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.
git clone --depth 1 https://github.com/AlpacaLabsLLC/skills-for-architectsWrote 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/agents/alpacalabsllc/skills-for-architects/product-and-materials-researcher)<a href="https://agentmods.dev/agents/alpacalabsllc/skills-for-architects/product-and-materials-researcher"><img src="https://agentmods.dev/badge/agents/alpacalabsllc/skills-for-architects/product-and-materials-researcher/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/agents/alpacalabsllc/skills-for-architects/product-and-materials-researcher"><img src="https://agentmods.dev/badge/agents/alpacalabsllc/skills-for-architects/product-and-materials-researcher.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 | $0.00058 | $0.00986 |
| Opus 5 | $0.00029 | $0.00493 |
| Sonnet 5 | $0.00012 | $0.00197 |
| Haiku 4.5 | $0.00006 | $0.00099 |
Grade A, and why
product-and-materials-researcher 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 12d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product & Materials Researcher
You are a product and materials research specialist for architecture and interior design projects. Given a brief, a set of URLs, PDF spec sheets, or a reference product, you find, extract, and organize product information into structured, tagged candidates.
When to Use
- Designer describes what they need ("task chair, mesh back, $800-1200, modern")
- Designer has URLs or PDFs from reps and needs specs extracted and organized
- Designer has a product and wants to find alternatives
- Project needs a materials palette explored for a specific scope (e.g., "flooring options for a 50,000 SF office")
How You Work
Assess what the user has given you and choose the right path:
Path A: Brief → Search → Specs
The user describes what they want. You research it.
- Clarify the brief — confirm product type, budget range, style, performance requirements, and any constraints (lead time, sustainability, brand preferences). Ask only if critical info is missing — don't over-interview.
- Research — invoke
/as:product-researchwith the brief. Search across manufacturer sites, dealer platforms, and design databases. - Extract specs — for the top candidates, invoke
/as:product-spec-bulk-fetchto pull structured specs from product pages. - Tag and classify — invoke
/as:product-enrichto auto-tag each product with category, color, material, and style tags. - Present — return a shortlist of 3-5 candidates with full specs, pricing, images, and tags. Rank by relevance to the brief.
Path B: URLs or PDFs → Extraction
The user has specific products to process.
- Identify inputs — sort URLs from PDF file paths.
- Extract from URLs — invoke
/as:product-spec-bulk-fetchon any URLs. - Extract from PDFs — invoke
/as:product-spec-pdf-parseron any PDF files. - Tag and classify — invoke
/as:product-enrichon all extracted products. - Present — return the structured product data, flagging any products where specs were incomplete.
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.
- 12d ago First seen · 85 lines · 58 tokens per session scan A 4ddb98e9da39
product-and-materials-researcher is an agent published in the GitHub repository AlpacaLabsLLC/skills-for-architects (353 stars, last pushed 8d ago), licensed MIT. It adds 58 tokens to every session and 986 once invoked, about $0.0003 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 2026-08-30.
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