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 AlpacaLabsLLC/skills-for-architects --skill product-data-cleanupgit 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/skills/alpacalabsllc/skills-for-architects/product-data-cleanup)<a href="https://agentmods.dev/skills/alpacalabsllc/skills-for-architects/product-data-cleanup"><img src="https://agentmods.dev/badge/skills/alpacalabsllc/skills-for-architects/product-data-cleanup/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/alpacalabsllc/skills-for-architects/product-data-cleanup"><img src="https://agentmods.dev/badge/skills/alpacalabsllc/skills-for-architects/product-data-cleanup.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 | $0.00042 | $0.02212 |
| Opus 5 | $0.00021 | $0.01106 |
| Sonnet 5 | $0.00008 | $0.00442 |
| Haiku 4.5 | $0.00004 | $0.00221 |
Grade A, and why
product-data-cleanup scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/as:product-data-cleanup — Product Data Normalizer
Harness note: use
/as:<skill>on Claude Code and$<skill>on Codex. Resolve<skill-root>as the directory containing this loadedSKILL.mdand<plugin-root>as the plugin root that containsskills/, and use equivalent native tools when host tool names differ.
Takes a messy FF&E schedule and normalizes everything: casing, dimensions, units, language, materials vocabulary, currency formatting, and duplicates. Outputs a clean, consistent, spec-ready schedule.
Persistent cleanup operates on the nearest project's product-library.csv. Pasted tables may be previewed in Markdown but are not another persistent format.
Input
The user provides a schedule in one of these ways:
- Project library — the nearest
product-library.csvunder an ancestor containingPROJECT.md. - CSV file path — import only after its exact 33-column header is validated.
- Pasted table — preview a proposed canonical mapping before any persistence.
If the input format is unclear, ask.
Cleanup Rules
1. Casing
| Field | Rule | Example |
|---|---|---|
| Product Name | Title Case | eames lounge chair → Eames Lounge Chair |
| Brand | Title Case, preserve known abbreviations | HERMAN MILLER → Herman Miller, HAY → HAY |
| Collection | Title Case | cosm → Cosm |
| Category | Title Case, singular | chairs → Chair, TABLES → Table |
| Materials | Sentence case, lowercase after first word | MOLDED PLYWOOD, FULL GRAIN LEATHER → Molded plywood, full grain leather |
| Colors/Finishes | Title Case per item | walnut/black leather → Walnut / Black Leather |
Known brand abbreviations to preserve: HAY, USM, B&B, DWR, CB2, HBF, OFS, SitOnIt, 3form, ICF
2. Category Normalization
Map free-text categories to the canonical vocabulary and alias table defined in ../../schema/product-schema.md. Read that file for the full mapping of variations (English, Spanish, legacy terms) to canonical category names.
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.
- 12d ago First seen · 194 lines · 42 tokens per session scan A e88ed48d1455
product-data-cleanup is a skill published in the GitHub repository AlpacaLabsLLC/skills-for-architects (353 stars, last pushed 8d ago), licensed MIT. It adds 42 tokens to every session and 2,212 once invoked, about $0.0002 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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