Make your CRM mean one thing to everyone.
“Our CRM has 200 fields and nobody agrees on what any of them mean.”
Cuts field sprawl to the ones that drive decisions, locks a definition and query pattern on each, and maps every stage to what must be true to sit in it.
What this skill does
Turns a messy CRM into a source of truth. It interviews you about your process, names the fields that actually drive decisions (not the 200 nobody fills), locks a plain-English definition and a query pattern for each, and maps your pipeline stages to what must be true at every one. The output is a reference every rep, analyst, and downstream skill reads from, so "what does this field mean?" has one answer instead of five.
Inputs
- Works today with: a description of your sales process, your stages, and the decisions you make from CRM data. Paste it and go.
- More powerful connected to a CRM: it reads your actual objects, field names, and fill rates, so the library names real fields and flags the dead ones.
- Sharper with your reporting: it ties each field to the report or metric that depends on it, so you know what breaks if it goes empty.
- Sharper with your process docs: it aligns the stage matrix to the methodology you already run.
How it works
- 1Name the fields that drive decisions
Most CRMs carry hundreds of fields and use twenty. Start from the decisions you make, forecast, prioritize, route, renew, and keep only the fields those decisions depend on. Everything else is noise the library should not bless.
- 2Lock a definition and a query pattern per field
Every kept field gets a one-line plain definition (what it means, who sets it, when) and a query pattern (how a skill or report reads it reliably). No field enters the library without both. This is what kills the "it depends who you ask" problem.
- 3Build the stage matrix
Map every pipeline stage to what must be true to sit in it: the fields that must be filled, the activity that must exist, the exit test. A deal that fails its stage test is misstaged, and the matrix is what lets an analyst catch it.
- 4Define the activity formula
Name what counts as real engagement and from which sources, so "active" and "dark" mean the same thing everywhere. A single-source activity check lies; name the full set (email, calls, meetings, product) and how days-dark is computed.
- 5Flag the dead and the dangerous
Call out fields that are always empty (drop them), fields that mean different things to different teams (redefine them), and fields people game (add a guard). The library is as much about what to stop trusting as what to keep.
Output format
CRM FIELD LIBRARY · [your org] · built [date]
Field Definition Query pattern
Deal_Stage where the opp sits in the process Opportunity.StageName
Next_Step the committed next action + date Opportunity.Next_Step__c
Champion_Identified a named internal advocate exists Contact role = Champion
Last_Activity_Date most recent real engagement max(email, call, meeting)
STAGE MATRIX (exit tests):
Discovery -> problem named + next step set + 1 meeting logged
Validation -> champion identified + success criteria written
Commit -> mutual plan + close date defended by activity
ACTIVITY FORMULA:
active = any of {email reply, call connect, meeting held, product use}
in last 21 days; else days-dark = today - last of those
FLAGGED:
- Lead_Source (32% filled) -> unreliable for routing, fix or drop
- "Engaged" (means 3 different things by team) -> redefine
Tips
- Every field in the library has a plain definition AND a query pattern. No entry with only one.
- The stage matrix has an explicit exit test per stage, never just a name.
- The activity formula names its sources; no single-source "active" claim.
- Dead and ambiguous fields are flagged, not silently included.
- No field is trusted on a fill rate it does not have. Low-fill fields are labeled, not assumed.
Turn a category into a real ICP definition.
Who exactly are we for?
Name the moments that signal value in your product.
What should my product analyst actually measure?