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 finsilabs/awesome-ecommerce-skills --skill package-fit-optimizationgit clone --depth 1 https://github.com/finsilabs/awesome-ecommerce-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/finsilabs/awesome-ecommerce-skills/package-fit-optimization)<a href="https://agentmods.dev/skills/finsilabs/awesome-ecommerce-skills/package-fit-optimization"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/package-fit-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/finsilabs/awesome-ecommerce-skills/package-fit-optimization"><img src="https://agentmods.dev/badge/skills/finsilabs/awesome-ecommerce-skills/package-fit-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00025 | $0.01900 |
| Opus 5.5 | $0.00010 | $0.00760 |
| Sonnet 5.5 | $0.00005 | $0.00380 |
| Haiku 4.5 | $0.00003 | $0.00190 |
Grade A, and why
package-fit-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 4d 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Package Fit Optimization
Overview
Package fit optimization helps merchants choose the smallest safe carton, mailer, or poly bag for each order before rate shopping or label creation. A good implementation reduces dimensional-weight surprises, packing waste, and warehouse exceptions without promising that every order can be optimally packed in real time. Use platform data first, then fall back to conservative defaults and human review when dimensions or fragility rules are incomplete.
When to Use This Skill
- When adding carton or mailer recommendations to a shipping workflow
- When dimensional weight is increasing shipping cost for bulky but light SKUs
- When a warehouse needs packer-facing package suggestions before label purchase
- When comparing boxes, rigid mailers, padded mailers, and poly bags for ecommerce orders
- When building batch analysis for catalog cleanup or packaging-cost reduction
Core Instructions
Step 1: Normalize product and package dimensions
Collect dimensions, weight, and handling constraints in one internal unit system before scoring packages.
| Input | Required? | Notes |
|---|---|---|
| Product length, width, height | Yes for fit scoring | Use inches or centimeters consistently; sort dimensions when orientation is flexible |
| Product weight | Yes for rate impact | Round up according to the carrier's weight rules before quoting |
| Fragility / crush risk | Recommended | Exclude mailers or require padding for fragile items |
| Nesting / foldability | Recommended | Apparel, soft goods, and poly bags need different logic from rigid boxes |
| Available package library | Yes | Include internal dimensions, max weight, packaging material cost, and allowed formats |
For Shopify, use product or variant metafields for dimensions when the theme or shipping app does not already store them. For WooCommerce, use the product shipping fields and custom attributes for fragility or packability. For custom stacks, store normalized dimensions on the SKU and version the package library so recommendations are auditable.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 177 lines · 25 tokens per session scan A a95ab0ab9abb
package-fit-optimization is a skill published in the GitHub repository finsilabs/awesome-ecommerce-skills (60 stars, last pushed 5d ago), licensed MIT. It adds 25 tokens to every session and 1,900 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-24.
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