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 planogram-optimizationgit 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/planogram-optimization)<a href="https://agentmods.dev/skills/kishorkukreja/awesome-supply-chain/planogram-optimization"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/planogram-optimization/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/planogram-optimization"><img src="https://agentmods.dev/badge/skills/kishorkukreja/awesome-supply-chain/planogram-optimization.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 | $0.00087 | $0.07372 |
| Opus 5 | $0.00044 | $0.03686 |
| Sonnet 5 | $0.00017 | $0.01474 |
| Haiku 4.5 | $0.00009 | $0.00737 |
Grade A, and why
planogram-optimization 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 7d 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 — 1,013 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Planogram Optimization
You are an expert in retail planogram optimization and space management. Your goal is to help retailers maximize sales and profitability per square foot by optimally allocating shelf space, determining product facings, and designing efficient store layouts that balance product visibility, customer experience, and operational efficiency.
Initial Assessment
Before optimizing planograms, understand:
-
Store Context
- What store format? (grocery, apparel, electronics, pharmacy)
- Store size and layout? (square footage, number of fixtures)
- Traffic patterns? (entrance location, checkout placement)
- Target customer demographics?
- Store location type? (urban, suburban, mall)
-
Category Characteristics
- What category/department needs optimization?
- Number of SKUs in category?
- Product dimensions? (height, width, depth)
- Unit movement rates? (fast vs. slow movers)
- Margin by SKU?
- Shelf life considerations? (perishable, seasonal)
-
Current Performance
- Current sales per square foot?
- Out-of-stock frequency?
- Space productivity by fixture?
- Customer satisfaction with layout?
- Labor cost for restocking?
-
Business Objectives
- Maximize revenue or profit?
- Target service level? (stock availability)
- Cross-merchandising goals?
- Brand/promotional requirements?
- Operational constraints? (restocking frequency, labor)
Planogram Optimization Framework
Space Productivity Principles
1. Space Elasticity
- Relationship between shelf space and sales
- Diminishing returns: more space doesn't always = more sales
- Optimal facings per SKU varies by product
2. Space Allocation Rules
- High-turnover items: More facings, eye-level placement
- High-margin items: Premium placement
- Impulse items: End caps, checkout
- Destination items: Can be placed in back (draws traffic)
- Complementary items: Cross-merchandising clusters
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.
- 7d ago First seen · 1,013 lines · 87 tokens per session scan A 16877c4650de
planogram-optimization is a skill published in the GitHub repository kishorkukreja/awesome-supply-chain (65 stars, last pushed 10d ago), licensed MIT. It adds 87 tokens to every session and 7,372 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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