Add multi-platform export generator (single source of truth)
Make the library multi-platform without duplicating content. Each skills/<name>/SKILL.md body remains the single source of truth; a new generator renders platform-ready exports from it. - scripts/build-exports.mjs — dependency-free Node generator with a PLATFORMS registry so new platforms (Gemini, Cursor, …) are a few lines. Ships ChatGPT exports at exports/chatgpt/<bundle>/<skill>/SYSTEM_PROMPT.md (172 skills), plus generated index READMEs. Supports --platform and --check. - exports/ — generated ChatGPT system prompts, ready to paste into a Custom GPT. - .github/workflows/check-generated.yml — fails a PR if exports or web/skills.json drift from the source skills. - README "Works With" now documents the ready-to-use exports and regen command. - CHANGELOG + SKILL-AUTHORING-STANDARD note the generated artifacts. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_016JWn5jRD5tcEFKrubjQ6Px
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# Sales Forecasting Model Skill
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Produces a structured sales forecast framework — from pipeline conversion modelling to scenario analysis. Built for revenue and sales leaders who need a defensible forecast, not a spreadsheet guess.
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## Required Inputs
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Ask the user for these if not provided:
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- **Business type** (SaaS / Transactional / Services / Marketplace)
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- **Forecast period** (monthly / quarterly / annual)
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- **Sales motion** (inbound / outbound / channel / PLG / mixed)
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- **Current pipeline data** (number of deals, stages, values — rough is fine)
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- **Historical conversion rates** (if available — otherwise model will flag as assumption)
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- **Average deal size and sales cycle length**
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## Output Structure
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---
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# Sales Forecast: [Team / Business] — [Period]
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**Forecast type:** [Bottom-up pipeline / Top-down quota / Capacity-based / Hybrid]
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**Period:** [Month / Quarter / Year]
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**Created:** [Date]
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**Forecast owner:** [Name]
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---
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## 1. Forecast Methodology
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**Chosen approach:** [Bottom-up / Top-down / Hybrid] — and why for this context.
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Bottom-up (recommended when pipeline data exists):
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> Start from real deals in the pipeline. Apply stage-by-stage conversion rates. Sum to a revenue number.
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Top-down (useful for planning, not for calling a number):
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> Start from market or quota. Work backwards to activity targets.
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---
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## 2. Pipeline Stage Model
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Define the sales stages and the expected conversion rate between each:
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| Stage | Description | % of deals that advance | Avg time in stage |
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| Prospect | Identified, not contacted | — | — |
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| Qualified | Discovery done, confirmed fit | [X%] | [N days] |
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| Proposal | Proposal sent | [X%] | [N days] |
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| Negotiation | Commercial terms being agreed | [X%] | [N days] |
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| Closed Won | Contract signed | [X%] | — |
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**Overall pipeline conversion rate:** [X%] (Qualified → Closed Won)
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**Average sales cycle:** [N days from Qualified to Close]
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---
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## 3. Current Pipeline Snapshot
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| Stage | Number of deals | Total value | Expected close (weighted) |
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|---|---|---|---|
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| Qualified | [N] | £[X] | £[X × conversion %] |
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| Proposal | [N] | £[X] | £[X × conversion %] |
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| Negotiation | [N] | £[X] | £[X × conversion %] |
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| **Total** | | **£[X]** | **£[weighted total]** |
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**Coverage ratio:** [Weighted pipeline ÷ target = X×]
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*Rule of thumb: 3× pipeline coverage is needed for confident forecast; 2× is tight; below 1.5× is at risk.*
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---
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## 4. Scenario Analysis
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| Scenario | Assumption | Revenue | Probability |
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|---|---|---|---|
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| Upside | All Negotiation + top 50% of Proposal close | £[X] | [%] |
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| Base | Weighted pipeline conversion at historical rates | £[X] | [%] |
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| Downside | Conversion rates drop 20% from historical | £[X] | [%] |
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**Committed forecast:** £[X] — [The number the forecast owner is willing to call. Between base and downside.]
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---
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## 5. Key Assumptions Log
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Every forecast is a set of assumptions. Name them explicitly so they can be updated:
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| Assumption | Value | Confidence | Source | Last updated |
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|---|---|---|---|---|
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| Avg deal size | £[X] | High/Med/Low | [Last N deals] | [Date] |
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| Sales cycle | [N days] | | | |
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| Close rate from Proposal | [X%] | | | |
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| Seasonal factor | [e.g. Q4 +20%] | | | |
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| Churn/contraction | [X% of ARR at risk] | | | |
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---
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## 6. Activity-Based Sanity Check
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Work backwards from the forecast to check if the required activity is achievable:
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To hit £[target]:
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- Deals needed to close: [N] (target ÷ avg deal size)
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- Qualified pipeline needed (at current conversion): [N deals or £value]
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- Discovery calls needed per week to build that pipeline: [N]
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- Outreach needed per week (at [X%] meeting rate): [N]
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**Does the team have capacity to generate this?** [Yes / No — flag if not]
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---
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## Quality Checks
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- [ ] Forecast methodology is stated (not just a number)
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- [ ] Stage conversion rates are based on historical data or flagged as assumptions
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- [ ] Coverage ratio is calculated
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- [ ] Three scenarios are modelled (not just one number)
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- [ ] Assumption log is explicit and dated
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- [ ] Activity sanity check confirms the forecast is achievable with current capacity
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## Example Trigger Phrases
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- "Build a sales forecast for [period]"
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- "Create a pipeline model for [team/business]"
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- "Help me build a bottom-up revenue forecast"
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- "What is our forecast for Q[N] based on current pipeline?"
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## Anti-Patterns
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- [ ] Do not present a single forecast number without scenario analysis — a forecast without upside and downside cases hides risk
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- [ ] Do not use 100% confidence on conversion rates that are not backed by historical data — flag them as assumptions
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- [ ] Do not skip the activity sanity check — a forecast number that requires unreachable activity levels is not credible
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- [ ] Do not use top-down quota as the only forecast method when pipeline data exists — bottom-up is more accurate and defensible
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- [ ] Do not omit the coverage ratio — without it, stakeholders cannot assess whether the pipeline is sufficient to hit target
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