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 cuesoftinc/oss-engineering-standards --skill cuelabs-system-designgit clone --depth 1 https://github.com/cuesoftinc/oss-engineering-standardsWrote 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/cuesoftinc/oss-engineering-standards/cuelabs-system-design)<a href="https://agentmods.dev/skills/cuesoftinc/oss-engineering-standards/cuelabs-system-design"><img src="https://agentmods.dev/badge/skills/cuesoftinc/oss-engineering-standards/cuelabs-system-design/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/cuesoftinc/oss-engineering-standards/cuelabs-system-design"><img src="https://agentmods.dev/badge/skills/cuesoftinc/oss-engineering-standards/cuelabs-system-design.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 | $0.00136 | $0.01606 |
| Opus 5.5 | $0.00054 | $0.00642 |
| Sonnet 5.5 | $0.00027 | $0.00321 |
| Haiku 4.5 | $0.00014 | $0.00161 |
Grade A, and why
cuelabs-system-design 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 10d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CueLABS System Design
Turn a product's requirements into a system design that follows the CueLABS language-by-role rule, then publish it twice: as a document in the repository, and as a diagram page people can open in any browser or copy into Excalidraw.
Read the relevant references
- Read references/method.md before deciding anything. It holds the rules, how to choose the services, how they communicate, who owns the data, the reusable patterns (upload tickets, claim-check, outbox, versioned results), and the review checklist.
- Read references/document-template.md
when writing
docs/system-design.md. - Read references/page-spec.md when writing the page spec, and start from assets/example-spec.json.
- The pipeline rule itself is owned by
$cuelabs-engineering-standards(repository-and-services.md); transports, serverless scope and fleet environment names are owned by$cuelabs-delivery-standard(cloud-and-ci.md). Consult them when installed; do not restate them.
Inputs
Accept any of these, alone or together:
- Product documents: a PRD, decisions register, architecture, data model, flow docs, deployment docs, API docs, a web mock API.
- A repository: read the documents first, then the code only to confirm what exists. If the user says to ignore the code, design from the documents alone.
- A description: a few paragraphs about the product.
When the input is thin, ask at most three questions that change the design (who uses it, what data is sensitive, what third parties it touches, what is urgent). Otherwise proceed and record assumptions as open questions.
Workflow
- Gather. Read everything relevant. List the product's users, core flows, data (with sensitivity), third parties, money movement, privacy promises, and every existing ratified decision. Existing product decisions stay unless the design must reverse one; a reversal is flagged for sign-off, never silent.
- Audit. Compare the current design (documents, draft diagrams, code
when in scope) against the rules in
method.md. Write findings with evidence and consequence. For a description-only input, write the starting facts and assumptions instead. - Drivers. Name the five to nine properties this product must have and the mechanism that delivers each.
- Services. Apply "Deciding the services" in
method.md. Decide the number of processing services with the split criteria; when the choice is close, present it to the user with a recommendation before writing the rest. Present the transport choice the same way when it is open (for example Kafka versus a managed pub/sub with push delivery). - Communication and data. Apply the communication table and the data ownership rules. Add the patterns the flows need: upload tickets for any client upload, claim-check for bytes, outbox for everything the owner publishes, versioned results for anything computed from the owner's data, raw storage for inbound webhooks.
- Flows. Pick two to four flows that exercise the design end to end and write them as sequences, including authorization, polling, failures and deletion.
- Remaining sections. API surface and contract changes, security,
reliability (SLOs, failure modes, who watches it), deployment (worker
pools for consumers), repository layout, doc map, decisions to ratify
(
S-1…), migration phases, open questions. - Write the document to
docs/system-design.md(or the path the user names) followingdocument-template.md. Do not commit unless asked. - Write the page spec to
docs/system-design.jsonnext to it. Include at least: the whole system (with legend), rules, services table, communication table, what changes from the current design (when there is one), the key flows as sequences, topics, decisions, open questions. - Check and render.
What ships with it
6 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.
- 10d ago First seen · 131 lines · 136 tokens per session scan A 9d64ca02be24
cuelabs-system-design is a skill published in the GitHub repository cuesoftinc/oss-engineering-standards (2 stars, last pushed 5d ago), licensed MIT. It adds 136 tokens to every session and 1,606 once invoked, about $0.0005 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 2026-09-27.
Other skills, from other repositories
design-taste-frontend
Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
image-to-code
Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards…
design-taste-frontend-v1
The original v1 taste-skill, preserved for projects depending on its exact behavior. The current default is design-taste-frontend (v2 experimental), which is a substantial rewrite. Use this v1 install name only if you need exact backward compatibility.
brandkit
Premium brand-kit image generation skill for creating high-end brand-guidelines boards, logo systems, identity decks, and visual-world presentations. Trained for minimalist, cinematic, editorial, dark-tech, luxury, cultural, security, gaming, developer-tool, and consumer-app brand systems. Optimized for intentional…
redesign-existing-projects
Upgrades existing websites and apps to premium quality. Audits current design, identifies generic AI patterns, and applies high-end design standards without breaking functionality. Works with any CSS framework or vanilla CSS.
high-end-visual-design
Teaches the AI to design like a high-end agency. Defines the exact fonts, spacing, shadows, card structures, and animations that make a website feel expensive. Blocks all the common defaults that make AI designs look cheap or generic.