Stand up an AI-native GTM motion
You are bolting AI onto outbound and you do not have four weeks to build it step by step. This is the condensed version of the AI-Native GTM Series: the same score, enrich, watch, and learn moves run in one sitting instead of across four episodes.
Stand up an AI-native GTM motion
You are bolting AI onto outbound and you do not have four weeks to build it step by step.
This is the condensed version of the AI-Native GTM Series: the same score, enrich, watch, and learn moves run in one sitting instead of across four episodes.
Details
Details
Details
Details
Details
Details
The 7th analyst
The QA agent that watches the whole motion.
A 33-cent bake-off
The answer was a signal-ownership waterfall, not one vendor.
The product channel
Usage turned into the best-converting channel.
In one pass
Not four episodes: a scored market
Enriched accounts
A fresh re-score on real data
You keep bolting an AI tool onto the same outbound motion and the number does not move, because a scored list, a usage read, and a QA layer added separately never talk to each other. Meanwhile the full 4-week AI-Native GTM Series feels like too much to commit to before you have even seen the shape of it.
The workflow that solves it, one step at a time.
Each step is the plain question you are already asking. The skills answer them in order, one handing its receipt to the next.
Fair across channels, blank fields never scored as zero, with a why behind each account's score.
Give it a name and company, or a domain, and get contacts or firmographics back. Waterfalls across your tools.
Fair across channels, blank fields never scored as zero, with a why behind each account's score.
Turns raw usage into per-capability adoption tiers, a 12-week trend on each, power-user IDs, and a ghost-active flag.
The check on your other analysts: it surfaces scoring blind spots, CRM hygiene gaps, and workflow drift, then hands back a weekly QA digest.
Reads QA findings, feedback, and outcomes, then proposes concrete changes to your skills and scoring. You approve, nothing self-edits.
In one pass, not four episodes: a scored market, enriched accounts, a fresh re-score on real data, a product-usage read, a QA agent that watches it, and a loop that learns.
- All 6 chained skills resolve to real, scored skills.
- Has a problem, an outcome, 2+ steps, and proof from a real build.
- Ships an example chat showing the play run in Claude.
- Reads in plain operator voice, grounded in a real build.
We did not just check the structure. A judge read this play against the full text of every skill it chains (composition), then ran it on a real scenario end to end (simulated). It passed both. Test scenario: A RevOps lead wants an AI-native motion running today, not spread across a 4-week series.
This play runs across the org, not inside sales.
Every function below owns a piece of the play. Skip one and it stalls where most GTM plays stall: in the handoff.
Without it: The motion runs fast on wrong data.
Without it: The warmest signal in the stack stays dark.
Without it: The system surfaces and nobody moves.
Watch the skills answer in order.
Describe your situation in plain language. The right skill fires on its own and hands its receipt to the next.
Before you run it, I ran it for real.
This workflow is genericized from a real build. Here is the number it moved, and what I actually did.
WHAT I DID ›
AI-native is not how many agents you have. It is whether one of them watches the others and turns their mistakes into improvements. Build the analyst that reads the audit trail. That is the one that compounds.
Dig into the full build →WHAT I DID ›
Do not run a vendor bake-off to crown a winner. Run it to map what each one covers, then rank the stack by cost and orchestrate it as one workflow: cheapest predictive field first, the expensive vendor last. You get the same data for a fraction of the spend, a model that scales, and contact data good enough that a dollar of pipeline books better than 1 in 3 instead of 1 in 5.
Dig into the full build →WHAT I DID ›
If you sell a product people can use before they buy, you already have a product-led channel. The question is never whether it exists. It is whether your usage data has been turned into context a rep can act on. Map the events first. The motion comes after.
Dig into the full build →The loop is only as good as the operator inside it
The six skills hand you a scored market, enriched data, a usage read, and agents that watch and learn; the revenue still comes from a rep acting on what the system surfaces the same week. Here is how a GTM leader stands the motion up in one pass without losing the human judgment that makes it sell.
Run the whole chain, icp-scoring through evolution-agent, on one small segment first, and walk the team through every handoff before it runs at full width. Nothing routes to a rep until the shakedown segment looks right end to end.
- 01Teach the reps what the score means
Before enriched, re-scored accounts route, walk the team through what moved between the first and second icp-scoring pass. A rep who watched the rank change on real data trusts the rank.
- 02Set the act-on-it SLA
A product-usage signal has a shelf life. Every surfaced account carries an owner and a same-week clock, and the weekly review opens with the signals nobody worked.
- 03Read the qa-agent digest weekly
The misses the qa-agent catches are the syllabus. Review the digest as a team and pick one fix a week to feed the loop; small and shipped beats big and planned.
- 04Approve every evolution, in person
The evolution-agent proposes; a named human approves. Keep the approval next to the decision-log entry so the system's changes stay explainable.
One plugin. 6 skills, bundled.
The whole playbook installs as a single plugin in Claude Code, no copy-pasting 6 times. Or grab any single step above. It runs on what you paste; connect your stack to go live.