inventory-demand-planning

inventory-demand-planning is a skill for Claude Code, Codex from ufy2024/AuC. It costs 86 tokens per session (5,491 once invoked), scanned A, original, MIT.

A retail planning guide for predicting product demand, setting extra stock levels, and deciding when and how much to reorder across multiple stores.

In plain words
What is it for?
Use it to create or review forecasts, set safety stock, plan seasonal or promotional replenishment, and check forecast accuracy.
Why use it?
It helps reduce empty shelves and excess inventory by connecting sales patterns, promotions, seasons, and supply constraints to purchasing decisions.

Skill for Claude CodeCodex

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

Good fit Use it to create or review forecasts, set safety stock, plan seasonal or promotional replenishment, and check forecast accuracy.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ufy2024/auc/inventory-demand-planning
View source ↗ ufy2024/AuC
About the project

AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.

ufy2024/AuC · 1,090 stars · on GitHub

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 ufy2024/AuC --skill inventory-demand-planning
Clone the repo
git clone --depth 1 https://github.com/ufy2024/AuC

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/ufy2024/auc/inventory-demand-planning/github.svg)](https://agentmods.dev/skills/ufy2024/auc/inventory-demand-planning)
Your own site
<a href="https://agentmods.dev/skills/ufy2024/auc/inventory-demand-planning"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/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/ufy2024/auc/inventory-demand-planning"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/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 5,491 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 3 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Anti-Refusal · line 195
    Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.
    Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
  • medium analysis-evasion · line 1
    Suspicious Unicode normalization or mixed-script content
    Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
  • medium Agent Snooping · line 26
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
How audits are shown
Origin original No closer match found 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 $0.05491
Opus 5 $0.00043 $0.02746
Sonnet 5 $0.00017 $0.01098
Haiku 4.5 $0.00009 $0.00549

Measured 5d ago against content hash a058c947158e, 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

Copies of this mod

6 near-identical copies found in the catalogue:

auc/skill_library/bundled/inventory-demand-planning/SKILL.md · 260 lines

How it starts

The opening of the file, as written. The whole thing — 260 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 · 260 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 · 260 lines · 86 tokens per session scan A a058c947158e

Subscribe to this mod's changes

inventory-demand-planning is a skill published in the GitHub repository ufy2024/AuC (1,090 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 5,491 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.

Related

Other skills, from other repositories

amazon-reviews-api-skill

This skill helps users automatically extract Amazon product reviews via the Amazon Reviews API. Agent should proactively apply this skill when users express needs like getting reviews for Amazon product with ASIN B07TS6R1SF, analyzing customer feedback for a specific Amazon item, getting ratings and comments for a…

browser-act/skills · 124 tokens

amazon-competitor-analyzer

Scrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.

browser-act/skills · 48 tokens

asc-subscription-localization

Bulk-localize subscription, subscription-group, and in-app purchase display names across App Store locales using asc, including API 4.4.1 version-scoped v2 resources. Use when filling or updating subscription/IAP names and descriptions without App Store Connect UI work.

rorkai/app-store-connect-cli-skills · 60 tokens

food-order

Reorder previous Foodora orders, preview cart contents, and track delivery ETA/status with ordercli. Use when the user wants to reorder food, check delivery status, or browse recent Foodora order history. Never confirm an order without explicit user approval.

Bitterbot-AI/bitterbot-desktop · 53 tokens

product-description-generator

E-commerce product description generator for any platform. Generates optimized titles, bullet points, descriptions, and backend keywords using competitor research + keyword scoring + FABE copywriting. Two modes: (A) Create — generate listing from product specs with optional competitor analysis, (B) Optimize — improve…

nexscope-ai/eCommerce-Skills · 126 tokens

amazon-price-tracker

Amazon price monitoring and competitive pricing intelligence. Real-time price tracking, Buy Box analysis, promotion detection, and dynamic pricing strategy optimization. Use when the user asks about price monitoring, competitor pricing, Buy Box tracking, or pricing strategy.

nexscope-ai/Amazon-Skills · 51 tokens