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graphvizconvert-image
Convert a Graphviz DOT specification to match a reference figure by iterative rendering
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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Convert a Graphviz DOT specification to match a reference figure by iterative rendering
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
Refactor unit test files by aligning strings, renaming methods, and factoring out common test code
Identify and refactor duplicated code blocks into shared functions across Python files
Find documentation files for a given dir, file, class, or function and summarize in 3 bullet points
Replace "from X import Y" style imports with "import X" and update usages throughout a file
Fix function call sites to pass positional args by position and assign constants to intermediate variables
Identify functions not called externally and rename them with a leading underscore to make them private
SOC 職業分類に基づく
| name | graphviz.convert_image |
| description | Convert a Graphviz DOT specification to match a reference figure by iterative rendering |
digraph legend {
graph [rankdir=TB, nodesep=0.5, ranksep=0.7];
node [shape=point, width=0, height=0, margin=0];
edge [dir=forward];
// Row 1: Direct causation
{
rank=same;
a1 [label="", style=invis];
b1 [label="", style=invis];
t1 [shape=plaintext, style=solid, label=" Direct causation", fontsize=16, fontname="Arial"];
a1 -> b1 [style=solid, color=black, penwidth=3, arrowsize=1.2, minlen=3];
b1 -> t1 [style=invis, minlen=1];
}
// Row 2: Uncertain/hypothesized causation
{
rank=same;
a2 [label="", style=invis];
b2 [label="", style=invis];
t2 [shape=plaintext, style=solid, label=" Uncertain / hypothesized causation", fontsize=16, fontname="Arial"];
a2 -> b2 [style=dotted, color=black, penwidth=2, arrowsize=1.0, minlen=3];
b2 -> t2 [style=invis, minlen=1];
}
// Row 3: Correlation/association
{
rank=same;
a3 [label="", style=invis];
b3 [label="", style=invis];
t3 [shape=plaintext, style=solid, label=" Correlation / association (non-causal)", fontsize=16, fontname="Arial"];
a3 -> b3 [style=dotted, color=gray50, penwidth=2, arrowhead=none, minlen=3];
b3 -> t3 [style=invis, minlen=1];
}
// Row 4: Positive effect
{
rank=same;
a4 [label="", style=invis];
b4 [label="", style=invis];
t4 [shape=plaintext, style=solid, label=" Positive effect (+ / ++ / +++)", fontsize=16, fontname="Arial"];
a4 -> b4 [style=solid, color="#228B22", penwidth=3, arrowsize=1.2, minlen=3];
b4 -> t4 [style=invis, minlen=1];
}
// Row 5: Negative effect
{
rank=same;
a5 [label="", style=invis];
b5 [label="", style=invis];
t5 [shape=plaintext, style=solid, label=" Negative effect (- / -- / ---)", fontsize=16, fontname="Arial"];
a5 -> b5 [style=solid, color="#DC143C", penwidth=3, arrowsize=1.2, minlen=3];
b5 -> t5 [style=invis, minlen=1];
}
// Row 6: Strength encoding - using <-> instead of Unicode
{
rank=same;
a6 [label="", style=invis];
b6 [label="", style=invis];
t6 [shape=plaintext, style=solid, label=" Strength encoding (weak <-> strong)", fontsize=16, fontname="Arial"];
a6 -> b6 [style=solid, color="#228B22", penwidth=3, arrowsize=1.2, minlen=3];
b6 -> t6 [style=invis, minlen=1];
}
// Force vertical ordering and left alignment
a1 -> a2 -> a3 -> a4 -> a5 -> a6 [style=invis];
b1 -> b2 -> b3 -> b4 -> b5 -> b6 [style=invis];
}
./helpers_root/dev_scripts_helpers/documentation/dockerized_graphviz.py
to generate a png