Close the loop so your GTM compounds
Episode 4, made runnable. You built a workflow, a context brain, and a signal library with an empty performance column. Close the loop: feed the outcomes back so the system gets smarter every week, instead of running once and forgetting.
Close the loop so your GTM compounds
Episode 4, made runnable.
You built a workflow, a context brain, and a signal library with an empty performance column. Close the loop: feed the outcomes back so the system gets smarter every week, instead of running once and forgetting.
Details
Details
Details
Details
The 7th analyst
The judgment layer that makes the system compound.
Numbers everyone trusts
Outcomes fed back into your ICP
Signal library
A QA layer watching for drift
Your GTM runs once and forgets. The campaign ends, the numbers sit in a dashboard, and nothing feeds back into the next play. You repeat the same misses, and the context brain and signal library you built slowly go stale.
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.
Conversion by stage, velocity, and win rate with every definition pinned, and the leak named.
Reads closed-won, closed-lost, and churned accounts, then hands back a ranked list of lookalike accounts to work.
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.
Numbers everyone trusts, outcomes fed back into your ICP and signal library, a QA layer watching for drift, and a system that proposes its own improvements for you to approve.
- All 4 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 Head of RevOps ran last month's signal-driven campaigns from Episode 3 and does not want the results to sit in a dashboard nobody revisits.
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 loop feeds back numbers nobody trusts.
Without it: The patterns learn from fiction.
Without it: The loop only learns from the front of the funnel.
Without it: One bad definition compounds every quarter.
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 →The system compounds only if someone closes the loop
The skills hand the team pinned metrics, outcome patterns, a drift watch, and proposed improvements; the compounding comes from a human approving the right changes every week. Here is how a GTM leader runs the feedback cadence so the system gets smarter instead of just older.
Every week, the loop owner reads what the system expected against what actually happened: signals that paid, deals that lied, definitions that bent. Approved changes ship that week; everything else is noted and left alone.
- 01Teach outcome hygiene first
The loop is only as honest as the closed-lost reasons and churn notes feeding pattern-analyst. Train the team to record what actually happened, not the polite version.
- 02Hold the weekly review hour
Same hour every week: the pinned funnel-metrics, the qa-agent digest, and evolution-agent's proposals in one sitting. A loop reviewed monthly is a report, not a loop.
- 03Rehearse the approve-or-decline call
Practice judging an evolution-agent proposal out loud: what evidence backs it, what breaks if it is wrong, how it gets reversed. Nothing self-edits, so the judgment is the safety.
- 04Inspect the loop's own record
Quarterly, check which approved changes actually improved conversion and which got quietly reverted. The loop gets audited like everything it audits.
One plugin. 4 skills, bundled.
The whole playbook installs as a single plugin in Claude Code, no copy-pasting 4 times. Or grab any single step above. It runs on what you paste; connect your stack to go live.