Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add finsilabs/awesome-ecommerce-skills --skill demand-forecastinggit clone --depth 1 https://github.com/finsilabs/awesome-ecommerce-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/demand-forecasting)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/demand-forecasting"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/demand-forecasting/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/demand-forecasting"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/demand-forecasting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.02748 |
| Opus 5 | $0.00014 | $0.01374 |
| Sonnet 5 | $0.00006 | $0.00550 |
| Haiku 4.5 | $0.00003 | $0.00275 |
Grade A, and why
demand-forecasting scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 219 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Demand Forecasting
Overview
Demand forecasting uses historical sales data, seasonal patterns, and lead times to predict how much inventory you'll need and when to reorder. Chronic stockouts or overstock situations are usually a sign that reorder points are based on intuition rather than data. Purpose-built inventory planning tools handle this for most merchants — custom forecasting code is only necessary for unique operational requirements.
When to Use This Skill
- When chronic stockouts or overstock situations indicate that current reorder points are set incorrectly
- When building automated replenishment recommendations to reduce manual inventory review
- When planning inventory for seasonal peaks (Black Friday, back-to-school, holiday season)
- When you have 12+ months of sales history and want to extract meaningful demand patterns
- When integrating with supplier lead times and purchase order workflows for end-to-end replenishment
Core Instructions
Step 1: Determine your platform and choose the right forecasting tool
| Platform | Recommended Tool | Why |
|---|---|---|
| Shopify | Inventory Planner (Shopify App Store) or Cogsy | Inventory Planner connects directly to Shopify, analyzes 12+ months of sales history, calculates reorder points, and generates purchase orders |
| WooCommerce | ATUM Inventory Management (free/premium) or Inventory Planner | ATUM provides reorder point management natively in WooCommerce; Inventory Planner has a WooCommerce connector for advanced forecasting |
| BigCommerce | Inventory Planner or Linnworks | Both have BigCommerce native integrations and handle multi-location inventory forecasting |
| Multi-channel | Skubana (now Extensiv) or Linnworks | Handles inventory forecasting across Shopify, WooCommerce, Amazon, and eBay from a single dashboard |
| Custom / Headless | Build a time-series analysis layer on top of your order database | Use moving averages, seasonal decomposition, and safety stock formulas against your historical sales data |
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- evals/database-schema-new-product-fallback-and/criteria.json 2.8 KB
- evals/database-schema-new-product-fallback-and/task.md 4.0 KB
- evals/demand-decomposition-and-time-series-for/criteria.json 2.9 KB
- evals/demand-decomposition-and-time-series-for/task.md 4.7 KB
- evals/reorder-point-and-safety-stock-calculati/criteria.json 2.5 KB
- evals/reorder-point-and-safety-stock-calculati/task.md 3.3 KB
- tile.json 230 B
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 219 lines · 28 tokens per session scan A 968868d6252e
demand-forecasting is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (52 stars, last pushed 6mo ago), licensed MIT. It adds 28 tokens to every session and 2,748 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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