算法艺术创作
algorithmic-art
通过代码和可调参数创作生成艺术与交互式视觉效果。
mqc-litigation-visual-redraw【设计与创意】Redraw a litigation diagram into a restrained, court-ready presentation graphic (SVG + PNG) WITHOUT changing any text or legal meaning. Use this whenever the user supplies a case timeline, a legal process flowchart, OR a party/relationship diagram and wants it cleaned up, beautified, redrawn, made professional, de-cluttered, recolored, or turned into…
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This skill is mqc-litigation-visual-redraw — the first open-source module of
新诉讼可视化 · New Litigation Visualization (slogan: 把法律画出来 · Make the Law
Visible), 缪奇川's litigation-visualization project. It takes an ugly / hand-drawn /
"AI-flavored" source — or even plain judgment text — and redraws it into a
standard legal diagram (timeline · flowchart · relationship). This module stands alone.
Turn a messy or generic litigation diagram into a calm, professional legal graphic. The guiding idea (先吃透,再重画): first understand the source faithfully, then redraw it — never change the wording and never change the legal meaning; only improve the visual expression. Method, in the spirit of mqc-legal-skills: scenario is vertical, the SOP is tight, the output should look like a McKinsey exhibit, not a student's slide.
Scope: timelines (three forms — numbered, dated, gantt), flowcharts, and relationship diagrams (free-form network + hierarchical tree; a two-column comparison table is the A-vs-B variant of the relationship family). All are frozen and share one visual language. This skill draws these three families and nothing else — do not invent new diagram types.
Read SKILL.md + references/STANDARDS.md always. Then, by intent, open only what
you need (don't preload everything):
| The user gives / wants | Read this | Then |
|---|---|---|
| An ugly / hand-drawn / screenshot / AI-style diagram, OR plain text / a judgment to turn into a diagram | references/extraction-guide.md (read→analyze→decompose, six steps) | pick a layout below |
| A timeline (events over time) | extraction-guide Step 1 timeline decision ladder | numbered / dated / gantt |
| A flowchart / process / decision | references/flowchart-spec.md | graphviz_flow |
| A relationship / parties / hierarchy | references/relationship-spec.md (tree-vs-network rule) | graphviz_relation / relation_tree |
| A vs B side-by-side comparison | schema comparison_table | two columns |
| Field/shape details for the JSON | references/semantic-map-schema.md | write JSON |
| Never | Do instead |
|---|---|
| Hand-write SVG coordinates / lay out nodes "by eye" | emit JSON; the scripts compute all geometry |
| Blue / slate / any second accent colour | neutral gray + the one deep red #991B1B (≤2 uses) |
| A diamond decision node | rounded hexagon (angled ends, r≈2.5) |
| Put an argument / 本院认为 reasoning / a whole paragraph inside a node | only facts & operative conclusions; reasoning is not a node |
| Change a frozen number (colour, radius, font, spacing) | change it in the owning spec / style-tokens.json first |
| Reorder events for looks, merge/drop items, or invent a date | verbatim, time-ordered; unknowns → provenance.uncertainties |
| Add a new diagram type / legend / icon / theme | stay within the three families above |
Do not hand-write SVG coordinates, and do not try to lay out nodes "by eye". Language models place boxes/arrows badly (overlaps, overflow, crossings), and this skill must work even on weaker models. So the division of labor is fixed:
semantic-map.json. Judgement calls (which element is the single
most important, reading order, above/below placement) live in that JSON.scripts/): all geometry — column math, date scaling,
text wrapping, collision-free stacking, styling, and rasterization.If you follow this split, output quality comes from the JSON being correct, not from the model being clever about pixels.
Read, analyze, decompose the source — this is the make-or-break stage of
this skill (turning an ugly/hand-drawn/cluttered source into a high-grade legal
diagram lives or dies here). Follow the six-step discipline in
references/extraction-guide.md: (1) classify the diagram type → pick the
layout; (2) find the spine first (the axis / the happy path / the core
party), then hang branches off it — never transcribe left-to-right blindly;
(3) transcribe every character verbatim — dates, labels, evidence numbers;
do not normalize ("2023年5月左右" stays as is), paraphrase, or merge; (4) strip
decoration (pie charts, waveforms, icons, flourishes) — this skill re-draws
STRUCTURE, it does not copy illustrations; (5) do NOT choose emphasis yourself —
the deep-red accent is the USER's call, asked at the checkpoint, and defaults to
NONE (no red anywhere) if they skip it; (6) when the source is dense, main structure goes in the diagram and
sub-notes go to provenance — do not cram. Anything you cannot read confidently
goes into provenance.uncertainties, never into a guess.
Write semantic-map.json. Follow references/semantic-map-schema.md.
Preserve original numbering if present; you may add numbering for readability
and must record that in provenance.
CHECKPOINT — one round of questions before rendering (not optional).
Do not compose the questions yourself. Generate them:
python3 scripts/checkpoint.py map.json --suggest=<n> # n = the mark you propose
Show that output to the user verbatim and wait. It asks the same three things every time:
Then record the answers in the map and render:
"checkpoint": {
"confirmed": true, // the user confirmed the structure
"emphasis_source": "user" // "user" | "model" | "none"
}
"user" — they named the element. Up to two marks."model" — they skipped, said "you pick", or did not reply. Mark the
ONE element you judge the case turns on. render.py keeps exactly one and
clears any others, and you must say in the delivery which one you marked
and why, so the user can move or remove it. It is your legal reading, not
theirs — never let it pass silently as if it were."none" — they asked for no red. The figure is pure greyscale.provenance.emphasis_note authorises nothing: it is
written by the same model whose choice it describes.Defaults when the user does not choose: 奇川风, and emphasis_source: "model"
— a figure with no focal point at all is not the safer answer, it is just a
weaker one. Until confirmed is true, every file is written as *-draft.*.
Render deterministically, in the mode chosen at the checkpoint. From scripts/.
Every run writes the master .svg, a .png, and three editable hand-offs —
.drawio, .pptx (PowerPoint / WPS) and .vsdx (ProcessOn / Visio / WPS /
Edraw). All five are transcribed from the same master, so none can drift from
the delivered figure. This is deliberate: which tool the lawyer edits in is not
ours to guess. Narrow it only if asked, with --formats=svg,png.
On an unfamiliar machine (a fresh clone), run python3 scripts/doctor.py first —
it reports missing tooling (graphviz, rasteriser, fonts) instead of failing obscurely:
python render.py <semantic-map.json> final # 奇川风 (recommended, colour)
python render.py <semantic-map.json> final --guizang # 歸藏风 (online / lecture)
python render.py <semantic-map.json> final --baimiao # 白描 (pure black and white)
(Or set "visual_mode":"歸藏风" / "白描" in the JSON instead of a flag.) This
picks the layout, writes final.svg (primary, editable) and final.png
(preview/filing), and prints an audit summary. All three modes share ONE geometry
— only the surface differs. Never edit coordinates by hand; if something is wrong,
fix the JSON or the script, not the SVG. Mode standards: references/visual-style.md.
Deliver. Hand over final.svg + final.png + a one-line audit summary
(elements preserved, mode used, emphasis used or "none", any
uncertainties). Keep the summary in the reply / JSON — never draw it onto the image.
Set "layout" in the JSON. Three timeline forms, chosen by what the spacing
should mean:
numbered_point_timeline — discrete events whose spacing carries NO
argument (a dense fact chronology, or events with no usable dates:
签约 → 转账 → 违约 → 起诉 → 判决). Axis is equidistant; markers are numbered
circles (1-2-3), cards alternating above/below. → render_points.py
dated_point_timeline — discrete events on a date-proportional axis, so
the distance between two events is faithful to the elapsed time. The axis is a
light-gray bar carrying an honest ruler (year ticks, or year+month for a short
span, auto-chosen); markers are dots (no numbering); the precise date sits in
each card. Best for long, well-separated chronologies (诉讼时效, 长期履行).
Every event needs a real date or it errors — use the numbered form for
undated/clustered events. → render_dated.py
proportional_gantt — periods that run, overlap, or leave gaps
(诉讼时效 / 保证期间 / 主债权 / 履行期间). Axis is date-proportional — bar
length and overlap ARE the legal point (e.g. whether 本诉 falls outside 诉讼时效).
One period per row. → render_spans.py
Rule of thumb: real time distances matter → dated_point_timeline (points) or
proportional_gantt (periods); only the ORDER matters → numbered_point_timeline.
Decide the timeline form with the ordered decision ladder in
references/extraction-guide.md (Step 1): if any event lacks a precise, parseable
date — or events are tightly clustered — use the equidistant numbered_point_timeline;
reserve dated_point_timeline for precise dates whose gaps carry legal meaning.
numbered_point_timeline is the safe default. A gantt may also carry point events
(转让公告, 提起本诉) as dashed vertical markers — put those in points (see schema).
For a process / procedure diagram (not dates but steps, decisions, branches, merges), use the flowchart layout:
graphviz_flow — nodes + directed edges. graphviz (dot) computes node
positions ONLY; the renderer routes the connectors itself (orthogonal, rounded
corners, sibling branches share a level "bus"), because graphviz's own ortho
edge routes are unreliable. Node shapes encode function: rounded rect = step,
rounded hexagon = decision (angled ends, corners r≈2.5, same height as a
same-content step box — a hexagon holds multi-line Chinese far better than a
diamond, which is a poor container for CJK text), pill = start/end terminal.
→ render_flow.py. Requires dot (graphviz) on PATH. See
references/flowchart-spec.md.For a party / relationship diagram (who the parties are and how they relate — 债权人/债务人/保证人, 股权, 资金流, 控制关系), use:
graphviz_relation — nodes are parties/entities; edges are labeled,
directed relationships; each node may carry a note below it. graphviz (engine
chosen by topology: dot for rows/chains, neato/fdp for networks, twopi/
circo for radial) positions nodes; the renderer draws cards, labeled lines,
notes, and top/bottom skip-routes itself. Layout is free-form — do NOT force a
fixed template (three-column, radial, etc.); let the source's real structure
decide. → render_relation.py. See references/relationship-spec.md.For a hierarchical party/entity structure — a top-down 主体关系图: 实际控制人 → 控股公司 → 子公司, 集团/股权/控制层级, org-chart-shaped — use:
relation_tree — a tidy hierarchy tree. The renderer positions nodes itself
(no graphviz): leaves take equal horizontal slots and every parent sits at the
MIDPOINT of its children, so every fork is symmetric with equal branch
distances. Boxes are one uniform height, and one uniform width per level, so
the levels read as tidy columns. Connectors are bracket lines with the same tiny
r≈2.5 rounded corners; structural edges have no arrowheads (a hierarchy line,
not a directed relationship) unless "arrows": true. Node shading is depth-coded
(dark root → mid → light leaves; aesthetic only, red still the one meaning); each
edge may carry a short label (持股比例 …) and each node an optional note.
→ render_tree.py. Use this when the source is a hierarchy; use graphviz_relation
when it is a free-form network of labeled relationships. See
references/relationship-spec.md.Full details in references/visual-style.md. The non-negotiables:
#991B1B = the single most important element, and nothing else.
It is a highlight, not decoration. An emphasized element is a solid deep-red
block with white text — no border tricks, no left accent bars.Full details in references/fidelity-rules.md. Text is verbatim. Never reorder
events for looks. Do not invent emphasis the source doesn't support without
flagging it as a suggestion for the user to confirm. Gray vs. white fill is a
free aesthetic choice (it does NOT encode parties like 甲方/施工方) — only deep
red carries meaning.
SVG is the deliverable; PNG is derived. render.py auto-detects an SVG
rasterizer and falls back to soffice (LibreOffice) → PDF → pdftoppm when no
dedicated one is installed — which is the common minimal setup. CJK fonts
(e.g. Noto Sans CJK SC) must be present or the PNG shows blank boxes; verify
with fc-list | grep -i "CJK SC". See references/rendering-and-workflow.md.
references/STANDARDS.md — the consolidated, authoritative standard (single
source of truth; on any conflict, this file wins). Read it first.references/extraction-guide.md — how to read, analyze & decompose an ugly /
hand-drawn / cluttered source into a correct map (the six-step discipline). This
is the make-or-break input stage; read it before your first extraction.references/semantic-map-schema.md — JSON schema + fields for all layouts.references/visual-style.md — every frozen visual rule with values.references/fidelity-rules.md — verbatim text, ordering, numbering, emphasis.references/flowchart-spec.md — flowchart shapes, connectors, forks, tidy-up.references/relationship-spec.md — relationship nodes, labeled edges, notes,
free-form layout, skip-routes, and the hierarchical tree standard.references/rendering-and-workflow.md — render pipeline, env probe, audit,
the human checkpoint, output naming.AUTHOR.md — author card and method.examples/ — worked semantic-map.json inputs (numbered/dated/gantt timelines,
flowchart, relationship network, relationship tree).把法律画出来 · Make the Law Visible | 新诉讼可视化 New Litigation Visualization | 缪奇川 出品 | v1.0.2