本文へ移動
cccskills
無料GitHub で公開

restore-diagram-legibility

Restore legibility to an auto-generated graph diagram that has outgrown its canvas - rendering at single-digit percent scale with sub-pixel labels. Covers the levers that measurably reduce layout units (source-level label wrapping, invisible chain links so unconnected nodes stop spreading into one row, per-cluster splitting, layout direction picked by rendering both and measuring) and the rejected ones (raising the render font is a no-op because layout engines measure in text units; a topology heuristic for direction regressed 13 of 38 diagrams). Use when a generated Mermaid/Graphviz/DOT diagram is too small to read; not for hand-authoring a small diagram.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md9.9 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

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-composition for 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.

  1. 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.
  2. 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, dropping the median effective label from 25.9px to 16.3px. Direction can only be measured (Step 6), not predicted.

Expected: The diagram source contains no font-size override added for legibility and no direction attribute chosen by a topology heuristic; if either is present from a prior attempt, it is reverted and the layout-unit change from the revert is recorded.

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. The legibility floor comes from Step 1's formula for your display context — not from any number in this skill.

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

<!-- Keep under 500 lines. Extract large examples to references/EXAMPLES.md if needed. -->

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Execute structural metamorphosis using strangler fig migration, chrysalis phases, and interface preservation. Covers transformation planning, parallel running, progressive cutover, rollback design, and post-metamorphosis stabilization for system architecture evolution. Use when assess-form has classified the system as READY for transformation, when migrating from monolith to microservices, when replacing a core subsystem while dependents continue operating, or when any architectural change must be gradual rather than big-bang.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月12日 更新

adaptic

無料

Master skill composing the 5-step synoptic cycle for panoramic synthesis across multiple domains. Orchestrates meditate, expand-awareness, observe, awareness, integrate-gestalt, and express-insight into a coherent process that produces unified understanding rather than sequential compromise. Use when a problem genuinely spans 3+ domains and the interactions between domains matter more than depth in any one, when sequential analysis feels like compromise rather than integration, or before major architectural decisions affecting multiple stakeholders.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月12日 更新

Scaffold a new puzzle type across all 10+ pipeline integration points in jigsawR. Creates the core puzzle module, wires it into the unified pipeline (generation, positioning, rendering, adjacency), adds ggpuzzle geom/stat layers, updates DESCRIPTION and config.yml, extends the Shiny app, and creates a comprehensive test suite. Use when adding a completely new puzzle type to the package or following the 10-point integration checklist to ensure nothing is missed end-to-end.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月12日 更新

Add Rcpp or RcppArmadillo integration to an R package for high-performance C++ code. Covers setup, writing C++ functions, RcppExports generation, testing compiled code, and debugging. Use when an R function is too slow and profiling confirms a bottleneck, when you need to interface with existing C/C++ libraries, or when implementing algorithms (loops, recursion, linear algebra) that benefit from compiled code.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月12日 更新

aikido

無料

Practice aikido as a defensive martial art emphasizing harmony, redirection, and controlled resolution. Covers centering and grounding, ukemi (safe falling and rolling), core techniques (irimi-nage, shiho-nage, kote-gaeshi, ikkyo), blending and tai sabaki (body movement), randori (multiple attacker practice), weapons awareness, and applying principles off the mat. Use when learning a defensive martial art that prioritizes de-escalation, developing the ability to redirect aggression without causing unnecessary harm, building safe falling skills, or cultivating calm centeredness under physical pressure.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月12日 更新

Analyze an arbitrary codebase to identify functions, APIs, and data sources suitable for exposure as MCP tools, producing a tool specification document. Use when planning an MCP server for an existing project, auditing a codebase before wrapping it as an AI-accessible tool surface, comparing what a codebase can do versus what is already exposed via MCP, or generating a tool spec to hand off to scaffold-mcp-server.

日本語の概要は準備中です。原文の説明を表示しています。

pjt222/agent-almanac372026年10月12日 更新

pjt222 のスキルをすべて見る

このスキルの問題を報告する