Über
Diese Fähigkeit korrigiert programmgesteuert automatisch generierte Mermaid/Graphviz-Diagramme, die unleserlich klein geworden sind, durch Anwendung von Layout-Optimierungen wie Label-Umbrüchen und Cluster-Aufteilungen. Sie identifiziert und verwendet effektive Techniken, während sie auf Basis empirischer Tests ineffektive vermeidet. Verwenden Sie sie, wenn ein generiertes Diagramm in einer unlesbaren Skalierung dargestellt wird, jedoch nicht für manuell erstellte Diagramme.
Schnellinstallation
Claude Code
Empfohlennpx skills add pjt222/agent-almanac -a claude-code/plugin add https://github.com/pjt222/agent-almanacgit clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/restore-diagram-legibilityKopieren Sie diesen Befehl und fügen Sie ihn in Claude Code ein, um diese Fähigkeit zu installieren
Dokumentation
Restore Diagram Legibility
Make an auto-generated graph diagram readable again after it has outgrown its canvas. The content is usually fine — forced to 1:1 the labels are crisp — so this is a delivery failure, not an authoring one, and the fix is reducing the diagram's layout units, not styling its text.
All numbers in this skill are one measured instance (a 39-diagram corpus, 2026-07), shipped as evidence that a lever moves the needle — never as targets.
When to Use
- A generated Mermaid/Graphviz/DOT diagram renders at single-digit percent scale in its display column, putting nominal 12px labels below ~1px
- Fullscreen or zoom helps by 2x against a 10x+ deficit
- The same diagram is legible when force-rendered at 1:1 (content is fine)
- NOT for hand-authoring a small diagram, and NOT for aesthetic styling —
see
create-2d-compositionfor composition work
Inputs
- Required: The diagram source (Mermaid, Graphviz/DOT, or another Sugiyama/Dagre-family layout input)
- Required: A way to render and measure output width in layout units or pixels (CLI renderer, browser devtools, or the generated SVG's viewBox)
- Optional: The display context's column width, to compute effective label size (nominal font px x render scale)
Procedure
Step 1: Measure the Deficit Before Touching Anything
Compute the render scale and effective label size; confirm the situation matches this skill.
# SVG output: viewBox width = layout units. Effective label px =
# nominal font px * (display column px / viewBox width).
grep -o 'viewBox="[^"]*"' diagram.svg
# e.g. viewBox="0 0 2685 900" in a 640px column:
# scale = 640/2685 = 23.8%; a 12px label renders at 2.9px
One measured instance: a system map rendered at 6.9% scale — nominal 12px labels at 0.83px effective. Fullscreen was a 2x improvement against a ~15x deficit.
Expected: A number pair (scale %, effective label px) and confirmation that a 1:1 render is crisp. If 1:1 is also illegible, the problem is authoring, not delivery — stop here, this skill does not apply.
On failure: If the renderer emits raster only, measure image width vs display width instead; the ratio is the same scale factor.
Step 2: Skip the Two Levers That Measurably Do Not Work
Do not spend time on these; both were killed by measurement.
- Raising the render font is a no-op. Sugiyama/Dagre-family layout engines measure node sizes in text units, so the canvas grows with the font and the ratio barely moves. Measured: 5.2px -> 8.3px effective for a much larger file — against a 10x+ deficit. This is a property of the layout family, not of any one tool version.
- Predicting layout direction from topology fails. A star-shape heuristic (one hub holding most edges -> flip direction) helped the two diagrams still too wide but regressed 13 of 38 others, dropping the median effective label from 25.9px to 16.3px. Direction can only be measured (Step 6), not predicted.
Expected: Neither lever is attempted; effort goes to Steps 3-6.
On failure: If a font increase was already applied, revert it before measuring the real levers — it inflates layout units and masks their effect.
Step 3: Wrap Long Labels at the Source
Node width tracks the longest unbroken line in the label, and total canvas
width follows. Break labels into short lines in the diagram source (Mermaid:
<br/> inside the label text; Graphviz: \n in the label attribute).
One measured instance: 2685 -> 901 layout units from wrapping alone.
Expected: Canvas width in layout units drops substantially on re-render; labels occupy 2-3 short lines instead of one long one.
On failure: If wrapping changes nothing, the width driver is elsewhere — check for one very wide row of disconnected nodes (Step 4) or a single oversized cluster (Step 5).
Step 4: Chain Disconnected Components with Invisible Links
Layout engines place nodes with no edges in one wide row, spreading the
canvas. Add invisible links (Mermaid: ~~~; Graphviz: style=invis edges)
between disconnected components so the engine stacks them instead.
One measured instance: 3292 -> 881 layout units. The worst single case was a cluster 3292 units wide holding 6 nodes and zero edges.
Expected: Disconnected nodes stack into rows/columns; canvas width drops.
On failure: If the renderer treats the invisible-link syntax as a real edge (arrows appear), check the tool's spelling for an invisible or unstyled link; the construct is general even where the syntax differs.
Step 5: Split One Canvas into Per-Cluster Diagrams
A whole system on one canvas divides one column width across every parallel cluster. Emit one diagram per cluster (or per subsystem) instead; the index page links them.
One measured instance: median effective label size went 0.8px -> 25.9px. Do NOT pick split candidates by node count — in the measured corpus the illegible clusters had a median of 6 nodes, identical to the legible ones; width, not membership, is the axis.
Expected: Each split diagram is independently legible at its display size; nothing shares a canvas with an unrelated cluster.
On failure: If clusters are interlinked so splitting breaks edges, duplicate boundary nodes into both diagrams with a visual marker rather than keeping one merged canvas.
Step 6: Pick Layout Direction by Rendering Both and Measuring
Direction (top-down vs left-right) is shape-dependent: measure, do not predict (Step 2.2). Render the diagram in both directions, compare canvas width in layout units, keep the narrower.
One measured instance: 2682 -> 1311 units from a direction flip — on that diagram; the same flip regresses others, which is why the comparison is per-diagram.
Expected: Both renders exist; the kept direction is the measured winner for this diagram, not a global default.
On failure: If both directions are still too wide, return to Steps 3-5; direction is the last lever, not the first.
Step 7: Re-Measure and Record the Numbers
Recompute scale % and effective label px per diagram. Record before/after in the commit or PR that ships the change, as one measured instance.
Across the measured 39-diagram corpus: median effective label 13.7px -> 25.9px; 30/39 -> 37/39 diagrams at 10px or better.
Expected: The before/after pair is recorded next to the change; any diagram still below the display's legibility floor is listed explicitly, not averaged away.
On failure: If a diagram resists all four levers, say so in the record — an explicit residual beats a silently truncated claim of success.
Validation
- Deficit measured before any change (scale %, effective label px, 1:1 crispness confirmed)
- Neither rejected lever (render font size, topology-predicted direction) was applied
- Canvas width in layout units recorded before/after each applied lever
- Direction chosen by rendering both and comparing, per diagram
- Before/after numbers recorded as one measured instance, not stated as targets
- Any still-illegible residual diagrams listed explicitly
Common Pitfalls
- Raising the render font: Layout engines measure in text units, so the canvas grows with the font — measured 5.2px -> 8.3px against a 10x+ deficit, at a much larger file size. Wrap the text instead; the lever is the text, not the font
- Predicting direction from topology: A hub-shape heuristic regressed 13 of 38 diagrams (median 25.9px -> 16.3px). Render both directions and measure; direction is per-diagram
- Splitting by node count: The wrong axis — illegible and legible clusters had the same median node count (6); one 6-node zero-edge cluster was 3292 units wide. Split by measured width
- Shipping the numbers as targets: Every figure here is one measured instance. Copying "901 units" or "25.9px" into acceptance criteria turns evidence into cargo cult; measure your own corpus
- A rejected approach without its measurement: "We tried X, it did not help" is not a negative result — it earns its place only with the measurement that killed it, so the next reader can check whether their case differs
Related Skills
- generate-workflow-diagram - produces the generated diagrams this skill makes legible
- create-2d-composition - authoring-side composition and layout, when the content itself is the problem
- render-publication-graphic - DPI/color-profile/print delivery once the diagram is legible on screen
- stale-proof-rendered-numbers - keeping recorded measurements from rotting once shipped
GitHub Repository
Häufig gestellte Fragen
Was ist der Skill restore-diagram-legibility?
restore-diagram-legibility ist ein Claude Skill von pjt222. Skills bündeln Anweisungen und Ressourcen, die Claude bei Bedarf lädt, um Aufgaben rund um restore-diagram-legibility ohne zusätzliche Eingaben auszuführen.
Wie installiere ich restore-diagram-legibility?
Verwende die Installationsbefehle auf dieser Seite: Füge restore-diagram-legibility als Plugin zu Claude Code hinzu oder klone das Repository in dein Skills-Verzeichnis. Starte Claude danach neu, damit der Skill geladen wird.
Zu welcher Kategorie gehört restore-diagram-legibility?
restore-diagram-legibility gehört zur Kategorie Meta.
Kann ich restore-diagram-legibility kostenlos nutzen?
Ja. restore-diagram-legibility ist auf AIMCP gelistet und kann kostenlos installiert werden.
Verwandte Skills
Diese Skill bietet eine produktionsgetestete Einrichtung für Content Collections – ein TypeScript-first-Tool, das Markdown/MDX-Dateien in typsichere Datensammlungen mit Zod-Validierung umwandelt. Verwenden Sie ihn beim Erstellen von Blogs, Dokumentationsseiten oder inhaltsstarken Vite + React-Anwendungen, um Typsicherheit und automatische Inhaltsvalidierung zu gewährleisten. Er behandelt alles von der Vite-Plugin-Konfiguration und MDX-Kompilierung bis hin zur Deployment-Optimierung und Schema-Validierung.
Diese Fähigkeit ermöglicht es Entwicklern, Anwendungen mit der Polymarket-Prognosemärkte-Plattform zu erstellen, einschließlich API-Integration für Handel und Marktdaten. Sie bietet außerdem Echtzeit-Datenstreaming über WebSocket, um Live-Trades und Marktaktivitäten zu überwachen. Nutzen Sie sie zur Implementierung von Handelsstrategien oder zur Erstellung von Tools, die Live-Marktaktualisierungen verarbeiten.
Diese Fähigkeit unterstützt Entwickler dabei, OpenCode-Plugins zu erstellen, die in über 25 Ereignistypen wie Befehle, Dateien und LSP-Operationen eingreifen. Sie bietet die Plugin-Struktur, Event-API-Spezifikationen und Implementierungsmuster für JavaScript/TypeScript-Module. Nutzen Sie sie, wenn Sie den Lebenszyklus des OpenCode KI-Assistenten mit benutzerdefinierter ereignisgesteuerter Logik abfangen, überwachen oder erweitern müssen.
SGLang ist ein hochperformantes LLM-Serving-Framework, das sich auf schnelle, strukturierte Generierung für JSON, Regex und agentenbasierte Workflows unter Verwendung seines RadixAttention-Prefix-Cachings spezialisiert. Es bietet deutlich schnellere Inferenz, insbesondere für Aufgaben mit wiederholten Präfixen, was es ideal für komplexe, strukturierte Ausgaben und Mehrfachdialoge macht. Wählen Sie SGLang gegenüber Alternativen wie vLLM, wenn Sie constrained decoding benötigen oder Anwendungen mit umfangreicher Präfix-Weitergabe entwickeln.
