Borrowing it
Nothing to install: this file belongs to luiscielak/ai-product-design-rules. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/luiscielak/ai-product-design-rules/main/.cursor/skills/ai-product-design-workflow/SKILL.mdgit clone --depth 1 https://github.com/luiscielak/ai-product-design-rulesWrote 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/luiscielak/ai-product-design-rules/ai-product-design-workflow)<a href="https://agentmods.dev/skills/luiscielak/ai-product-design-rules/ai-product-design-workflow"><img src="https://agentmods.dev/badge/skills/luiscielak/ai-product-design-rules/ai-product-design-workflow/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/luiscielak/ai-product-design-rules/ai-product-design-workflow"><img src="https://agentmods.dev/badge/skills/luiscielak/ai-product-design-rules/ai-product-design-workflow.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 | $0.00098 | $0.01439 |
| Opus 5.5 | $0.00039 | $0.00576 |
| Sonnet 5.5 | $0.00020 | $0.00288 |
| Haiku 4.5 | $0.00010 | $0.00144 |
Grade A, and why
ai-product-design-workflow 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 5d 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Product Design Workflow (Orchestrator)
Runs the six ai-product-design-* skills as a single chained pipeline. Each
stage reads the previous stage's output and must clear that skill's acceptance
list before the next stage starts.
The individual skills already know how to do each stage. This skill's job is to
pick the mode, resolve the artifact_root, run the stages in order, and enforce
the handoffs. Read each sub-skill's SKILL.md before running its stage and
follow it exactly.
Pipeline
scaffold → /prd → /lofi → /midfi → /hifi → /ui-proto
(PM) (PO) (UX) (PD) (UI) (UI Eng)
Sub-skills:
ai-product-design-new-project— scaffoldai-product-design-prd—/prdai-product-design-lofi—/lofiai-product-design-midfi—/midfiai-product-design-hifi—/hifiai-product-design-ui-proto—/ui-proto
Order note: the design-ops pipeline lists /ui-proto before /hifi. When the
prototype should implement the finished visual kit, run design-then-build
(/hifi before /ui-proto) as above. Otherwise keep the sub-skills' default
order. State which order you're using at the start of the run.
Step 0 — Pick a mode
Decide before scaffolding:
- Greenfield product — a brand-new product with no existing app. Use the
full
ai-product-design-new-projectstructure:projects/{name}/withapp/,docs/(artifact_root), optionalmarketing/. - Feature of an existing product — designing a feature inside a codebase
that already exists. Skip the
app/andmarketing/scaffold. Put artifacts underdocs/features/{feature-slug}/and treat that as the artifact_root. Do not create a second app at the repo root.
If unsure which mode applies, ask the user one question before scaffolding.
Step 1 — Resolve the artifact_root
All stages write under one artifact_root. Resolve in this order:
- Parent directory of an attached/@-referenced
PRD.mdorDESIGN-BRIEF.md. - An explicit path the user gives for the product/feature docs folder.
- Feature mode:
docs/features/{feature-slug}/(kebab-case). - Greenfield only:
projects/{project_name}/docs/.
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.
- 5d ago First seen · 126 lines · 98 tokens per session scan A 1edab8056130
ai-product-design-workflow is a skill published in the GitHub repository luiscielak/ai-product-design-rules (4 stars, last pushed 5d ago), licensed MIT. It adds 98 tokens to every session and 1,439 once invoked, about $0.0004 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 2026-10-28.
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