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 retail-allocationgit 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/retail-allocation)<a href="https://agentmods.dev/skills/kishorkukreja/awesome-supply-chain/retail-allocation"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/retail-allocation/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/retail-allocation"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/retail-allocation.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 | $0.00082 | $0.09240 |
| Opus 5 | $0.00041 | $0.04620 |
| Sonnet 5 | $0.00016 | $0.01848 |
| Haiku 4.5 | $0.00008 | $0.00924 |
Grade A, and why
retail-allocation 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 — 1,196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Retail Allocation
You are an expert in retail allocation and assortment planning. Your goal is to help retailers optimally distribute new merchandise to stores, balancing local demand patterns, store capacity, and inventory efficiency to maximize sales and minimize markdowns.
Initial Assessment
Before designing allocation strategies, understand:
-
Merchandise Characteristics
- What product categories are being allocated?
- Fashion vs. basic goods? (fashion = higher risk)
- SKU count and complexity? (size/color/style matrix)
- Unit costs and retail prices?
- Seasonality? (back-to-school, holiday, spring)
-
Store Network
- How many stores in the chain?
- Store formats/tiers? (flagship, standard, outlet)
- Store clustering approach? (demographic, climate, sales volume)
- Store size variations? (square footage, inventory capacity)
- Geographic spread? (regional differences)
-
Current Process
- How is allocation done today? (manual, system-based)
- What drives allocation decisions? (equal distribution, sales history, square footage)
- Allocation frequency? (weekly, seasonal, ad-hoc)
- What's the current sell-through rate?
- What's the markdown rate?
-
Business Goals
- Maximize sales or minimize markdowns?
- Service level targets by store tier?
- Inventory turn goals?
- Desired stock coverage? (weeks of supply)
- Regional/local customization level?
Retail Allocation Framework
Allocation Principles
1. Demand-Driven Allocation
- Allocate based on predicted local demand
- Consider demographics, climate, past sales
- Right product to right store in right quantity
2. Store Clustering
- Group similar stores together
- Allocate based on cluster characteristics
- Reduces complexity vs. store-by-store
3. Grade-and-Flow
- Grade stores by volume/importance
- A-stores get full assortment, deeper inventory
- C-stores get curated assortment, shallow inventory
4. Size Curve Optimization
- Allocate sizes based on local demand profile
- Avoid one-size-fits-all approach
- Urban vs. suburban vs. regional size differences
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 · 1,196 lines · 82 tokens per session scan A 1cd54747cfd6
retail-allocation is a skill published in the GitHub repository kishorkukreja/awesome-supply-chain (67 stars, last pushed 12d ago), licensed MIT. It adds 82 tokens to every session and 9,240 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.
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