Skill · em Publicar e operar

forecast-accuracy-review

Evaluate demand-forecast quality honestly - WMAPE, bias and Forecast Value Added against a naive benchmark over a rolling-origin backtest. Use when the user mentions forecast accuracy, MAPE, demand planning performance, tahmin doğruluğu, talep tahmini, or asks whether a forecasting process or tool…

Procedência

Antes de instalar

1 arquivo · 3,5 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/forecast-accuracy-review/.

d=".claude/skills/forecast-accuracy-review"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/operations/forecast-accuracy-review"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.claude/skills/forecast-accuracy-review/.

d="$HOME/.claude/skills/forecast-accuracy-review"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/operations/forecast-accuracy-review"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Codex

Neste projeto: instala em .agents/skills/forecast-accuracy-review/.

d=".agents/skills/forecast-accuracy-review"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/operations/forecast-accuracy-review"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.agents/skills/forecast-accuracy-review/.

d="$HOME/.agents/skills/forecast-accuracy-review"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/operations/forecast-accuracy-review"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Antigravity

Neste projeto: instala em .agents/skills/forecast-accuracy-review/.

d=".agents/skills/forecast-accuracy-review"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/operations/forecast-accuracy-review"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Global: instala em ~/.gemini/antigravity-cli/skills/forecast-accuracy-review/.

d="$HOME/.gemini/antigravity-cli/skills/forecast-accuracy-review"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/operations/forecast-accuracy-review"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.forecast-accuracy-review

Prévia do SKILL.md

---
name: forecast-accuracy-review
description: Evaluate demand-forecast quality honestly - WMAPE, bias and Forecast Value Added against a naive benchmark over a rolling-origin backtest. Use when the user mentions forecast accuracy, MAPE, demand planning performance, tahmin doğruluğu, talep tahmini, or asks whether a forecasting process or tool is worth…
---

# Forecast Accuracy Review

A forecast is only worth what it adds over the free alternative: shipping last period's number. Every review must answer "how many points does this process add over naive?" before any model discussion.

## Required data

Per-SKU demand history at the planning bucket (usually monthly): `sku`, `period`, `qty`. If evaluating an existing forecast, also the forecast values with their creation dates (to avoid hindsight leakage). 18+ periods per SKU for a meaningful backtest; flag SKUs with less.

## Workflow

1. **Profile the demand first.** Per SKU compute mean, CV and zero-period share; classify smooth / erratic / intermittent / lumpy (defaults: CV 0.5 and 1.0 boundaries, intermittency at >25% zero periods - state them, adjust to natural breaks). Accuracy expectations differ by class; never report one blended number alone…
2. **Set the benchmarks.** Naive (last period) always; seasonal naive when 2+ full seasons exist. These are non-negotiable controls.
3. **Backtest rolling-origin.** One-step-ahead forecasts for each of the last 6+ periods, expanding window, using only data before each origin. A single train/test split is one lucky draw - do not accept it.
4. **Score with honest metrics:**
   - **WMAPE** = sum(|error|) / sum(actual) - the volume-weighted headline
   - **Bias** = sum(error) / sum(actual) - direction; a fine WMAPE with persistent bias is quietly building excess stock or stockouts
   - MAPE only as a footnote, and always disclose how many zero-actual periods it dropped
5. **Deliver the FVA verdict.** FVA = WMAPE(naive) - WMAPE(candidate), per segment and overall. Negative FVA means the process destroys value - say it plainly.
6. **Validate.** Recompute WMAPE for one model directly from the raw backtest rows and confirm it matches the table before presenting.

## Pitfalls to check explicitly

- **MAPE with zeros**: undefined on zero-actual periods; silently dropping them fakes precision on intermittent SKUs.
- **MAPE asymmetry** rewards under-forecasting (errors capped at 100% below, unbounded above).
- **Aggregation mix**: a good total can hide terrible A-item accuracy; always show the value-weighted cut.
…

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