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PLAYBOOK · SALES
✓ APPROVED

Score the whole market, then work the top

Reps burn a third of their week on accounts that were never going to buy, because nobody has ever scored the list. Score them, then work them. This assumes your ICP is already defined; start with icp-playbook first if it is not.

[ SALES OPERATOR ]

Score the whole market, then work the top

G / Groundthe problem that started all of it
Sales

Reps burn a third of their week on accounts that were never going to buy, because nobody has ever scored the list.

Score them, then work them. This assumes your ICP is already defined; start with icp-playbook first if it is not.

A / Assignthe build, block by block, and who owns each one. Open a step to see it run.
HUMAN + AI, IN THE LOOP
The skills run the work. You stay in the loop on the calls that need judgment.
Claude01
Score whether an account is a real ICP fit.
Details
Validate the fit rubric on a sample and catch where your current scoring tool is wrong, before running it at scale.
Connect to analyze
AI
Salesforce02
Turn a pile of accounts into a stack rank with a reason on every row.
Details
Stack-rank the market with a reason on every row.
Powers up with
AI
Claude03
Turn an ICP into a scored, prioritized target list.
Details
Turn the top into a prioritized target list.
Connect to analyze
AI
Salesforce04
Turn "which leads should I work?" into a ranked work plan.
Details
Give each rep a lead-by-lead work plan.
Powers up with
AI
T / Tie backthe result it drove, and how they know
RESULT · 01

One rubric for the market

Score the whole TAM, a tier and a move per account.

OUTPUT · 01

A fit verdict per account

OUTPUT · 02

A stack rank

OUTPUT · 03

A target list

OUTPUT · 04

A per-lead work plan

The Sales Operator
01 / WHY RUN IT
RUN THIS AND YOU CAN
A fit verdict per account
A stack rank
A target list
A per-lead work plan
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
Score whether an account is a real ICP fit.

Rolls firmographics, product usage, hiring intent, and tech stack into one 0-100 verdict, and flags where a scoring tool is wrong.

YOU GETThe weighted composite, the per-source breakdown, and channel tagging.
OPEN THE SKILL →
2✓ TESTED
Turn a pile of accounts into a stack rank with a reason on every row.

Fair across channels, blank fields never scored as zero, with a why behind each account's score.

YOU GETThe three score modes, the gates and escalators, and per-account reasoning.
OPEN THE SKILL →
3✓ TESTED
Turn an ICP into a scored, prioritized target list.

The firmographic and signal filters, the tiering, the personas per account, and the reason each account earns its spot.

YOU GETThe fit gates, the 0-100 score, the tiering, and personas per account.
OPEN THE SKILL →
4✓ TESTED
Turn "which leads should I work?" into a ranked work plan.

Sorts every lead into hot, engaged, cold, or never-touched, computes days-dark, and names the next move per lead.

YOU GETThe 4-state lead classifier, the days-dark decay sort, and a next action per lead.
OPEN THE SKILL →
WHERE YOU LAND

A fit verdict per account, a stack rank, a target list, and a per-lead work plan.

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 2026-09-12
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 lead has a defined ICP but a 2,400-account universe nobody has ever scored, and reps are working the list alphabetically.

03 / WHO ELSE HAS TO MOVE

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.

REVOPS
Account data quality and the scoring runs at scale

Without it: The rubric is right and the rows are wrong

MARKETING
The ICP definition the rubric encodes

Without it: Sales and marketing score the same market two different ways

CS
The retained-customer truth about who actually succeeds

Without it: You rank the market on who buys, not who stays

04 / 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 CHATMorgan, a RevOps lead, running the loop in one sitting
M
Leadership keeps saying our ICP is 'mid-market SaaS' but that's not a real definition. Can you tighten it?
S
Step 1· Icp Analyst
Rolled firmographics, product usage, hiring intent, and tech stack into one 0-100 fit read and broke it down by source so we see what's driving fit. Also flagged where our current scoring tool is calling fit wrong.
Receipt
ICP fit model built, per-source breakdown showing tech-stack and headcount as the two strongest signals.
M
Now run our whole account universe through that and rank them.
S
Step 2· Icp Scoring
Scored every account into a stack rank with a reason on every row, kept it fair across inbound and outbound, and never scored a blank field as a zero. Applied the gates and escalators so a single strong signal can lift an account.
scored2,400
strike310
Receipt
2,400 accounts stack-ranked with a why each, 310 landing in the strike zone.
M
Take that strike-zone group and turn it into an actual target list for the reps.
S
Step 3· Outbound List Builder
Ran the strike-zone accounts through fit gates, tiered them, and tagged the personas to hit inside each one, keeping the reason each account earns its spot. Reps get a list they can work, not a spreadsheet dump.
tier A90
tier B140
Receipt
310 strike-zone accounts turned into a tiered list, top 90 flagged tier A with personas attached.
M
Of that list, which do we work this week and what's the next move on each?
S
Step 4· Prospecting Analyst
Sorted every lead into hot, engaged, cold, or never-touched, computed how many days each has gone dark, and named the next move per lead so nobody guesses. The hot and engaged ones float to the top of the queue.
hot22
engaged48
Receipt
Full list turned into a ranked work plan, 22 hot leads with a named next move, ready for the team Monday.
05 / 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 · TAM + PRIORITIZATION
Scoring the TAM
~4x
more revenue-bearing opps than the prior run rate
~4.5x
more new pipeline created than the prior run rate
~90%
of that pipeline came from strong-fit accounts
100%
of outbound landed on-strike-zone
WHAT I DID
01
One rubric for the whole market. ICP fit earns the base, 40 to 70. Product engagement adds up to 30, the loudest and most honest signal. Buying signals add up to 20, bounded so they can only ever help, never carry a bad-fit account.
02
Three stages, one engine. Fit, product, and intent are scored separately and stacked, so the number always shows its work and no single signal can hide behind another.
03
Every account gets a tier and a move. The output is not a score in a vacuum. It is a tier, Strike or Hot and down, and a specific first action per account, by role, so a rep never has to ask what to do next.

You cannot prioritize a market by scoring one deal at a time. Put the entire TAM through one rubric, fit for the base, product for the lift, intent capped so it can only help, and make reps work the ranked list top-down. The teams that do book more of the right pipeline and stop burning a quarter of their capacity on accounts that were never going to buy.

Dig into the full build
06 / ROLL IT OUT AND INSPECT IT · THE OTHER HALF

The score is only as good as the rubric behind it

The skills hand the team a fit verdict on every account, a stack rank, and a per-lead plan; the revenue comes from reps actually working the top of the rank instead of their pet accounts. Here is how a sales leader gets the team to trust a score enough to obey it.

THE FRAMEWORK BEHIND IT
The sample-first calibration

Before icp-scoring runs at scale, the team hand-scores a sample with icp-analyst, argues the misses, and locks the rubric. Rerun the calibration any time the ICP definition moves.

  1. 01
    Train reps to work the reason

    Every scored row carries a reason. Teach reps to open with the reason in their outreach, so the score shapes the message and not just the order.

  2. 02
    Set the rescore cadence

    The market gets rescored on a fixed rhythm and after any ICP change. A stale rank is a confident way to be wrong.

  3. 03
    Hold the line on the top tier

    Rep time follows the stack rank. In pipeline review, ask about the top-tier accounts not yet worked before anyone presents a long shot.

  4. 04
    Inspect the misses both ways

    Pull the high scores that went nowhere and the low scores that closed, and take both back to the rubric. The prospecting-analyst work plans are only as good as the rank they inherit.

07 / 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 Score the whole market, then work the top step by step, and run each skill as we go.
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