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✓ APPROVED

Turn closed deals into a repeatable edge

You closed 30 deals this year and could not name the one thing the wins share. The pattern in your wins and losses is the edge you are leaving on the table.

Install the whole playbook ↓
[ BUILT GTM ]

Turn closed deals into a repeatable edge

Solvethe problem worth solving
Problem · 01RevOps &

You closed 30 deals this year and could not name the one thing the wins share.

The pattern in your wins and losses is the edge you are leaving on the table.

Stackthe workflow, box by box
Salesforce01
Extract the repeatable reason you win.
Extract the repeatable reason you win.
Powers up with
AI
Salesforce02
Categorize your losses and name the fix.
Categorize losses and name the fix.
Powers up with
AI
Gong03
Turn closed deals into a change, not a post-mortem deck.
Turn the interviews into a coded pattern.
Powers up with
AI
Claude04
Find what your wins, losses, and churn share.
Read wins and losses side by side to find the one thread, then rank the active accounts that match it.
Connect to analyze
AI
Splithow control passes: agents branch left, humans right
HUMAN + AI, IN THE LOOP
How control passes.
AgentsHuman
Agent01
Extract the repeatable reason you win.
Salesforce runs it.
Agent02
Categorize your losses and name the fix.
Salesforce runs it.
Agent03
Turn closed deals into a change, not a post-mortem deck.
Gong runs it.
Agent04
Find what your wins, losses, and churn share.
Claude runs it.
The skills run the work. You stay in the loop on the calls that need judgment.
Outputwhat the workflow produces
OUTPUT · 01

What wins share

OUTPUT · 02

Why losses happen

OUTPUT · 03

A coded pattern

OUTPUT · 04

A change you can ship

Outcomethe result it drives
OUTCOME · 01

The number-one predictor was not in the model

The win/loss backtest that found the real signal.

Built GTM
── 01 / WHY RUN IT
RUN THIS AND YOU CAN
✓What wins share
✓Why losses happen
✓A coded pattern
✓A change you can ship
── 02 / THE RUN, STEP BY STEP

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.

1✓ TESTED
Extract the repeatable reason you win.

Reads your won deals, finds the signals, personas, and motions that repeat, and ranks active lookalikes to work.

YOU GETThe pattern extraction, the win-signal ranking, and the lookalike list.
POWER IT UPSalesforceHubSpotAttioPipedriveDynamicsGong
OPEN THE SKILL →
2✓ TESTED
Categorize your losses and name the fix.

Sorts lost deals into no-decision, price, competitor, timing, or fit, pulls competitive intel, and splits preventable from structural.

YOU GETThe loss taxonomy, the competitive intel pull, and the fix per theme.
POWER IT UPSalesforceHubSpotAttioPipedriveDynamicsGong
OPEN THE SKILL →
3✓ TESTED
Turn closed deals into a change, not a post-mortem deck.

Who to interview, the question guide, how to code themes across deals, and how to turn the pattern into a messaging or product change.

YOU GETThe interview slate, the question guide, and pattern-to-change with an owner.
POWER IT UPGongChorusFirefliesOtterAvomaFathom
OPEN THE SKILL →
4✓ TESTED
Find what your wins, losses, and churn share.

Reads closed-won, closed-lost, and churned accounts, then hands back a ranked list of lookalike accounts to work.

YOU GETThe won / lost / churn pattern logic and the lookalike ranking.
POWER IT UPSalesforceHubSpotAttioPipedriveDynamicsAmplitude
OPEN THE SKILL →
WHERE YOU LAND

What wins share, why losses happen, a coded pattern, a change you can ship, and the active accounts that already look like your winners.

EVERY STEP, TESTED AND PASSED
EVAL RESULTPASS4/4 dimensions
HOW WE EVAL →
✓Skills resolve
✓Structure + proof
✓Example chat
✓Quality judge
  • 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.
Last eval run 2026-08-05
DEEP EVAL · PASS
THE STANDARD →

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 manager just closed the quarter and wants to know what the wins actually have in common before the team forgets the specifics.

── 03 / SEE IT RUN

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.

EXAMPLE CHATSam, RevOps Manager, running the loop in one sitting
S
We just closed the quarter. Before everyone forgets, what do our wins actually have in common?
S
Step 1· Closed Won Analysis
I read every won deal and pulled the signals, personas, and motions that keep repeating, then ranked the active accounts that look like them. The pattern is pretty clear.
Receipt
3 of your last 4 wins came in through a product trial, all with a VP champion by week 2.
S
And the ones we lost? I want to know if we're losing on price or something we can fix.
S
Step 2· Closed Lost Analysis
I sorted the losses into no-decision, price, competitor, timing, and fit, then split what's preventable from what's structural. Price is not your problem.
no-decisionhalf
price2 of 11
Receipt
Most losses were no-decision, not lost to a competitor.
S
Can we make this a real program so we're not guessing? Who should we actually talk to?
S
Step 3· Win Loss Program
I built the interview slate, a question guide that won't lead the witness, and a way to code themes across deals so the pattern turns into an actual change with an owner.
Receipt
8 interviews queued, 4 won and 4 lost; messaging fix assigned to a named owner.
S
Pull it all together. What do wins and losses share, and who do we go work now?
S
Step 4· Pattern Analyst
I read wins and losses side by side to find the one thread running through both, then handed back a ranked list of active lookalikes to work.
Receipt
Single-threaded deals stall or lose; 12 active accounts match the winning pattern, ranked.
── 04 / SEE IT IN PRODUCTION

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.

THE BUILD · SCORING EVIDENCE
The Win/Loss Backtest
2.67x
win-lift from the strongest behavioral signal
3.56x
that signal stacked with an ICP threshold
16 to 0
won-lost record of every account that reached Hot or Strike
1.13x
the fit score everyone trusted, barely discriminating
WHAT I DID ›
01
Measure lift, not opinion. For every signal, I compared how often it showed up in won deals versus lost ones. Lift, not vibes. A signal that appears equally in wins and losses is noise, no matter how good it feels.
02
Find the predictors that were not in the model. The strongest discriminators were behavioral, how the buyer actually worked, not the firmographics we had been scoring on. The single best one carried a 2.67x lift and was not weighted at all.
03
Stack the signals that compound. One behavioral signal plus an ICP threshold stacked to a 3.56x lift. The combination beat either alone, which is the whole argument for keeping signals separate so they can compound.

Before you trust a scoring model, backtest it against the deals you actually won and lost. Measure lift per signal. The ones that feel important and the ones that are important are rarely the same list.

Dig into the full build →
05 / INSTALL THE WHOLE PLAYBOOK

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.

OR KICK IT OFF IN CHAT
“Here is my situation: [describe your workflow or account in a sentence or two]. Walk me through Turn closed deals into a repeatable edge step by step, and run each skill as we go.”
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One runnable workflow a week. Free.