← ALL PLAYBOOKS
── PLAYBOOK · GTM STRATEGY
✓ APPROVED

Diagnose why you are missing the number

The forecast is soft and the room is guessing. Find the real cause before you cut or spend.

[ BUILT GTM ]

Diagnose why you are missing the number

Solvethe problem worth solving
Problem · 01GTM Strategy

The forecast is soft and the room is guessing.

Find the real cause before you cut or spend.

Stackthe workflow, box by box
Salesforce01
Build funnel metrics the meeting will actually trust.
Quantify the funnel stage by stage and name the single worst-converting transition in numbers, the exact stage strike-zone-analyst drills into next.
Powers up with
AI
Salesforce02
Find where the funnel leaks and what to fix.
Drill only the exact stage funnel-metrics just flagged and diagnose the root cause by channel and scoring gap, not a second pass over the whole funnel.
Powers up with
AI
Salesforce03
Get a straight verdict on every open deal.
Separate the real deals from the hopeful ones.
Powers up with
AI
Salesforce04
Find what your wins, losses, and churn share.
See which channel concentrates your closed-lost and churn risk, then hand that channel finding to forecast-call-prep as a named risk scripted into the call, not just color commentary.
Powers up with
AI + human
Salesforce05
Walk into the forecast call with a number you can defend.
Rebuild a number you can defend, with pattern-analyst's channel finding folded into the named risk and press question for every deal sourced from that channel.
Powers up with
AI
Splithow control passes: agents branch left, humans right
HUMAN + AI, IN THE LOOP
How control passes.
AgentsHuman
Agent01
Build funnel metrics the meeting will actually trust.
Salesforce runs it.
Agent02
Find where the funnel leaks and what to fix.
Salesforce runs it.
Agent03
Get a straight verdict on every open deal.
Salesforce runs it.
AI + human04
Find what your wins, losses, and churn share.
AI drafts, a human checks.
Agent05
Walk into the forecast call with a number you can defend.
Salesforce runs it.
The skills run the work. You stay in the loop on the calls that need judgment.
Outputwhat the workflow produces
OUTPUT · 01

The leak named

OUTPUT · 02

The deals that are real

OUTPUT · 03

The pattern underneath

OUTPUT · 04

A defensible forecast

Outcomethe result it drives
OUTCOME · 01

A number the room stopped arguing with

The same forecast discipline that cut deal slippage 50%+, now defending a single quarter's number.

Built GTM
── 01 / WHY RUN IT

The number is soft, the room starts guessing, and the loudest voice wins the theory. Someone wants to cut spend, someone wants to cut heads, and nobody has actually named which stage is leaking, why, or which deals are real before the decision gets made.

RUN THIS AND YOU CAN
The single worst-converting stage, named in numbers everyone agrees on
The root cause behind that exact stage, by channel and scoring gap
The deals that are real, separated from the hopeful ones
A defensible commit number with the channel risk scripted into the call
── 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
Build funnel metrics the meeting will actually trust.

Conversion by stage, velocity, and win rate with every definition pinned, and the leak named.

YOU GETStage conversion, time-in-stage, win rate, and a definition ledger.
OPEN THE SKILL →
2✓ TESTED
Find where the funnel leaks and what to fix.

Finds the leak, the accounts to work next, and where the scoring model is quietly letting you down.

YOU GETThe funnel-leak diagnosis, the account ranking, and the scoring audit.
OPEN THE SKILL →
3✓ TESTED
Get a straight verdict on every open deal.

Healthy, slipping, stuck, or dead. Checks real activity, whether the rep qualified it, and whether the deal is multi-threaded.

YOU GETThe risk taxonomy, the activity checks, and the per-deal verdict format.
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.
OPEN THE SKILL →
5✓ TESTED
Walk into the forecast call with a number you can defend.

Sorts each deal into commit, best-case, or pipeline by evidence, flags one risk, and scripts what to press each rep on.

YOU GETThe evidence-based sort, one risk per deal, and the press question.
OPEN THE SKILL →
WHERE YOU LAND

The leak named, the deals that are real, the pattern underneath, and a defensible forecast.

EVERY STEP, TESTED AND PASSED
EVAL RESULTPASS4/4 dimensions
HOW WE EVAL →
Skills resolve
Structure + proof
Example chat
Quality judge
  • All 5 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 Head of Sales is about to miss the quarter and the room is arguing theories instead of facts.

── 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, Head of Sales, running the loop in one sitting
S
We are going to miss the quarter and the room is guessing why. I want the real cause before I start cutting spend. Start with the funnel.
S
Step 1· Funnel Metrics
I pinned conversion by stage, velocity, and win rate with every definition spelled out, so the meeting argues about the fix and not the math. One stage is clearly dragging the whole funnel.
Receipt
Demo-to-proposal conversion sits at 22%, down from 41% last quarter.
S
So where is it actually leaking, and is scoring part of the problem?
S
Step 2· Strike Zone Analyst
I traced the leak past that one stage and audited the scoring model behind it. The model is sending reps into a segment that almost never converts, which is where the drop is coming from.
Receipt
38% of demos went to a segment converting at 6%, a scoring escalator set too loose.
S
Now tell me which of the deals still in the forecast are actually real.
S
Step 3· Deal Health Analyst
I gave each open deal a straight verdict and checked real activity and whether it is multi-threaded. A chunk of what is propping up the forecast is single-threaded with no recent buyer activity.
verdictshealthy, slipping, stuck, dead
Receipt
Of $4.2M forecast, $1.5M is stuck or single-threaded and should not be in commit.
S
Is there a pattern under the losses I should know about?
S
Step 4· Pattern Analyst
I read the closed-lost and churned cohorts together by channel. Paid is the weakest performer in both: the highest loss rate and the channel behind 3 of the last 4 churns. I am handing that finding straight to forecast-call-prep as a named risk on every paid-sourced deal still in the forecast.
Receipt
Paid: worst closed-lost rate and 3 of the last 4 churns, flagged as a named risk on the 2 paid-sourced deals in this quarter's pipeline.
S
Rebuild me a forecast number I can defend on Friday.
S
Step 5· Forecast Call Prep
I re-sorted every deal into commit, best-case, or pipeline by evidence, not optimism, folded pattern-analyst's paid-channel finding into the press question for every paid-sourced deal, and scripted what to press each rep on. You walk in with a number and the reasoning behind it.
Receipt
Defensible commit lands at $2.7M, with the press question written for each of the 6 shaky deals, including a named paid-channel check on the 2 sourced from paid.
── 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 · GTM OPERATING RHYTHM
Reporting That Drove Action
Deal Confidence
a forecast the team trusts, built on a score not a guess
-50%+
deal slippage, via the Deal Confidence Score
6 motions
one weekly read: leads, deals, expansion, churn, renewals, forecast
Sales + CS
one operating rhythm for the whole GTM org
WHAT I DID
01
Automate the report itself. The weekly report generated itself from the source of truth, so no one spent a day assembling slides. The time went to the action, not the assembly.
02
Let AI surface what a human read misses. AI read across the whole dataset and pulled the insights a person scanning a dashboard would never see: the account quietly cooling, the team over-consuming its seats, the deal whose activity did not match its stage.
03
Build the sales action layer. The report did not end at a number. It surfaced the hot leads worth working and a deal tracker with every deal risk and next step, each pointed at the rep who could move it.

A report a leader reads and a report a team acts on are different objects. Automate the assembly, let AI surface the insights a human read misses, and build an action layer for every motion, sales and CS, so one weekly read drives hot leads, expansion, churn saves, renewals, and a forecast the team trusts. The report becomes the operating rhythm, not the rear-view mirror.

Dig into the full build
05 / INSTALL THE WHOLE PLAYBOOK

One plugin. 5 skills, bundled.

The whole playbook installs as a single plugin in Claude Code, no copy-pasting 5 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 Diagnose why you are missing the number step by step, and run each skill as we go.
── GET THE NEXT PLAYBOOK

One runnable workflow a week. Free.