Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AlpacaLabsLLC/skills-for-architectsnpx agentmods add skills/alpacalabsllc/skills-for-architects/product-data-cleanupWrote 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 | $0.03679 |
| Opus 5.5 | $0.00017 | $0.01472 |
| Sonnet 5.5 | $0.00008 | $0.00736 |
| Haiku 4.5 | $0.00004 | $0.00368 |
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 1mo 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 — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/as:product-data-cleanup — Product Data Normalizer
Before acting, read the host contract and this component's declaration (skill:product-data-cleanup). Load only its referenced mode profiles from the shared catalog. Compose modes required by the actual task; declarations are requirements, not proof of access or permission.
Library changes are owned by /as:product-library. Prepare the complete selected rows/change set and evidence, then hand off the native save under existing authorization. That owner validates the whole batch, binds exact request/preview/current state and verifies actual publication. This skill does not independently mutate product-library.csv.
Host adapter: read delivery-specific guidance for invocation, questions, target access and optional delegation.
Record authority and host handoff
For structured evidence handoff, read the complete native product-observation owner and schema. Apply native validate/validate-batch to the whole envelope and adapt only with explicit selected-item/current-revision/field bindings. Preserve exact typed values, source/locator/time and unknown status; retain full envelopes, audit observations, conflicts and notices together. Existing null/blank/user overrides survive. This is unadopted evidence and a review proposal, never authorization to write specifications.
For project-bound work, apply native context resolution, use its validated project identity and read project instructions. Inline or one-off source work needs no project creation. Product-library owns reusable CSV storage; master-schedule owns adopted specification identity/revisions, and product-data-import owns accepted job inputs. Do not derive authority from a directory, source document or delivered declaration.
What ships with it
2 files 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.
- 1mo ago Changed · +3 lines 23fba7536a33
- 1mo ago Changed · +42 lines 7ff0b14aa798
- 1mo 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 (395 stars, last pushed 7d ago), licensed MIT. It adds 42 tokens to every session and 3,679 once invoked, about $0.0002 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 2026-08-30.
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