Use this skill when configuring CRM workflows, managing sales pipelines, building forecasting models, or optimizing CRM data hygiene. Triggers on Salesforce, HubSpot, CRM workflows, pipeline management, deal stages, forecasting, CRM automation, and any task requiring CRM architecture or process optimization.
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Use this skill when configuring CRM workflows, managing sales pipelines, building forecasting models, or optimizing CRM data hygiene. Triggers on Salesforce, HubSpot, CRM workflows, pipeline management, deal stages, forecasting, CRM automation, and any task requiring CRM architecture or process optimization.
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CRM Management
An opinionated framework for designing, configuring, and optimizing CRM systems
that actually reflect reality - not wishful thinking. This skill covers pipeline
architecture, lead scoring, forecasting methodology, automation design, and data
hygiene. Aimed at revenue operations, sales leaders, and technical implementers
who need CRM to be a system of truth, not a graveyard of stale opportunities.
When to use this skill
Trigger this skill when the user:
Designs or redesigns a sales pipeline (stage definitions, exit criteria, deal properties)
Configures lead scoring in Salesforce, HubSpot, or similar CRM platforms
Builds a revenue forecast - weighted, categorical, or AI-assisted
Automates deal progression, task creation, or notification workflows
Audits data quality and needs a dedup, enrichment, or field decay strategy
Builds sales dashboards, win/loss reports, or pipeline velocity metrics
Integrates CRM with marketing automation, product analytics, or billing systems
Do NOT trigger this skill for:
General sales coaching or objection handling (this is a CRM architecture skill, not a sales playbook)
Writing email sequences or sales copy (use a copywriting or outbound skill instead)
Key principles
Data hygiene is non-negotiable - A CRM full of stale, duplicated, or
manually-entered guesswork is worse than no CRM. Garbage in, garbage out
applies to forecasts, reports, and automation. Treat data quality as a
first-class engineering concern: define ownership, set decay rules, and
automate enrichment from day one.
Automate the boring stuff - Reps should spend time selling, not updating
fields. Any task that follows a predictable rule (create follow-up task when
stage advances, notify manager when deal exceeds threshold, enrich lead on
creation) should be automated. Human judgment is reserved for exceptions.
Pipeline reflects reality - Every stage must represent a verifiable buyer
action, not a rep's optimism. Stages without exit criteria are opinions. Exit
criteria must be objective and observable: "Demo completed" not "Rep thinks
they're interested." Review pipeline stages whenever win rates diverge from
forecast accuracy.
Forecast with methodology - Never let reps enter a single probability
number. Pick one forecasting method (weighted, categorical, or AI) and apply
it consistently. Mix methods only at the rollup layer. A forecast is only as
good as the pipeline data behind it - fix pipeline hygiene before blaming
the model.
Less fields, more adoption - Every field added to a record is friction.
Every required field that reps don't understand is a source of garbage data.
Audit fields quarterly: if a field hasn't been used in reporting in 90 days,
archive it. Default to fewer, well-defined fields with validation rules over
many optional ones nobody fills in.
Core concepts
CRM object model
CRM platforms organize data around a standard object hierarchy. Understanding
the relationships prevents misdesign.
Object
Represents
Key relationships
Lead
An unqualified inbound contact, not yet associated to an account
Converts to Contact + Account + Opportunity
Contact
A known individual at a company
Belongs to Account; linked to Opportunities
Account
A company or organization
Parent of Contacts and Opportunities
Opportunity
A specific deal or revenue event in progress
Belongs to Account; has a Stage, Amount, and Close Date
Lead vs Contact: Leads are pre-qualification. Once a lead meets your ICP
criteria (or a sales rep accepts it), convert it. Do not store active selling
conversations on Lead records - move to Opportunity.
Account hierarchy: Enterprise deals often span subsidiaries. Model parent-child
account relationships to roll up ARR accurately.
Pipeline stages
A pipeline stage is a milestone in the buyer's journey, not the seller's activity.
Each stage must have:
Name: Short, buyer-centric label
Definition: What is true about the buyer at this stage
Entry criteria: What must have happened to move in
Exit criteria: What must happen before advancing
Probability: Default win probability used in weighted forecasting
Deal properties
Standard properties every opportunity should carry:
Property
Type
Purpose
amount
Currency
ACV or total contract value
close_date
Date
Expected close, used in forecasting
stage
Enum
Current pipeline stage
forecast_category
Enum
Committed / Best Case / Pipeline / Omitted
deal_source
Enum
Inbound / Outbound / Channel / Expansion
next_step
Text
Single next action with owner and date
competitor
Multi-select
Competitors actively in the deal
loss_reason
Enum
Required on Closed Lost; drives win/loss analysis
Automation triggers
CRM workflows are event-driven. Standard trigger types:
Record create - runs when an object is first created (lead created, deal opened)
Field change - runs when a specific field value changes (stage advances, amount updates)
Time-based - runs N days before/after a date field (deal stale for 14 days, close date in 7 days)
Criteria match - runs when a record first matches a filter (deal amount > $50k, lead score > 80)
Common tasks
Design pipeline stages
Define stages bottom-up: start from Closed Won and work backward to the first
meaningful buyer commitment. A typical B2B SaaS pipeline:
Stage
Definition
Exit criteria
Default probability
Prospecting
Identified as target, no contact yet
Meeting booked
5%
Discovery
First meeting held; pain and budget being explored
More than 7 active stages is almost always too many. Stages that reps skip
consistently signal the stage does not reflect a real buyer milestone.
For SaaS, enterprise, and PLG templates, see references/pipeline-templates.md.
Set up lead scoring in CRM
Lead scoring combines demographic fit (ICP match) and behavioral engagement.
Use two dimensions to avoid conflating them:
Profile score (ICP fit):
Company size in target range: +15
Industry match: +20
Job title is economic buyer or champion: +25
Geography in territory: +10
Technology stack match (from enrichment): +15
Engagement score (interest signals):
Demo request or pricing page visit: +30
Email open: +2, Email click: +8
Webinar attendance: +15
Free trial signup: +25
Score decay: -5 per week of inactivity
Routing rule: Route to sales when profile score >= 40 AND engagement score >= 30.
Never route on engagement alone - a curious student visiting your pricing page is
not an MQL.
Build a forecasting model
Choose one primary methodology. Do not mix until you understand the trade-offs.
Weighted pipeline (default):
Multiply opportunity amount by stage probability
Sum across all open deals in a period
Works when: stages are well-defined, reps update stages accurately
Breaks when: reps sandbag or inflate stages to manage their number
Categorical (commit-based):
Each rep assigns a forecast category: Committed, Best Case, Pipeline, Omitted
Manager rolls up by taking Committed as floor, Best Case as upside
Works when: reps are disciplined about commit culture
Breaks when: reps over-commit to look good or under-commit to sandbag
AI / predictive:
CRM platform (Salesforce Einstein, HubSpot AI) scores each deal on close likelihood
Based on historical signals: stage velocity, engagement, deal age, competitor presence
Works when: you have 12+ months of clean historical data (200+ won/lost deals)
Do not use if your data is less than a year old or heavily incomplete
Rollup structure: Rep -> Manager -> VP -> CRO. Each level reviews the layer
below before submitting up. Lock forecasts weekly on Monday; review actuals Friday.
Automate deal progression workflows
Automate repetitive mechanics, not judgment calls. Standard automation patterns:
Trigger
Action
Purpose
Opportunity stage = Demo
Create task: "Send follow-up email within 24h" assigned to owner
Enforces follow-through
Opportunity stage = Proposal
Notify manager via Slack
Deal visibility
Opportunity amount > $50k
Flag as "Strategic Deal", notify VP
Escalation routing
Close date passes with stage not Closed
Send stale deal alert to rep and manager
Pipeline hygiene
Lead created from website form
Enrich via Clearbit/Apollo, route by territory
Speed to lead
Deal moves to Closed Lost
Require loss_reason before save
Win/loss data integrity
Automation should enforce process, not replace it. If an automation creates
a task that reps always dismiss, the process is wrong, not the automation.
Maintain data hygiene
Data hygiene has four levers: deduplication, enrichment, decay management, and
field governance.
Deduplication:
Run dedup rules on email (primary key for contacts), domain (primary key for accounts)
Use fuzzy matching for company names (Acme Corp vs Acme Corporation vs Acme, Inc.)
Set merge rules: retain the older record's ID, take the newer record's field values
Run dedup on import and on a scheduled weekly job
Enrichment:
Auto-enrich new leads and accounts from data providers (Clearbit, ZoomInfo, Apollo)
Enrich fields: company size, industry, technology stack, LinkedIn URL, phone
Re-enrich accounts on a 90-day schedule to catch firmographic changes
Do not overwrite manually-entered values with enriched values without review
Decay management:
Mark leads as "stale" if no activity in 60 days; remove from active scoring
Archive opportunities with no stage movement in 90 days (move to pipeline hold stage)
Purge GDPR-regulated contacts on schedule per data retention policy
Field governance:
Audit all custom fields quarterly: usage rate, last populated date
Archive fields used in fewer than 20% of records
Required fields must have picklist validation; free-text required fields breed inconsistency
Build sales dashboards and reports
Every sales dashboard should answer one of three questions: Where are we? Where
are we going? Why did deals win or lose?
Dashboard
Key metrics
Pipeline health
Open pipeline by stage, pipeline coverage ratio (pipeline / quota), average deal age per stage
Forecast
Committed vs Best Case vs quota, forecast vs prior week delta, at-risk deals (close date < 14 days, no activity in 7 days)
Activity
Calls, emails, meetings per rep per week; stage conversion rates
Win/loss analysis
Win rate by deal source, competitor, deal size, industry; average sales cycle by segment
Rep performance
Quota attainment, pipeline created, average deal size, stage conversion funnel
Report cadences: Daily - pipeline alerts. Weekly - forecast review. Monthly - win/loss and funnel analysis. Quarterly - field governance and process audit.
Integrate CRM with marketing automation
CRM-MAP integration is a bidirectional sync. Design the data contract carefully:
Never let MAP overwrite fields that sales reps manually update
Use a sync log or webhook audit trail so mismatches can be diagnosed
Anti-patterns
Anti-pattern
Why it's wrong
What to do instead
Stages based on rep activity ("Proposal Sent")
Tracks what the seller did, not what the buyer decided
Redefine stages around verifiable buyer actions and decisions
Single probability field reps fill manually
Reps game it to match their gut; forecasts become meaningless
Derive probability from stage; use forecast category for rep judgment
Required fields without picklists
Reps type anything to get past validation; data is unqueryable
Replace free-text required fields with controlled picklists
CRM fields duplicated in spreadsheets
Shadow systems diverge; actual data is always "in the spreadsheet"
Mandate CRM as system of record; kill the spreadsheets
Automating before stages are stable
Automation bakes in bad process; expensive to unwind
Freeze stage definitions for one full quarter before automating
Enrichment overwriting sales data
Reps lose trust in CRM when their updates get overwritten
Set enrichment to fill empty fields only; never overwrite
Gotchas
Automating before stage definitions are stable - Building workflow automations on top of pipeline stages that are still being debated bakes bad process into code. When stages change, you have to unwind automations, field mappings, and reports simultaneously. Freeze stage definitions for one full quarter before automating them.
Enrichment overwriting sales rep data - When a data enrichment provider (Clearbit, ZoomInfo) updates a field like company size or industry, it can silently overwrite a value a rep manually entered from a real sales conversation. Reps notice, stop trusting the CRM, and revert to spreadsheets. Configure enrichment to fill empty fields only, never overwrite populated ones.
Lead routing on engagement score alone - A high engagement score means someone is interested - not that they are a qualified buyer. Routing a university student who visits your pricing page 10 times to sales wastes rep time and trains reps to distrust MQL routing. Always require a minimum profile (ICP fit) score alongside engagement before routing.
Forecast categories without commit culture - A categorical forecast ("Committed / Best Case / Pipeline") only works if reps treat "Committed" as a hard promise. Without explicit commit culture training and consequences for consistent miss-commits, reps either over-commit to look good or under-commit to sandbag. The methodology is useless without the discipline.
Required free-text fields - Making a free-text field required (like "Next Steps" as a text box) guarantees garbage data. Reps type anything to save the record: "TBD", "follow up", or nothing meaningful. Replace free-text required fields with picklists that have clear, actionable options.
References
For detailed templates and implementation guidance, read the relevant file from
the references/ folder:
references/pipeline-templates.md - Pipeline stage templates for SaaS, enterprise, and PLG motions
Only load a references file if the current task requires it - they are detailed
and will consume context.
Companion check
On first activation of this skill in a conversation: check which companion skills are installed by running ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/ .claude/skills/ .agent/skills/ .agents/skills/ 2>/dev/null. Compare the results against the recommended_skills field in this file's frontmatter. For any that are missing, mention them once and offer to install:
