# Anycap Architecture Diagrams > Produce architecture and system diagrams with AnyCap — fact-graph-first prompting, label fidelity audit via image-read, hybrid plates with deterministic labels, and a public-safety pass that strips internal system details before generation. Use when turning a system design into a shareable architecture figure with AnyCap image models. - Skill: `convergeai-labs/anycap-architecture-diagrams` (Agent Skill) - Install (CLI): `npx skillmds@latest add convergeai-labs/anycap-architecture-diagrams` - Raw SKILL.md: https://api.skillmd.com/api/skills/convergeai-labs/anycap-architecture-diagrams/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - Author: convergeai-labs (https://skillmd.com/u/convergeai-labs) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/convergeai-labs/anycap-architecture-diagrams --- # AnyCap Architecture Diagrams Turn a system design into a clean architecture figure with AnyCap. Distilled from repeated real use; the loop below is the one that converges. ## 0. Sanitize the fact graph FIRST (public-safety pass) An architecture diagram is a浓缩的系统地图 — it leaks exactly what scans can't see: internal system names, topology, data flows. Before anything is generated: - Write the fact graph (nodes, edges, labels) in plain text. - Replace every internal name with its **public role** (`内部订单库` → `orders DB`, `knlb-xxx` → `API gateway`). If a node's only honest name is an internal one, the diagram is not publishable — generalize the whole example or pick a synthetic system. - Drop what the viewer question doesn't need. Each removed node is attack surface removed. - For "example/teaching" diagrams, prefer a **synthetic reference system** (a generic RAG app, a URL shortener) over any real deployment. ## 1. Freeze the contract - Viewer question + one takeaway (what should the reader understand in 10 seconds?) - Fact graph: node list, edge list with direction, **exact label whitelist** - Grammar: layered architecture / request flow / deployment / state machine — one per figure - Surface: doc inline (16:9 or 4:3), slide, or README hero - **Style — freeze it too.** Default to the editorial language: warm-white canvas (#FAFAF7), navy (#1E3A5F) structure, accent colors only where they carry meaning, bold title + one-line takeaway caption, text-only cards (no provider logos/icons). A dark "dashboard chrome" theme (saturated neon boxes on navy) reads as a themed Mermaid export — "looks like Mermaid" is a reject reason even when every label is correct. ## 2. Choose the production branch | Branch | When | Trade-off | |---|---|---| | **T1 full generated** (labels in image) | short labels (≤4 chars each), ≤10 nodes, Chinese OK | beautiful, but label fidelity must be audited | | **Hybrid** (no-text generated plate + deterministic text overlay) | long labels, many nodes, exact wording required | labels guaranteed; two-step work | | **Retreat to deterministic** (Mermaid/SVG/D2) | topology must be exact, or two audits already failed | always correct, less pretty | Never invent nodes, edges, arrows, or numbers beyond the frozen fact graph — and don't let the model do it either. ## 3. Generate with the fact graph in the prompt Structure the prompt as: scene grammar → node inventory with exact labels → edges with direction → style (flat vector, dark/light plate) → constraints ("every label must be exactly as listed, no extra text"). ``` anycap image generate --model --prompt "..." -o candidate.png ``` For Chinese-labeled T1 candidates, models with strong short-label rendering (e.g. nano-banana class) beat general image models. ## 4. Audit with image-read — labels, not vibes ``` anycap actions image-read --file candidate.png \ --instruction "List every text label in this diagram exactly as written, and every arrow with its direction. Then: (1) any label not in this whitelist: ; (2) any missing/duplicated label; (3) any arrow pointing the wrong way; (4) any pseudo-text. Answer AUDIT-PASS or AUDIT-FAIL -
." ``` Typical failure modes: misspelled labels, merged nodes, reversed arrows, hallucinated extra services, pseudo-text watermarks. **One concrete failure → regenerate with the failure named in the prompt. Two failed audits → switch branch (hybrid or deterministic).** Don't keep rolling the same branch. Measured ceiling (two shipped projects): T1 full generation with inline labels is reliable up to **~6 nodes / ~6 edges**. At 8 nodes × 8 arrows, every roll drifted exactly one arrow — a different one each time (fix-one-break-another carousel). Layout-first prompt fixes (place a node so its required arrow is short and vertical) are a valid second-round move, but they don't move the ceiling. Retreating to a deterministic SVG/Mermaid for the densest view is a branch decision, not a failure — the deterministic source doubles as the editable truth layer. ## 5. Hybrid finishing when labels must be perfect Generate a no-text plate (gate it CLEAN like any brand mark), then overlay labels deterministically (HTML/CSS, SVG, or an editor). The plate carries the mood; the overlay carries the truth. ## 6. Completion gate - Every label matches the whitelist exactly; every edge direction verified - No invented components; no internal names anywhere - Readable at the consumer's real size (inline width, not 100% zoom) - Provenance sidecar: fact graph, model, audit results, branch decisions ## Anti-patterns - "Draw my company's architecture" as a prompt — semantic leak by design - Accepting a diagram because it "looks professional" while two arrows point backwards - More than ~12 nodes in one generated figure — split into views - Shipping the raster without keeping the fact graph — the next edit restarts from zero - Shipping a single overview when the system has distinct questions — a small-multiples **view set** (overview / request flow / data pipeline / deployment), all projecting one frozen fact model, beats one overloaded poster. Views share node names, palette, typography, and connector style; change the model in one view and you must change all.