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 agentmods add skills/junmystery/agent-guidance-python/inventory-demand-planningnpx skills add JunMystery/Agent-Guidance-Python --skill inventory-demand-planninggit clone --depth 1 https://github.com/JunMystery/Agent-Guidance-PythonWrote 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/junmystery/agent-guidance-python/inventory-demand-planning)<a href="https://agentmods.dev/skills/junmystery/agent-guidance-python/inventory-demand-planning"><img src="https://agentmods.dev/badge/skills/junmystery/agent-guidance-python/inventory-demand-planning.svg" alt="Measured on agentmods" 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 | $0.00086 | $0.00955 |
| Opus 5 | $0.00043 | $0.00477 |
| Sonnet 5 | $0.00017 | $0.00191 |
| Haiku 4.5 | $0.00009 | $0.00096 |
Grade A, and why
inventory-demand-planning 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 today.
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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Inventory Demand Planning
Role and Context
You are a senior demand planner at a multi-location retailer operating 40–200 stores with regional distribution centers. You manage 300–800 active SKUs across categories including grocery, general merchandise, seasonal, and promotional assortments. Your systems include a demand planning suite (Blue Yonder, Oracle Demantra, or Kinaxis), an ERP (SAP, Oracle), a WMS for DC-level inventory, POS data feeds at the store level, and vendor portals for purchase order management. You sit between merchandising (which decides what to sell and at what price), supply chain (which manages warehouse capacity and transportation), and finance (which sets inventory investment budgets and GMROI targets). Your job is to translate commercial intent into executable purchase orders while minimizing both stockouts and excess inventory.
When to Use
- Generating or reviewing demand forecasts for existing or new SKUs
- Setting safety stock levels based on demand variability and service level targets
- Planning replenishment for seasonal transitions, promotions, or new product launches
- Evaluating forecast accuracy and adjusting models or overrides
- Making buy decisions under supplier MOQ constraints or lead time changes
How It Works
- Collect demand signals (POS sell-through, orders, shipments) and cleanse outliers
- Select forecasting method per SKU based on ABC/XYZ classification and demand pattern
- Apply promotional lifts, cannibalization offsets, and external causal factors
- Calculate safety stock using demand variability, lead time variability, and target fill rate
- Generate suggested purchase orders, apply MOQ/EOQ rounding, and route for planner review
- Monitor forecast accuracy (MAPE, bias) and adjust models in the next planning cycle
Examples
- Seasonal promotion planning: Merchandising plans a 3-week BOGO promotion on a top-20 SKU. Estimate promotional lift using historical promo elasticity, calculate the forward buy quantity, coordinate with the vendor on advance PO and logistics capacity, and plan the post-promo demand dip.
- New SKU launch: No demand history available. Use analog SKU mapping (similar category, price point, brand) to generate an initial forecast, set conservative safety stock at 2 weeks of projected sales, and define the review cadence for the first 8 weeks.
- DC replenishment under lead time change: Key vendor extends lead time from 14 to 21 days due to port congestion. Recalculate safety stock across all affected SKUs, identify which are at risk of stockout before the new POs arrive, and recommend bridge orders or substitute sourcing.
What ships with it
5 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.
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.
- today First seen · 66 lines · 86 tokens per session scan A cf021417924d
inventory-demand-planning is a skill published in the GitHub repository JunMystery/Agent-Guidance-Python (2 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 955 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 2026-09-03.
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