Sales Pipeline Review

Pulls deal and meeting data from the user’s CRM, analyzes it across seven dimensions, and produces an executive summary and detailed diagnostic. Triggered when the user says ‘review my pipeline’, ‘pipeline report’, ‘how’s our pipeline looking’, ‘deal review’, ‘1:1 prep’, or ‘weekly sales review’.

Goal: Pull deal and meeting data from whatever CRM the user tracks their pipeline in, analyze it over a chosen period, and produce a report that answers the questions a founder, sales leader, or AE actually cares about.

Tool	Purpose	Required
Salesforce / HubSpot / Pipedrive / Close	Pull deal and pipeline data via API or CSV export	No (one required)
Supabase	Query deals or outreach tables directly	No (optional)
Google Sheets	Read pipeline data from a spreadsheet	No (optional)
Notion MCP	Query a Notion pipeline database	No (optional)

Trigger: Run on demand when the user asks for a pipeline review, deal review, 1:1 prep, board meeting prep, weekly sales review, or asks about meeting quality, conversion rates, or pipeline health. Also runs when an upstream workflow (Pipeline Ops, daily BDR rhythm) triggers an end-of-period review.

Setup

On first run, collect and store these preferences. Skip on subsequent runs.

Data Source Config

Question	Options	Stored As
Where do you track your pipeline?	Salesforce / HubSpot / Pipedrive / Close / Supabase / Google Sheets / CSV / Notion / Other	crm_tool
How do we access it?	API / Export CSV / MCP tools / Direct query	access_method
What object represents a “deal” in your system?	Opportunity / Deal / Lead / Meeting / Custom	deal_object

Pipeline Stage Definitions

Question	Purpose	Stored As
What are your pipeline stages in order?	Map data to a standard funnel	pipeline_stages
Which stage means “qualified”?	Qualification rate calculation	qualified_stage
Which stage means “closed won”?	Win rate calculation	won_stage
Which stage means “closed lost”?	Loss analysis	lost_stage
What is your expected sales cycle length? (days)	Identify stuck deals	expected_cycle_days

Field Mapping

Different CRMs use different field names. Map the user’s fields to standard analysis fields:

Standard Field	Purpose	Stored As	Examples
Deal name	Identification	field_deal_name	”Name”, “Deal Name”, “Opportunity Name”
Company	Account grouping	field_company	”Company”, “Account”, “Organization”
Stage	Funnel position	field_stage	”Stage”, “Pipeline Stage”, “Status”
Owner	Rep-level analysis	field_owner	”Owner”, “Assigned To”, “Rep”
Source	Channel attribution	field_source	”Lead Source”, “Source”, “Channel”
Created date	Period filtering	field_created_date	”Created Date”, “Created At”, “Date Added”
Close date	Velocity tracking	field_close_date	”Close Date”, “Expected Close”, “Closed At”
Amount	Revenue analysis	field_amount	”Amount”, “Deal Value”, “ARR”, “MRR”
Last activity date	Stale deal detection	field_last_activity	”Last Activity”, “Last Modified”, “Last Touched”
Loss reason	Loss analysis	field_loss_reason	”Loss Reason”, “Closed Lost Reason”

Minimum viable data: deal name + stage + created date. All other fields enrich but aren’t required.

Benchmarks (Optional)

Question	Purpose	Stored As
Target meeting volume per week/month?	Compare actuals to goals	target_meetings
Target qualification rate?	Flag if below target	target_qual_rate
Target win rate?	Flag if below target	target_win_rate
Target pipeline value?	Revenue gap analysis	target_pipeline_value

Store config in: clients/<client-name>/config/pipeline-review.json or equivalent.

Do not ask again in future runs.

Step 1: Pull Pipeline Data

Pull deal and meeting data from the configured CRM for the specified time period.

Ask the user for the review period if not provided:

“What period should I analyze? Options: last 7 days, last 14 days, last 30 days, last 90 days, or a custom date range.”

Based on crm_tool and access_method, pull data as follows:

CRM	Access Method	How to Pull
Salesforce	API / CSV	SOQL query or user uploads CSV export
HubSpot	API / CSV	Deals API or user uploads CSV export
Pipedrive	API / CSV	Deals API or CSV export
Close	API / CSV	Leads/Opportunities API or CSV
Supabase	Direct query	Query deals/outreach_log tables
Google Sheets	Sheets API / CSV	Read sheet or user exports CSV
Notion	Notion MCP	Query database
CSV	File read	User provides file path

Pull two datasets:

• Current period: All deals created or active within the specified date range
• Comparison period: Same-length prior period (e.g., if reviewing last 2 weeks, also pull the 2 weeks before that)

Normalize all data into a standard structure regardless of source:

deals: [
  {
    id, name, company, stage, owner, source,
    created_date, close_date, last_activity_date,
    meeting_date, amount, qualification_status,
    loss_reason, days_in_current_stage, total_age_days
  }
]

Present a human checkpoint:

“Data Pulled Source: [CRM name] Current period: [start] to [end] — [N] deals Comparison period: [start] to [end] — [N] deals Fields available: [list] Fields missing: [list — analysis will adapt]

Data looks correct? (Y/n)“

Step 2: Analyze Pipeline

Run all seven analyses on the current period data. Where comparison period exists, calculate period-over-period trends.

Analysis 1 — Volume Metrics

Calculate: total deals created, meetings booked, meetings held, no-show rate, weekly run rate, period-over-period change, vs. target_meetings.

Analysis 2 — Qualification Breakdown

Calculate: qualified deals, unqualified deals, pending qualification, qualification rate, top disqualification reasons, vs. prior period, vs. target_qual_rate.

A deal is qualified if it reached or passed qualified_stage. A deal is unqualified if it was closed lost before reaching qualified_stage.

Analysis 3 — Source Attribution

Group by field_source: count deals, meetings, qualified deals, won deals. Calculate qualification rate and win rate per source. Flag if any single source drives more than 60% of pipeline.

Analysis 4 — Stage Distribution and Velocity

Group active deals by stage. Calculate deal count, revenue, weighted revenue, average days in stage, and stage-to-stage conversion rates.

Use these stage probability defaults unless actual data is available:

Stage	Probability
Lead	5%
Meeting Booked	10%
Meeting Held	20%
Qualified	40%
Proposal Sent	60%
Negotiation	80%
Closed Won	100%
Closed Lost	0%

Analysis 5 — Stuck Deals and At-Risk Pipeline

A deal is “stuck” if days_in_current_stage exceeds 2× the average for that stage, or exceeds expected_cycle_days total.

Default stuck thresholds (use if stage-average data is insufficient):

Stage	Expected Max Days
Lead	3
Meeting Booked	7
Meeting Held	5
Qualified	10
Proposal Sent	14
Negotiation	14

Also flag: deals with no activity for more than 14 days; deals where total_age_days > 1.5× expected_cycle_days; any single deal > 30% of total pipeline value.

Analysis 6 — Win/Loss Analysis

Calculate: win rate overall, win rate by source, win rate by owner, average deal size (won), average days to close, top loss reasons, loss stage distribution.

Analysis 7 — Forecast and Coverage

Only run if target_pipeline_value or revenue targets are configured.

Calculate: pipeline coverage ratio (weighted pipeline / remaining quota), commit forecast (Negotiation + Proposal stage deals), best case forecast (commit + Qualified deals × historical win rate), gap to target, required deals and meetings to close the gap.

Coverage assessment:

Coverage	Assessment
3x+	Healthy
2–3x	Adequate
<2x	At risk
<1x	Critical

Step 3: Generate Report

Generate two formats from the analysis output.

Executive Summary — one page, numbers and trends, what a founder reads in 60 seconds:

# Pipeline Review — [Period Type]: [Start Date] to [End Date]

## Snapshot

| Metric             | This Period | Prior Period | Change   |
| Meetings booked    | [N]         | [N]          | [+/-Z%]  |
| Meetings held      | [N]         | [N]          | [+/-Z%]  |
| Qualification rate | [X%]        | [Y%]         | [+/-Z pts] |
| Win rate           | [X%]        | [Y%]         | [+/-Z pts] |
| Pipeline value     | $[X]        | $[Y]         | [+/-Z%]  |
| Avg deal size      | $[X]        | $[Y]         | [+/-Z%]  |
| Avg days to close  | [X]         | [Y]          | [+/-Z]   |

## Red Flags
- [Metric trending down significantly]
- [Stuck deals above threshold]
- [Pipeline coverage below 2x]
- [Single source >60% of pipeline]
- [No-show rate above 20%]

## Green Lights
- [Metrics trending up]
- [Sources performing well]
- [Stages moving faster than expected]

## Top 3 Actions
1. [Most impactful action this week]
2. [Second most impactful]
3. [Third most impactful]

Detailed Diagnostic — full data tables, stage-by-stage breakdown, source analysis, stuck deal list, and specific recommendations. Sections: Volume, Qualification, Source Effectiveness, Stage Distribution and Velocity, Stuck Deals, Win/Loss Analysis, Forecast and Coverage (if targets set), Recommendations.

Each recommendation must cite the specific data point that drives it. Apply this logic:

Pattern	Recommendation
Qualification rate <40%	Tighten ICP targeting — top DQ reason is [X]
No-show rate >20%	Implement confirmation reminders 24h and 1h before meetings
One source >60% of pipeline	Diversify — if this channel underperforms, pipeline collapses
High-quality source with low volume	Scale it — [source] has [X]% qual rate but only [Y]% of volume
Deals stuck in a specific stage	Unblock [stage] — [N] deals averaging [Y] days (2× normal)
Win rate declining	Win rate dropped from [X]% to [Y]% — top loss reason is now [Z]
Pipeline coverage <2x	Need [Z] more qualified deals — requires [W] meetings at current rates
Cycle lengthening	Bottleneck at [stage] — deals [X] days slower vs prior period

Present the executive summary first, then offer the detailed diagnostic:

“[Executive Summary rendered]

---

Full detailed diagnostic is also available.

Actions from this review:

1. [High priority recommendation]
2. [High priority recommendation]
3. [Medium priority recommendation]

Stuck deals requiring immediate attention:

Deal	Company	Stage	Days Stuck	Action

Want to see the full diagnostic? Or take action on any of these recommendations?”

Step 4: Export and Share

Save or share the report based on user preference:

Destination	How
Markdown file	Save to clients/<client>/reports/pipeline-review-[date].md
Google Sheets	Export data tables (metrics, deal list, source breakdown)
Notion	Push to a Notion database page via Notion MCP
Slack	Send executive summary to a specified channel
Email	Send via agentmail
Display only	Render inline (default)

Output Format

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Pipeline Review — [Period Type]: [Start] to [End]

Snapshot

Metric	This Period	Prior Period	Change
Meetings booked	[N]	[N]	[+/-Z%]
Meetings held	[N]	[N]	[+/-Z%]
Qualification rate	[X%]	[Y%]	[+/-Z pts]
Win rate	[X%]	[Y%]	[+/-Z pts]
Pipeline value	$[X]	$[Y]	[+/-Z%]
Avg deal size	$[X]	$[Y]	[+/-Z%]
Avg days to close	[X]	[Y]	[+/-Z]

Red Flags: [List or “None”] Green Lights: [List or “None”] Top 3 Actions:

1. [Action]
2. [Action]
3. [Action]

Stuck Deals

Deal	Company	Stage	Days Stuck	Owner	Action

---

Edge Cases

• Missing amount field: Skip revenue metrics, weighted pipeline, and forecast. Volume and stage analysis still run.
• Missing source field: Skip source attribution (Analysis 3). All other analyses still run.
• Missing loss reason field: Skip loss reason breakdown. Win/loss rate still calculates.
• Missing last activity date: Skip no-activity deal detection. Stuck deals are still detected via stage duration.
• No comparison period data: Run single-period analysis only. Omit all period-over-period trend columns.
• No targets configured: Skip forecast and coverage analysis (Analysis 7). Note in output that targets are not set.
• Loss reasons mostly blank or “Other”: Flag data hygiene issue in recommendations. Note that loss analysis is unreliable until reps fill in reasons consistently.
• CRM not connected or CSV not provided: Ask the user to either connect the CRM via MCP or upload a CSV export before proceeding.