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 HoangNguyen0403/agent-skills-standard --skill system-design-artifact-intakegit clone --depth 1 https://github.com/HoangNguyen0403/agent-skills-standardWrote 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/hoangnguyen0403/agent-skills-standard/system-design-artifact-intake)<a href="https://agentmods.dev/skills/hoangnguyen0403/agent-skills-standard/system-design-artifact-intake"><img src="https://agentmods.dev/badge/skills/hoangnguyen0403/agent-skills-standard/system-design-artifact-intake/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/hoangnguyen0403/agent-skills-standard/system-design-artifact-intake"><img src="https://agentmods.dev/badge/skills/hoangnguyen0403/agent-skills-standard/system-design-artifact-intake.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 | $0.00074 | $0.01043 |
| Opus 5 | $0.00037 | $0.00522 |
| Sonnet 5 | $0.00015 | $0.00209 |
| Haiku 4.5 | $0.00007 | $0.00104 |
Grade A, and why
system-design-artifact-intake 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 13d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design Artifact Intake
Priority: P1 (HIGH)
The artifact is not the design; the extracted fact sheet is. Never review what you have not provably read.
Classify the Artifact First
| Class | Members | Route |
|---|---|---|
| A - structured text | Mermaid, PlantUML/C4, Structurizr DSL, Excalidraw JSON, raw .drawio, Archify JSON, IaC, ASCII art | Parse directly |
| B - embedded structure | .drawio.png / .drawio.svg, pptx/docx with glued connectors, Confluence drawio-macro attachments, Lucid/Miro/Figma exports or API, Whimsical-to-Mermaid | Extract the source, then treat as Class A |
| C - vision only | Plain images, whiteboard photos, rendered PDF pages | Vision protocol below |
| D - mixed prose + artifacts | PDF docs, Confluence/Notion pages, Word/Markdown docs | Split streams, classify each embed, cross-check prose against topology |
Probe Before Vision
- A "screenshot" is often a
.drawio.png: check PNG text chunks for anmxfilekey before reading pixels..drawio.svgcarries the model in the rootcontentattribute; pptx connectors live instCxn/endCxn; Confluence drawio macros store the XML as a page attachment. - One probe replaces an entire lossy vision pass. Recipes per format: artifact formats.
- A share link is not an artifact. Ask for an export or API access; never scrape a link.
The Design Fact Sheet
Extract every artifact into the same shape before any judgment:
- Nodes: id, label, inferred type - never a guessed type without marking it inferred.
- Edges: source, target, direction, label, and a confidence mark per edge.
- Boundaries: kind (trust, deployment, ownership) and member nodes.
- Prose claims: each with its source location, kept separate from drawn topology.
UNRECOVERABLE: what the artifact cannot tell you (numbers, SLOs, consistency, intent).
Vision Protocol (Class C)
- Enumerate every node with label and position first. No edge before the node list is complete.
- Resolve each edge against that node list: source, target, direction, label. Arrowheads and crossing lines are the least reliable pixels - mark ambiguity per edge, never per diagram.
- Boundaries third: dashed frames, tints, swimlanes become containment lists.
- An unlabeled arrow stays an unlabeled edge. Never infer a protocol from proximity.
- Request the source file when fidelity matters, and say why: the extraction is lossy and the review inherits every loss.
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.
- 13d ago First seen · 81 lines · 74 tokens per session scan A ca664f644f2b
system-design-artifact-intake is a skill published in the GitHub repository HoangNguyen0403/agent-skills-standard (565 stars, last pushed 3d ago), licensed MIT. It adds 74 tokens to every session and 1,043 once invoked, about $0.0004 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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