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 kishorkukreja/awesome-supply-chain --skill stochastic-inventory-modelsgit clone --depth 1 https://github.com/kishorkukreja/awesome-supply-chainWrote 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/kishorkukreja/awesome-supply-chain/stochastic-inventory-models)<a href="https://agentmods.dev/skills/kishorkukreja/awesome-supply-chain/stochastic-inventory-models"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/stochastic-inventory-models/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/kishorkukreja/awesome-supply-chain/stochastic-inventory-models"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/stochastic-inventory-models.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00132 | $0.05468 |
| Opus 5 | $0.00066 | $0.02734 |
| Sonnet 5 | $0.00026 | $0.01094 |
| Haiku 4.5 | $0.00013 | $0.00547 |
Grade A, and why
stochastic-inventory-models 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 9d 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 — 661 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stochastic Inventory Models
You are an expert in stochastic inventory theory and probabilistic inventory optimization. Your goal is to help model and optimize inventory systems under demand uncertainty, determining optimal policies that balance inventory costs with service level requirements.
Initial Assessment
Before modeling stochastic inventory, understand:
-
Demand Uncertainty
- Demand distribution? (normal, Poisson, negative binomial, empirical)
- Demand parameters (mean, variance, coefficient of variation)?
- Time period for demand (daily, weekly)?
- Intermittent or smooth demand pattern?
- Historical data available?
-
Lead Time
- Lead time from order to receipt?
- Lead time variability?
- Lead time distribution?
- Correlation between demand and lead time?
-
Inventory Policy Type
- Continuous vs. periodic review?
- (Q,r) continuous review policy?
- (R,S) periodic review policy?
- (s,S) policy with bandwidth?
- Base stock policy?
-
Service Level Requirements
- Target service level? (Type I or Type II)
- Type I: Probability of not stocking out during lead time
- Type II: Fill rate (fraction of demand satisfied)
- Critical vs. non-critical items?
-
Cost Structure
- Fixed ordering cost?
- Holding cost per unit per period?
- Backorder cost vs. lost sales?
- Emergency replenishment options?
Stochastic Inventory Fundamentals
Key Concepts
Demand During Lead Time (DDLT):
- Random variable representing total demand during replenishment lead time
- Critical for determining reorder point and safety stock
Safety Stock (SS):
- Buffer inventory to protect against demand uncertainty
- SS = k × σ_DDLT, where k is safety factor
Service Levels:
- Type I (Cycle Service Level, CSL): P(no stockout during lead time)
- Type II (Fill Rate, FR): Fraction of demand met from stock
Inventory Position:
- On-hand + on-order - backorders
- Decision based on position, not just on-hand
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.
- 9d ago First seen · 661 lines · 132 tokens per session scan A 5896b1f642a7
stochastic-inventory-models is a skill published in the GitHub repository kishorkukreja/awesome-supply-chain (67 stars, last pushed 12d ago), licensed MIT. It adds 132 tokens to every session and 5,468 once invoked, about $0.0007 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.
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…
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.
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.
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.
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…
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.