inventory-optimizer

inventory-optimizer is a skill for Claude Code, Codex from cosmicstack-labs/mercury-agent-skills. It costs 42 tokens per session (1,532 once invoked), scanned A, original, MIT.

An inventory-management assistant that monitors stock usage, supplier delivery times, minimum stock levels, and changing demand. It helps decide when and what to reorder.

In plain words
What is it for?
Use it to set stock levels, track fast- and slow-moving items, generate supplier orders, plan for seasonal demand, and rotate items by expiry date.
Why use it?
It reduces shortages that lose sales and excess stock that ties up money or spoils. It also accounts for suppliers whose delivery times vary.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument.

Good fit Use it to set stock levels, track fast- and slow-moving items, generate supplier orders, plan for seasonal demand, and rotate items by expiry date.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/cosmicstack-labs/mercury-agent-skills/inventory-optimizer
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 cosmicstack-labs/mercury-agent-skills --skill inventory-optimizer
Clone the repo
git clone --depth 1 https://github.com/cosmicstack-labs/mercury-agent-skills

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-optimizer

README.md
[![agentmods](https://agentmods.dev/badge/skills/cosmicstack-labs/mercury-agent-skills/inventory-optimizer/github.svg)](https://agentmods.dev/skills/cosmicstack-labs/mercury-agent-skills/inventory-optimizer)
Your own site
<a href="https://agentmods.dev/skills/cosmicstack-labs/mercury-agent-skills/inventory-optimizer"><img src="https://agentmods.dev/badge/skills/cosmicstack-labs/mercury-agent-skills/inventory-optimizer/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-optimizer

Your own site · 80×15
<a href="https://agentmods.dev/skills/cosmicstack-labs/mercury-agent-skills/inventory-optimizer"><img src="https://agentmods.dev/badge/skills/cosmicstack-labs/mercury-agent-skills/inventory-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 42 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,532 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 pass 7 Sept 2026
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 $0.00042 $0.01532
Opus 5 $0.00021 $0.00766
Sonnet 5 $0.00008 $0.00306
Haiku 4.5 $0.00004 $0.00153

Measured 8d ago against content hash 76e92773d5e8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

inventory-optimizer 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 8d 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.

categories/shop-restaurant/inventory-optimizer/SKILL.md · 153 lines

How it starts

The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Inventory Optimizer

Core Principles

Inventory is cash sitting on shelves. Too much ties up capital and spoils. Too little loses sales and frustrates customers. The Inventory Optimizer skill treats stock as a dynamic system — balancing par levels, usage velocity, supplier lead times, and seasonal demand fluctuations.

The Five Pillars of Smart Inventory

  1. Know your par levels. Every item has a minimum quantity you should never dip below. Set pars based on historic usage and reorder lead time.
  2. Track usage velocity. Fast-movers need frequent reordering. Slow-movers need scrutiny. Group items by turnover rate (A = high, B = medium, C = low).
  3. Account for lead time variance. A supplier who delivers in 2 days on paper often takes 5. Build buffer based on actual delivery history.
  4. Seasonal forecasting. Demand shifts with holidays, weather, and local events. Adjust pars proactively, not reactively.
  5. First-expiry-first-out (FEFO). Rotate stock religiously. The oldest inventory moves first. Nothing wrecks margins like spoilage.

Skill Workflow

Step 1 — Audit Current Inventory

Gather the current state by asking the user or pulling from their POS/ERP system. Collect:

  • Current stock quantities for each item
  • Unit of measurement (kg, cases, bottles, units)
  • Storage location (dry, cold, frozen)
  • Expiry dates (for perishables)
  • Last 30/60/90 days of usage data
  • Current supplier and price per unit

Prompt the user for:

"Let's start with your inventory snapshot. Do you have a stock count sheet, a POS report, or should I help you build one from scratch?"

Step 2 — Calculate Par Levels & Reorder Points

For each item, calculate:

Reorder Point = (Average Daily Usage × Lead Time in Days) + Safety Stock
Safety Stock = Average Daily Usage × Lead Time Variance Buffer
Par Level = Reorder Point + (Average Daily Usage × Order Cycle Days)

Example calculation for a busy café:

  • Espresso beans: 3kg/day usage, 4-day lead time, 2-day variance buffer
  • Reorder Point = (3 × 4) + (3 × 2) = 18kg
  • Order cycle: weekly (7 days)
  • Par Level = 18 + (3 × 7) = 39kg
  • If current stock = 12kg → trigger order for 27kg (Par − Current)

Read the full file on GitHub · 153 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. 8d ago First seen · 153 lines · 42 tokens per session scan A 76e92773d5e8

Subscribe to this mod's changes

inventory-optimizer is a skill published in the GitHub repository cosmicstack-labs/mercury-agent-skills (471 stars, last pushed 17d ago), licensed MIT. It adds 42 tokens to every session and 1,532 once invoked, about $0.0002 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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