the gap the whole industry has been quietly hiring against without measuring.
Time to start measuring it.
Conversations like this happen every day in the RevGenius Slack. If you want to be in them, that's where to start. Join the RevGenius community.
And if you want to hear these conversations play out live, that's Coffee Talks.
Next Coffee Talk: Thursday, July 23rd at 11am ET.
Bring something you're building, something that's stuck, or just bring questions. The point is to show up.
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