sales-pipeline

name: sales-pipeline description: | Tracks all Hedge Edge deals through pipeline stages, forecasts monthly recurring revenue, identifies stuck or at-risk opportunities, and provides actionable pipeline health reports. Enables data-driven sales decisions with prop-firm-specific deal intelligence.

Sales Pipeline

Objective

Provide real-time visibility into every active Hedge Edge deal from first qualification to close. Forecast MRR growth, flag stalled opportunities before they go cold, and surface patterns (e.g., "FTMO traders close 2 faster than Apex traders") that sharpen the sales playbook.

When to Use This Skill

• A pipeline health report or revenue forecast is requested.
• A deal has been stuck in the same stage for longer than the stage SLA.
• Weekly pipeline review meeting prep is needed.
• A tier-upgrade opportunity is detected (e.g., Starter subscriber adding accounts).
• Win/loss analysis is requested after a deal closes.
• IB revenue pipeline needs separate tracking and forecasting.

Input Specification

`yaml pipeline_request: type: enum[health_report, forecast, stuck_deals, deal_detail, win_loss_analysis, ib_pipeline, weekly_review] required: true

filters: date_range: start: date | null end: date | null stage: list[string] | null # filter by specific stages tier: list[enum[starter, pro, hedger]] | null source: list[enum[discord, landing_page, free_guide, referral, ib_partner]] | null prop_firm: list[string] | null # e.g. ["FTMO", "The5%ers"] min_score: integer | null assigned_to: string | null

deal_id: string | null # for deal_detail requests `

Step-by-Step Process

Step 1 Aggregate Pipeline Data

1. Pull all active deals from Google Sheets CRM ("Leads" tab where stage is NOT closed_won or closed_lost).
2. Pull corresponding Notion deal cards for enriched context (notes, attachments, linked interactions).
3. Query Supabase for current subscription status of any leads who already have accounts.
4. Query Creem.io for recent payment events to catch upgrades, downgrades, or failed payments that affect pipeline value.

Step 2 Execute Requested Analysis

health_report:

1. Calculate pipeline metrics:
   • Total pipeline value: sum of MRR 12 (annual contract value proxy) for all active deals, weighted by stage probability:
     • qualified = 10%, discovery_call_booked = 20%, demo_scheduled = 35%, demo_completed = 50%, proposal_sent = 65%, egotiation = 80%
   • Deal count by stage: histogram of deals per stage.
   • Average deal age: mean days since created_at for active deals.
   • Conversion rates between stages: e.g., 70% of demo_completed proposal_sent.
   • Velocity: average days per stage transition.
2. Break down by tier:
   • Starter deals (/mo 12 = ACV)
   • Pro deals (/mo 12 = ACV)
   • Hedger deals (/mo 12 = ACV)
3. Flag anomalies: stages with conversion rate < 50%, deals with age > 2 the stage SLA.

forecast:

1. Use weighted pipeline to project MRR for the next 30, 60, and 90 days.
2. Factor in historical close rates by tier and source.
3. Add IB commission forecast: estimated new Vantage/BlackBull accounts average monthly commission per account.
4. Present three scenarios: conservative (use lower-bound close rates), expected (historical average), optimistic (upper-bound).
5. Track against monthly MRR target.

stuck_deals:

1. Define stage SLAs:
   • qualified discovery_call_booked: 3 days max
   • discovery_call_booked demo_scheduled: 5 days max
   • demo_scheduled demo_completed: 7 days max (accounts for scheduling lag)
   • demo_completed proposal_sent: 2 days max
   • proposal_sent
     egotiation or closed_*: 5 days max
   • egotiation closed_*: 7 days max
2. Flag any deal exceeding its stage SLA.
3. For each stuck deal, generate a recommended action:
   • "Lead went silent after demo send a recap email with the ROI calculation for their 4 FTMO accounts."
   • "Proposal sent 6 days ago, no response follow up via Discord DM with a limited-time IB bonus offer."
   • "Discovery call booked but no-showed trigger no-show sequence from call-scheduling skill."
4. Prioritise stuck deals by pipeline value (Hedger > Pro > Starter).

deal_detail:

1. Pull the complete record for deal_id: lead data, all interactions, stage history, proposal details, payment status.
2. Calculate days in pipeline and days in current stage.
3. List all touchpoints chronologically.
4. Show the recommended next action and optimal timing.

win_loss_analysis:

1. Pull all closed_won and closed_lost deals in the specified date range.
2. Calculate:
   • Overall win rate
   • Win rate by tier, source, prop firm, and platform
   • Average sales cycle length for wins vs. losses
   • Most common loss reasons (price, timing, no need, competitor, went silent)
   • Revenue from closed-won deals (MRR and ACV)
3. Surface actionable insights: "Traders from FTMO close at 42% vs. 18% from Apex prioritise FTMO-sourced leads."

ib_pipeline:

1. Track leads who opened Vantage or BlackBull accounts via Hedge Edge IB links.
2. Calculate: conversion rate (subscriber IB-referred broker account), estimated monthly commission per account, total IB revenue pipeline.
3. Identify subscribers who are NOT yet using Vantage/BlackBull these are IB upsell opportunities.
4. Forecast IB commission revenue alongside SaaS MRR.

weekly_review:

1. Compile a structured weekly summary:
   • New leads added this week (count, sources, average score)
   • Deals that advanced a stage
   • Deals closed (won + lost, with revenue and loss reasons)
   • Stuck deals requiring attention
   • MRR added this week, total MRR
   • IB conversions this week
   • Key actions for next week

Output Specification

yaml pipeline_output: report_type: string generated_at: datetime summary: string # 23 sentence executive summary metrics: total_pipeline_value_weighted: float total_pipeline_value_unweighted: float active_deal_count: integer deals_by_stage: dict[string, integer] deals_by_tier: dict[string, integer] average_deal_age_days: float mrr_current: float mrr_forecast_30d: float mrr_forecast_60d: float mrr_forecast_90d: float close_rate_overall: float ib_revenue_current: float ib_revenue_forecast: float stuck_deals: list[object] # each with deal_id, stage, days_stuck, recommended_action insights: list[string] # actionable observations action_items: list[object] # prioritised next steps with owners and deadlines

API & Platform Requirements

Quality Checks

• Pipeline value calculations use weighted probabilities by stage never raw unweighted sums in forecasts.
• Stage SLAs are enforced: every deal exceeding its SLA appears in the stuck-deals list.
• Forecast includes both SaaS MRR and IB commission revenue as separate line items.
• Win/loss analysis includes at least 3 actionable insights, not just raw numbers.
• Weekly review is generated every Monday by 09:00 UTC and distributed via Railway-hosted automation script.
• Deal counts in the pipeline report match the actual CRM row count reconciliation check on every report.
• Tier-specific ACV values are correct: Starter=, Pro=, Hedger=.
• No deal appears in both active pipeline and closed lists simultaneously.
• IB pipeline tracks conversion from subscriber IB account, not just lead subscriber.