inventory-demand-planning

inventory-demand-planning is a skill for Claude Code, Codex from Jamkris/everything-gemini-code. It costs 86 tokens per session (5,451 once invoked), scanned A, a copy of inventory-demand-planning, MIT.

A set of methods for forecasting product demand and deciding how much stock to keep and reorder across multiple retail locations. It covers seasonal products, promotions, new products, and different levels of demand variation.

In plain words
What is it for?
Use it to prepare demand forecasts, set safety stock, plan replenishment, assess promotions, and review forecast accuracy.
Why use it?
It helps retailers avoid running out of products while also avoiding more inventory than they can reasonably sell.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to prepare demand forecasts, set safety stock, plan replenishment, assess promotions, and review forecast accuracy.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jamkris/everything-gemini-code/inventory-demand-planning
Install

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.

Any agent
npx skills add Jamkris/everything-gemini-code --skill inventory-demand-planning
Clone the repo
git clone --depth 1 https://github.com/Jamkris/everything-gemini-code

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for inventory-demand-planning

README.md
[![agentmods](https://agentmods.dev/badge/skills/jamkris/everything-gemini-code/inventory-demand-planning/github.svg)](https://agentmods.dev/skills/jamkris/everything-gemini-code/inventory-demand-planning)
Your own site
<a href="https://agentmods.dev/skills/jamkris/everything-gemini-code/inventory-demand-planning"><img src="https://agentmods.dev/badge/skills/jamkris/everything-gemini-code/inventory-demand-planning/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.

agentmods 80×15 button for inventory-demand-planning

Your own site · 80×15
<a href="https://agentmods.dev/skills/jamkris/everything-gemini-code/inventory-demand-planning"><img src="https://agentmods.dev/badge/skills/jamkris/everything-gemini-code/inventory-demand-planning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 86 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,451 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 95% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00086 $0.05451
Opus 5 $0.00043 $0.02726
Sonnet 5 $0.00017 $0.01090
Haiku 4.5 $0.00009 $0.00545

Measured 5d ago against content hash 355eb6dbb9f4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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 5d 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.

Origin

This is a copy

95% identical to inventory-demand-planning — 46 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/inventory-demand-planning/SKILL.md · 248 lines

How it starts

The opening of the file, as written. The whole thing — 248 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

  1. Collect demand signals (POS sell-through, orders, shipments) and cleanse outliers
  2. Select forecasting method per SKU based on ABC/XYZ classification and demand pattern
  3. Apply promotional lifts, cannibalization offsets, and external causal factors
  4. Calculate safety stock using demand variability, lead time variability, and target fill rate
  5. Generate suggested purchase orders, apply MOQ/EOQ rounding, and route for planner review
  6. 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.

Read the full file on GitHub · 248 lines

Changes

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.

  1. 5d ago First seen · 248 lines · 86 tokens per session scan A 355eb6dbb9f4

Subscribe to this mod's changes

inventory-demand-planning is a skill published in the GitHub repository Jamkris/everything-gemini-code (88 stars, last pushed 3mo ago), licensed MIT. It adds 86 tokens to every session and 5,451 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to inventory-demand-planning, differing in 46 lines, and is treated as a copy.

Related

Other skills, from other repositories

doku-payment-gateway

Expert guide for integrating DOKU Payment Gateway (Jokul API v2). Covers HMAC-SHA256 header signature calculation, Checkout & Direct APIs (VA, QRIS, E-Wallet, Credit Card), webhook notification verification, and sandbox/production setup / Panduan ahli integrasi DOKU Payment Gateway.

roedyrustam/vibes-plug · 71 tokens

codeck

Route explicit requests from a host coding agent to one or more locally configured AI executors through Codeck, attach Markdown or other project files, moderate cross-model consultation, expose disagreements, and synthesize traceable results. Use when the user explicitly names Codeck or asks to consult, compare, or…

isdou/codeck · 108 tokens

scan

Scan your AI coding tool ecosystem — Gemini CLI, Claude Code, Antigravity (Desktop, CLI, IDE), Continue, Windsurf, JetBrains AI, OpenCode. Produces a maturity score, advisory recommendations, and optionally generates reusable SKILL.md files from your conversation patterns. Use when the user asks to audit their…

pauldatta/gemini-cli-scanner · 82 tokens

review-work

Post-implementation review orchestrator. Launches 5 parallel background sub-agents: Oracle (goal/constraint verification), Oracle (code quality), Oracle (security), unspecified-high (hands-on QA execution), unspecified-high (context mining from GitHub/git/Slack/Notion). All must pass for review to pass. MUST USE after…

daeryundf2-prog/LAZYANTIGRAVITY · 116 tokens

archify

Create polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and…

daeryundf2-prog/LAZYANTIGRAVITY · 0 tokens

image-prompt

A Korean-language skill that turns a rough image idea into a detailed prompt for gpt-image-2, OpenAI’s image-generation model.

daeryundf2-prog/LAZYANTIGRAVITY · 651 tokens