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 cosmicstack-labs/mercury-agent-skills --skill inventory-optimizergit clone --depth 1 https://github.com/cosmicstack-labs/mercury-agent-skillsWrote 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/cosmicstack-labs/mercury-agent-skills/inventory-optimizer)<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.
<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>- 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 | $0.00042 | $0.01532 |
| Opus 5 | $0.00021 | $0.00766 |
| Sonnet 5 | $0.00008 | $0.00306 |
| Haiku 4.5 | $0.00004 | $0.00153 |
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.
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
- 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.
- Track usage velocity. Fast-movers need frequent reordering. Slow-movers need scrutiny. Group items by turnover rate (A = high, B = medium, C = low).
- Account for lead time variance. A supplier who delivers in 2 days on paper often takes 5. Build buffer based on actual delivery history.
- Seasonal forecasting. Demand shifts with holidays, weather, and local events. Adjust pars proactively, not reactively.
- 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)
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.
- 8d ago First seen · 153 lines · 42 tokens per session scan A 76e92773d5e8
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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