sales-forecast

name	sales-forecast
description	Builds and validate revenue forecasts with pipeline coverage and gap analysis. Use when you're not confident in your commit number, pipeline coverage looks thin, a forecast call is coming and you need to tighten the number, weighted pipeline doesn't match gut feel, or there's a gap between target and what's realistic. Do NOT use for individual deal analysis (use /sales-deal-inspect) or building outbound cadences (use /sales-cadence).
argument-hint	[describe scope, time period, known numbers, and primary concern]
license	MIT
version	1.0.0
tags	["sales","forecasting","pipeline","revenue"]

Build a Revenue Forecast

Help the user build and validate a revenue forecast — from category modeling through pipeline coverage analysis, deal-level inspection, and gap planning.

Step 1 — Gather context

If references/learnings.md exists, read it first for accumulated knowledge.

Ask the user:

1. Scope:

   • A) Individual rep forecast
   • B) Team / pod forecast
   • C) Regional forecast
   • D) Company-wide forecast

2. Time period:

   • Current quarter
   • Next quarter
   • Half / full year
   • Custom period

3. What numbers do you have? (provide what you know)

   • Quota / target
   • Closed-won so far this period
   • Commit (deals you're confident will close)
   • Best case (deals that could close with things going right)
   • Total open pipeline
   • Average sales cycle length
   • Historical win rate

4. What's your primary concern?

   • A) We're behind quota and need a gap plan
   • B) I need to validate my commit number
   • C) I need to present a forecast to leadership
   • D) Pipeline coverage feels thin
   • E) Too many deals are slipping from commit to best case
   • F) Other — describe it

If the user's request already provides most of this context, skip directly to the relevant step. Lead with your best-effort answer using reasonable assumptions (stated explicitly), then ask only the most critical 1-2 clarifying questions at the end — don't gate your response behind gathering complete context.

Step 2 — Forecast model

Build a forecast model table:

Category	# Deals	Total Value	Win Probability	Weighted Value
Closed Won			100%
Commit			85-95%
Best Case			40-60%
Pipeline (Stage 3+)			15-30%
Early Pipeline (Stage 1-2)			5-10%
Upside (not in pipeline yet)			2-5%

These are typical probability ranges — adjust based on the team's historical conversion data if available. Teams with strong qualification tend toward the higher end; teams early in building pipeline discipline should use the lower end.

Forecast summary

Metric	Value
Quota
Closed Won
Weighted forecast (sum of weighted values)
Expected outcome (most likely landing zone)
Gap to quota
Coverage ratio (total pipeline / remaining quota)
Commit coverage (commit / remaining quota)

Forecast scenarios

• Worst case: Closed Won + (Commit × 80%) — assumes some commit deals slip
• Most likely: Closed Won + (Commit × 90%) + (Best Case × 40%)
• Best case: Closed Won + (Commit × 95%) + (Best Case × 60%) + (Pipeline × 15%)

Present as a range: "Based on current pipeline, expect to land between $X (worst) and $Y (best), most likely around $Z."

Step 3 — Pipeline coverage analysis

Metric	Current	Benchmark	Status
Coverage ratio (pipeline / quota)		3-4x for new business, 2-3x for expansion	Green/Yellow/Red
Commit coverage (commit / remaining gap)		1.0x+ means commit covers the gap
Average deal size		Compare to quota-required deal size
Average cycle length		Deals must have enough runway to close in period
Win rate		Historical vs. current period
Pipeline creation rate		$ created per week/month — is it accelerating or slowing?
Stage conversion rates		Stage 1→2, 2→3, 3→4, etc. — where are deals stalling?

Coverage analysis rules

• Coverage < 2x: Critical — not enough pipeline to hit quota even optimistically. Immediate pipeline generation needed.
• Coverage 2-3x: Thin — likely to miss unless win rates are above average. Focus on deal acceleration and pipeline gen.
• Coverage 3-4x: Healthy for new business — focus on execution and deal quality.
• Coverage 4x+: Strong coverage — focus on deal progression and closing, not more pipeline.
• Commit > remaining gap: You have enough in commit to cover the gap. Focus shifts to deal execution and preventing slippage.
• Deals closing after period end: Flag any "commit" deals with close dates after the period ends — these aren't real commit.

Step 4 — Deal-level inspection

For each deal in Commit and Best Case, create an inspection table:

Deal	Value	Stage	Days in stage	Close date	Risk flags	Confidence
						High/Med/Low

Risk flags to check

• Close date pushed more than once
• No activity in 14+ days
• Single-threaded (one contact)
• No compelling event
• In stage longer than 2x average
• Economic buyer not engaged
• Competitor mentioned but not addressed
• Budget not confirmed

Recommendations per deal

For each deal, recommend one of:

• Keep in Commit: Strong deal, high confidence, clear path to close
• Move to Best Case: Has potential but too many open risks for commit
• Move to Pipeline: Significant unknowns — not ready for commit or best case
• Pull in: Deal could close faster than planned — what would accelerate it?
• Push to next period: Won't close this period — move out and plan accordingly
• Qualify out: Deal isn't real — remove from pipeline

Step 5 — Gap plan

If there's a gap between the forecast and quota, build a plan to close it:

Lever	Description	Potential value	Actions	Timeline
Pull-in	Accelerate deals currently slated for next period		Identify 2-3 deals that could close sooner with the right push (executive meeting, POC, special terms)	This week
Accelerate stalls	Reactivate stalled pipeline deals		Re-engage with new value prop, bring in executive sponsor, offer assessment/workshop	2 weeks
Expansion	Upsell/cross-sell existing customers		Identify customers with low product adoption, recent growth, or upcoming renewal	2-4 weeks
New outbound	Create new pipeline via outbound		Blitz campaign to high-intent accounts, leverage trigger events (funding, hiring, tech changes)	4-8 weeks
Partner/referral	Source deals through partners or referrals		Activate partner relationships, request customer referrals, co-sell with tech partners	2-6 weeks

For each lever, answer:

1. How much can it realistically contribute? (be conservative — gap plans are usually optimistic)
2. What specific actions will you take this week? (not "do more outbound" — specific companies, people, messages)
3. Who owns it? (rep, manager, SE, executive)
4. When will you know if it's working? (set a checkpoint date)

Gap plan math

• Gap: Quota − (Closed Won + Weighted Commit + Weighted Best Case)
• Gap plan target: Gap × 1.5 (plan for more than you need, since not all levers will work)
• Minimum viable gap close: At least 50% of gap plan should come from Pull-in and Accelerate (fastest to materialize)

For platform-specific forecasting guidance (Momentum, Sybill, Oliv, Outreach, Weflow, People.ai, Veloxy, Scratchpad, Dialpad), see references/platforms.md.

Before recommending a specific platform skill

This skill covers a strategy domain across many platforms. Before pointing the user to any specific platform skill (any /sales-{platform} listed in ## Related skills, e.g., /sales-mailshake, /sales-klaviyo, /sales-apollo), read that platform skill's actual SKILL.md first. The 1-line description in ## Related skills is enough to identify a candidate — it's not enough to commit to it or to write a prompt that invokes it well.

How to read it:

• If ~/.claude/skills/{skill-name}/SKILL.md exists locally, Read it.
• For sales-* skills, WebFetch directly from this repo: https://raw.githubusercontent.com/sales-skills/sales/main/skills/{skill-name}/SKILL.md — e.g., for sales-mailshake: https://raw.githubusercontent.com/sales-skills/sales/main/skills/sales-mailshake/SKILL.md.
• For non-sales-* skills (third-party), look up {org}/{repo} in ~/.claude/skills/sales-do/references/skill-sources.md if installed and fetch the same skills/{skill-name}/SKILL.md path under that repo.

After reading, ground your recommendation in something concrete from the SKILL.md (its scope, a sub-flow, its argument-hint shape, or a "Do NOT use for..." negative trigger). Align any generated invocation with the platform skill's argument-hint. If the platform skill turns out not to fit the user's situation, swap to another or handle the question here directly rather than recommending a poor fit.

Related skills

• /sales-gong — Gong platform help (Gong Forecast module — widely considered weak, ~40% of customers stack Clari)
• /sales-salesloft — Salesloft Forecast module for submission workflows and AI-assisted predictions
• /sales-deal-inspect — Deep-dive on individual deals in your forecast
• /sales-cadence — Build outbound cadences for gap-plan pipeline generation
• /sales-clari-copilot — Clari Copilot platform help — conversation intelligence tightly integrated with Clari's forecasting engine, deal scoring from call signals
• /sales-revenue-io — Revenue.io platform help — Revenue Intelligence dashboards and deal scoring for Salesforce-native teams
• /sales-momentum — Momentum platform help — AI revenue orchestration with automated CRM updates, MEDDIC Autopilot, AI coaching, churn signals, executive briefs (acquired by Salesforce Feb 2026)
• /sales-modjo — Modjo platform help — EU-native deal intelligence, Ask Modjo AI pipeline queries, CRM auto-fill for forecast accuracy, GDPR-compliant
• /sales-veloxy — Veloxy platform help — Salesforce-native field sales with predictive intelligence and automatic activity logging for better forecast data
• /sales-do — Not sure which skill to use? The router matches any sales objective to the right skill. Install: npx skills add sales-skills/sales --skill sales-do

Gotchas

• Don't use pipeline total without weighting by stage. Claude will sometimes say "you have $2M in pipeline against a $1M quota, so you're covered." Raw pipeline total is meaningless — only weighted pipeline matters. Always apply stage-based win probabilities.

• Don't assume historical win rates apply to the current quarter. Win rates shift based on deal mix, market conditions, new competitors, and team changes. If the user provides historical rates, use them as a starting point but flag that current-quarter dynamics may differ.

• Don't forget to account for slipped deals from last quarter. Deals that pushed from last quarter inflate current-quarter pipeline but often have lower close probability (they already missed one deadline). Flag these and weight them more conservatively.

• Don't ignore seasonality. Q4 and fiscal year-end typically see higher close rates due to budget pressure. Q1 often sees longer cycles as budgets reset. Ask about the company's fiscal year when it matters.

• Don't present a single forecast number without a range. Always give worst/most likely/best case scenarios. A single number creates false precision and sets the user up for a bad forecast call.

• Self-improving: If you discover something not covered here, append it to references/learnings.md with today's date.

Examples

Example 1: Quarterly forecast build

User says: "Build my team's Q2 forecast. Quota is $2M, we've closed $800k, commit is $600k across 4 deals, best case is $400k, total pipeline is $1.8M, 6 weeks left." Skill does:

1. Builds a forecast model with weighted values across all categories
2. Calculates coverage ratio (1.5x — flags as thin)
3. Presents worst/most likely/best case scenarios
4. Creates a gap plan with specific levers to close the gap Result: Complete forecast ready for the leadership call, with a gap plan if needed

Example 2: Commit validation

User says: "Validate my $500k commit — Deal A ($200k, negotiation, strong champion), Deal B ($150k, proposal, no EB meeting), Deal C ($150k, demo stage, verbal interest only)." Skill does:

1. Inspects each deal against risk criteria
2. Recommends keeping Deal A in commit, moving Deal B to best case, moving Deal C to pipeline
3. Adjusts commit to $200-350k with reasoning Result: Defensible commit number the rep can present to their manager

Troubleshooting

Don't have all the numbers

Solution: Start with what you know. The skill can build a useful forecast from just quota + closed-won + pipeline total. It will flag what's missing and estimate where possible. Even a rough forecast with assumptions stated is better than no forecast.

Forecast keeps missing — always too optimistic

Solution: Apply stricter win probability weights. Most teams over-weight commit (use 85% not 95%) and best case (use 40% not 60%). Check for "commit creep" — deals that sit in commit for multiple forecast periods without closing. The deal-level inspection step catches these patterns.

Gap plan feels unrealistic

Solution: Apply the 50% rule — at least half of gap plan value should come from pull-in and accelerate levers (fastest to materialize). New outbound takes 4-8 weeks to generate pipeline, so it won't help this quarter. Be conservative on each lever and plan for 1.5x the gap.