---
title: "commercial-forecaster — Claude Code Plugin & Agent Skill"
description: "Use when building a quarterly bookings forecast, ARR projection, pipeline forecast, NRR projection, or commit/best-case/pipe-only board number . Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw."
---

# commercial-forecaster

<div class="page-meta" markdown>
<span class="meta-badge">:material-handshake-outline: Commercial</span>
<span class="meta-badge">:material-identifier: `commercial-forecaster`</span>
<span class="meta-badge">:material-github: <a href="https://github.com/alirezarezvani/claude-skills/tree/main/commercial/skills/commercial-forecaster/SKILL.md">Source</a></span>
</div>

<div class="install-banner" markdown>
<span class="install-label">Install:</span> <code>claude /plugin install commercial-skills</code>
</div>


## Purpose

Help Commercial leaders answer three questions at the forecast moment:

1. **What's the commit / best-case / pipe-only number?** (3-tier bookings forecast with disclosed assumptions)
2. **Which cohorts are leaking, and is the consolidated NRR hiding the leak?** (per-cohort NRR/GRR projection over horizon)
3. **Which funnel stages are reliable, and which are statistical noise?** (per-stage coefficient-of-variation confidence band)

The skill recommends **three forecast numbers + an explicit assumption block**. The CRO presents the number, the board sees the assumptions, the theatre dies.

## When to use

- Building the quarterly bookings forecast for the board
- Preparing the QBR forecast where the CFO will ask "what's the commit, what's the best-case, what's the pipe-only"
- Projecting ARR for next 4-8 quarters using cohort retention data
- Suspecting a consolidated NRR number is hiding a leaky recent cohort
- Pipeline-coverage is shrinking and you need to know which stages are still trustworthy
- You're being asked for a "single number" and you need the structured answer that surfaces the assumption

**Do not use for:**
- Backward-looking financial close + reporting → `finance/financial-analysis`
- Strategic financial planning (multi-year, scenario, fundraise) → `c-level-advisor/cfo-advisor`
- "Should we hire a VP Sales?" / territory design / comp plan → `c-level-advisor/cro-advisor`
- Setting prices → sibling `pricing-strategist` (projects revenue *at* prices already set)
- Per-deal discount approval → sibling `deal-desk`

## Workflow

### Step 1 — Intake pipeline + cohort + historical conversion data

Fill `assets/forecast_intake_template.md` (≈ 20 min). Captures: opportunity list with stage/amount/close-date/age/last-activity; historical stage-to-stage conversion across last 4Q and last 12Q; per-cohort ARR + per-quarter retention + expansion data; funnel stage names with 12-quarter conversion history.

### Step 2 — Run 3-tier bookings forecast

```
scripts/bookings_forecaster.py --input intake.json --profile saas --output markdown
```

Outputs three numbers — **commit**, **best-case**, **pipe-only** — each with the conversion rate applied, the data window used (last-4Q vs. last-12Q weighted 70/30), and the time-to-close probability adjustment. Surfaces variance between commit and pipe-only as the pipeline-risk indicator.

**The assumption block is non-optional.** If you remove it, the forecast becomes theatre.

### Step 3 — Project cohort-level ARR

```
scripts/cohort_arr_projector.py --input intake.json --output markdown
```

Computes per-cohort NRR + GRR over the projection horizon. Flags any cohort whose NRR is declining vs. the trailing-cohort average — these are the leaky cohorts that the consolidated number will hide for 2-3 quarters before the leak surfaces in the topline.

Output includes the consolidated NRR/GRR trajectory + the cohort heatmap + a leaky-cohort callout.

### Step 4 — Score per-stage funnel confidence

```
scripts/funnel_confidence_scorer.py --input intake.json --output markdown
```

Per stage: mean conversion %, standard deviation, coefficient of variation (CoV = StDev / Mean), confidence band (HIGH < 10%, MEDIUM 10-25%, LOW 25-50%, VERY LOW > 50%). Recommends treatment per stage: extend-data-window, treat-as-soft-floor, or commit-quality.

### Step 5 — Assemble the forecast deck

Take the 3-tier bookings number + cohort heatmap + funnel confidence into the QBR / board deck. **The assumption block goes on the slide with the number.** If the slide has a single number and no assumption block, the slide is theatre.

## Scripts

- `scripts/bookings_forecaster.py` — 3-tier bookings forecast (commit / best-case / pipe-only) with disclosed conversion-rate + data-window + weighting block
- `scripts/cohort_arr_projector.py` — per-cohort NRR/GRR projection over horizon with leaky-cohort callout
- `scripts/funnel_confidence_scorer.py` — per-stage CoV-based confidence bands with treatment recommendation

All scripts: stdlib only. `--help` and `--sample` work on all three.

## References

- `references/saas_forecasting_canon.md` — Skok, Tunguz, OpenView, BVP, Pacific Crest/KeyBanc, ProfitWell, Patrick Campbell
- `references/cohort_analysis_canon.md` — Andrew Chen (a16z), Brian Balfour, Skok, Ramanujam, OpenView, Lenny Rachitsky, Reforge
- `references/forecast_anti_patterns.md` — McKinsey, Tunguz, OpenView, MIT Sloan, Bain, Forrester, Pacific Crest

## Assumptions

- **Historical conversion is the prior, not the truth.** Last 4Q is weighted 70%, last 12Q is weighted 30%. The blend captures regime change (recent slowdown) without overfitting to a single bad quarter. Window + weighting are surfaced in every output.
- **A forecast without a disclosed assumption block is theatre.** This is the skill's hard rule. The CLI refuses to omit the assumption block.
- **Cohort decomposition reveals leaks 2-3 quarters before the consolidated number does.** Reporting NRR without per-cohort breakdown hides the leak.
- **CoV (coefficient of variation) is the right discipline for stage confidence.** A stage with mean conversion 40% and stdev 4% (CoV 10%) is HIGH confidence; mean 40% stdev 20% (CoV 50%) is VERY LOW. The same average masks very different reliability.
- **Industry profile tunes priors, not truth.** Profile shifts default stage-conversion rates by industry; your historical data overrides.
- **The skill emits three numbers and an assumption block.** The CRO picks the commit number, owns the trade-off, and walks the board through the variance.

## Anti-patterns

- **Single-number forecast with no confidence band.** The board asks for "the number"; the discipline is to present three with named assumptions. See `forecast_anti_patterns.md`.
- **Using last-12-quarter conversion blindly.** Hides recent slowdown. The 70/30 blend on last-4Q vs. last-12Q corrects this.
- **Reporting NRR without cohort decomposition.** The consolidated number can be flat while a recent cohort is leaking 15 pp; the leak surfaces in the topline 2-3 quarters later. Always decompose.
- **Treating best-case as commit.** The CFO will eat you. Best-case includes weighted-stage opps that have a < 50% time-to-close probability; commit only includes commit-grade stages.
- **Hiding the assumption block.** The skill refuses; if you remove it manually, you own the theatre.
- **No leaky-cohort callout.** If `cohort_arr_projector.py` flags a cohort and you suppress the flag in the deck, the leak owns you next quarter.
- **Ignoring late-stage opp age.** A "verbal" deal that's been verbal for 180 days is not a commit. The bookings forecaster downweights stalled opps automatically; do not re-up them by hand.
- **No pipeline-coverage check.** Industry rule of thumb: forecast > pipeline ÷ 3 is anti-pattern. The tool surfaces the ratio; respect it.

## Distinct from

- **`finance/financial-analysis`** — backward-looking financial close, GAAP/IFRS reporting, variance vs. budget. commercial-forecaster is forward-looking pipeline math.
- **`c-level-advisor/cfo-advisor`** — strategic multi-year financial planning, fundraise scenarios, runway. commercial-forecaster is one input to the CFO, not the strategy.
- **`c-level-advisor/cro-advisor`** — strategic CRO judgment: "do we hire a VP Sales?", territory design, comp plan, when to add a sales engineer. commercial-forecaster is the math the CRO uses; cro-advisor is the judgment the CRO applies.
- **sibling `pricing-strategist`** — sets the price (model + range). commercial-forecaster *projects revenue at those prices*. Pricing comes first; forecast comes after.
- **sibling `deal-desk`** — per-deal scoring + discount approval routing. commercial-forecaster aggregates the pipeline that deal-desk operates on day-by-day.

## Forcing-question library (Matt Pocock grill discipline)

Walked one at a time by `/cs:grill-commercial` or the orchestrator. Recommended answer + canon citation per question. Never bundled.

1. **"What conversion rate are you using, and is it last-4Q or last-12Q?"**
   Recommended: a 70/30 blend (last-4Q weighted 70%, last-12Q weighted 30%). Last-12Q alone hides recent slowdown; last-4Q alone overfits one bad quarter.
   Canon: Tomasz Tunguz (Theory Ventures) — forecasting studies show single-window conversion estimates miss regime change at ~3-quarter lag.

2. **"What's your pipeline coverage ratio, and is your commit above pipeline ÷ 3?"**
   Recommended: 3x coverage is the SaaS-industry floor; below 3x means your commit is structurally unsupported.
   Canon: Pacific Crest / KeyBanc SaaS Survey — top-quartile SaaS companies maintain 3.0-4.5x pipeline coverage against committed bookings.

3. **"Can you show me NRR by cohort, not just consolidated?"**
   Recommended: never report a consolidated NRR without the per-cohort breakdown. Leaky cohorts hide in averages.
   Canon: Patrick Campbell (ProfitWell) + David Skok — cohort-driven retention decomposition surfaces leaks 2-3 quarters before consolidated NRR moves.

4. **"What's the variance (CoV) on each stage's conversion rate over the last 12 quarters?"**
   Recommended: CoV < 10% → commit-grade; 10-25% → moderate; 25-50% → soft floor only; > 50% → do not use this stage for forecasting.
   Canon: MIT Sloan forecasting research / Hyndman & Athanasopoulos (*Forecasting: Principles and Practice*) — CoV on the input series predicts forecast accuracy more reliably than mean.

5. **"How long has each late-stage opp been in late-stage?"**
   Recommended: stage-age > 2x the median stage-duration → treat as stalled, exclude from commit, keep in pipe-only.
   Canon: David Skok (*For Entrepreneurs*) — stalled-opp identification by stage-age is the #1 forecast hygiene practice in top-decile SaaS pipelines.

6. **"Is your best-case forecast within 30% of your pipe-only?"**
   Recommended: if best-case is < 50% of pipe-only, your stage-conversion assumptions are pessimistic and you're sandbagging; if best-case > 80% of pipe-only, you're hockey-sticking.
   Canon: McKinsey research on forecast bias + OpenView SaaS benchmarks — most teams operate in one of two failure modes: sandbagging (commit << earnings) or hockey-sticking (commit >> earnings).

7. **"What assumption block accompanies the number on the board slide?"**
   Recommended: every forecast number on a board slide names (a) the conversion rate, (b) the data window, (c) the weighting choice, (d) the pipeline-coverage ratio. No assumption block = the slide is theatre.
   Canon: Bain & Company commercial-forecasting practice + Forrester pipeline-coverage research — undisclosed-assumption forecasts have 2.3x higher variance against actuals than disclosed-assumption forecasts.

Walk depth-first. Lock 1-3 before opening 4-7. After all 7 are answered, invoke `bookings_forecaster.py` → `cohort_arr_projector.py` → `funnel_confidence_scorer.py` in sequence.
