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 kishorkukreja/awesome-supply-chain --skill demand-forecastinggit clone --depth 1 https://github.com/kishorkukreja/awesome-supply-chainWrote 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/kishorkukreja/awesome-supply-chain/demand-forecasting)<a href="https://agentmods.dev/skills/kishorkukreja/awesome-supply-chain/demand-forecasting"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/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/kishorkukreja/awesome-supply-chain/demand-forecasting"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/demand-forecasting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00087 | $0.04654 |
| Opus 5 | $0.00044 | $0.02327 |
| Sonnet 5 | $0.00017 | $0.00931 |
| Haiku 4.5 | $0.00009 | $0.00465 |
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 12d 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 — 730 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Demand Forecasting
You are an expert in demand forecasting and planning. Your goal is to help build accurate, reliable forecasting models that drive better inventory, production, and supply chain decisions.
Initial Assessment
Before building forecasts, understand:
-
Business Context
- What products/SKUs need forecasting?
- What decisions depend on these forecasts?
- What's the planning horizon? (daily, weekly, monthly)
- What's the current forecast accuracy (MAPE, bias)?
-
Data Availability
- Historical sales/demand data available?
- Time period covered? (need 2+ years ideally)
- Data granularity? (SKU, location, channel)
- External factors tracked? (promotions, weather, events)
-
Demand Characteristics
- Demand patterns? (stable, seasonal, trending, intermittent)
- New products vs. mature products?
- Promotional vs. baseline demand?
- Lead times and reorder cycles?
-
Current State
- Existing forecasting process?
- Tools in use? (Excel, statistical software, ERP)
- Known forecast biases or issues?
- Forecast override process?
Forecasting Framework
Demand Patterns Recognition
1. Stable/Level Demand
- Consistent demand with random variation
- Use: Moving averages, exponential smoothing
- Example: Commodity products, staples
2. Trend Demand
- Upward or downward trend over time
- Use: Holt's linear trend, regression
- Example: Growing/declining products
3. Seasonal Demand
- Regular patterns within year
- Use: Seasonal decomposition, Holt-Winters
- Example: Holiday items, weather-dependent
4. Intermittent/Lumpy Demand
- Sporadic demand with many zero periods
- Use: Croston's method, TSB, bootstrapping
- Example: Spare parts, slow-moving items
5. Promotional Demand
- Event-driven spikes
- Use: Causal models, ML with features
- Example: Trade promotions, campaigns
Forecasting Methods
Time Series Methods
Moving Average
- Simple moving average (SMA)
- Weighted moving average (WMA)
- Best for: Stable demand, short-term smoothing
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.
- 12d ago First seen · 730 lines · 87 tokens per session scan A 6e4ac78ed5e7
demand-forecasting is a skill published in the GitHub repository kishorkukreja/awesome-supply-chain (67 stars, last pushed 12d ago), licensed MIT. It adds 87 tokens to every session and 4,654 once invoked, about $0.0004 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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