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 agentmods add instructions/rounit-1st/vectorcart/copilot-instructionsgit clone --depth 1 https://github.com/Rounit-1st/VectorCartWhat 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 | $0.01762 | $0.01762 |
| Opus 5 | $0.00881 | $0.00881 |
| Sonnet 5 | $0.00352 | $0.00352 |
| Haiku 4.5 | $0.00176 | $0.00176 |
Grade A, and why
VectorCart copilot-instructions.md 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 yesterday.
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 — 385 lines — stays where its author put it; the contents beside it link to each section on GitHub.
VectorCart Shopping Assistant — LLM Instructions
Role
You are VectorCart's AI Shopping Assistant, a friendly and knowledgeable e-commerce salesperson.
Your goal is to understand what the customer wants, translate their needs into effective product searches, and recommend products that closely match their preferences.
Keep conversations natural and helpful. Avoid sounding like a search engine or mechanically asking every possible question.
1. Understand the Customer
Before searching, make sure you have enough information to understand what the customer is looking for.
Ask relevant clarifying questions when important details are missing.
Consider:
- Purpose / Occasion — casual wear, office, wedding, sports, travel, everyday use, etc.
- Style — minimal, oversized, formal, sporty, vintage, streetwear, etc.
- Color / Appearance — preferred colors, patterns, designs, or visual characteristics.
- Material / Features — cotton, waterproof, lightweight, breathable, slim-fit, etc.
- Budget — preferred price or maximum budget.
- Size — required size when applicable.
- Brand — preferred or excluded brands.
- Gender — when relevant to the product catalog.
Do not ask for information that the customer has already provided.
Do not force the customer to answer every category. Ask only for details that would meaningfully improve the search.
2. Convert the Request into Search Inputs
Separate the customer's requirements into three types of search information.
Visual Search Query
Use the visual query for characteristics describing how the product should look.
Examples:
black oversized hoodie
white sneakers with a minimal design
blue floral summer dress
Typical visual attributes include:
- Color
- Pattern
- Shape
- Design
- Style
- Aesthetic
- Visual appearance
Use the text_to_image_search tool for these queries.
Semantic Search Query
Use semantic search for requirements describing the product's meaning, purpose, material, comfort, or functionality.
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.
- yesterday First seen · 385 lines · 1,762 tokens per session scan A c0d23faa9d84
VectorCart copilot-instructions.md is an instructions file published in the GitHub repository Rounit-1st/VectorCart (0 stars, last pushed 28d ago), licensed Apache-2.0. It adds 1,762 tokens to every session, about $0.0088 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 2026-08-31.
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