FigureSpec: Deterministic JSON → SVG Figure Generation

Generate publication-quality architecture diagrams, workflow pipelines, audit cascades, and system topology figures as editable SVG vector graphics using a deterministic JSON → SVG renderer.

When to Use This Skill

Use figure-spec for:

System architecture diagrams (layered, hub-and-spoke, multi-plane)
Workflow / pipeline figures
Audit cascade / flow-control diagrams
Any structured diagram where node positions, connections, and groupings are semantically important
Figures that need to be edited/tweaked later (SVG is plain text)
Figures where determinism matters (same spec → same SVG)

Do NOT use for:

Data plots (bar/line/scatter) — use /paper-figure
Natural/qualitative illustrations — use /paper-illustration
Quick state-machine / flowchart — use /mermaid-diagram (lighter syntax)

Core Properties

Deterministic: identical FigureSpec JSON always produces identical SVG output (for a fixed renderer version + fonts)
Editable: SVG output is plain-text, can be post-edited by hand or programmatically
Validated: renderer enforces schema, rejects malformed specs with clear error messages
Shape-aware: edge clipping works correctly for rect/rounded/circle/ellipse/diamond
CJK support: multi-line labels with proper Chinese character width estimation
No external API: runs fully local, no network, no API keys

Tool Location

Phase 3.1 (Arch C) move: the canonical implementation now lives at skills/figure-spec/scripts/figure_renderer.py. tools/figure_renderer.py is kept as a backwards-compatible os.execv shim so legacy layers continue to resolve. Codex-side install layouts that previously copied the canonical into ~/.codex/skills/figure-spec/figure_renderer.py must now place it at ~/.codex/skills/figure-spec/scripts/figure_renderer.py to match the new layout (re-run install_aris_codex.sh to pick up the new symlink target).

Resolve $FIGURE_RENDERER via the Codex-side hybrid chain (layer 0 preferred for self-contained owner SKILL; layers 1-4 are legacy shared-runtime compatibility):

# Layer 0: self-contained at the new canonical location (Phase 3.1).
FIGURE_RENDERER=""
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills-codex.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null) || true
fi
[ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/skills/figure-spec/scripts/figure_renderer.py" ] && FIGURE_RENDERER="$ARIS_REPO/skills/figure-spec/scripts/figure_renderer.py"

# Layers 1-3: legacy shared-runtime chain via shim at tools/figure_renderer.py.
[ -z "$FIGURE_RENDERER" ] && [ -n "${ARIS_REPO:-}" ] && [ -f "$ARIS_REPO/tools/figure_renderer.py" ] && FIGURE_RENDERER="$ARIS_REPO/tools/figure_renderer.py"
[ -z "$FIGURE_RENDERER" ] && [ -f tools/figure_renderer.py ] && FIGURE_RENDERER="tools/figure_renderer.py"

# Layer 4: Codex-side skill-local install (`install_aris_codex.sh` may place it here).
[ -z "$FIGURE_RENDERER" ] && [ -f ~/.codex/skills/figure-spec/scripts/figure_renderer.py ] && FIGURE_RENDERER="$HOME/.codex/skills/figure-spec/scripts/figure_renderer.py"
[ -z "$FIGURE_RENDERER" ] && [ -f ~/.codex/skills/figure-spec/figure_renderer.py ] && FIGURE_RENDERER="$HOME/.codex/skills/figure-spec/figure_renderer.py"  # pre-Phase-3.1 layout

[ -n "$FIGURE_RENDERER" ] || {
  echo "ERROR: figure_renderer.py not found at any of: \$ARIS_REPO/skills/figure-spec/scripts/, \$ARIS_REPO/tools/, tools/, ~/.codex/skills/figure-spec/scripts/, ~/.codex/skills/figure-spec/. Set ARIS_REPO, rerun install_aris_codex.sh, or copy the canonical \$ARIS_REPO/skills/figure-spec/scripts/figure_renderer.py next to this skill." >&2
  exit 1
}

python3 "$FIGURE_RENDERER" render <spec.json> --output <out.svg>
python3 "$FIGURE_RENDERER" validate <spec.json>
python3 "$FIGURE_RENDERER" schema

Workflow

Step 1: Understand the Diagram Goal

From $ARGUMENTS (description or path to PAPER_PLAN.md / NARRATIVE_REPORT.md), identify:

Purpose: architecture, workflow, pipeline, audit cascade, topology?
Main entities: what are the boxes?
Relationships: how do they connect? (uses, produces, calls, verifies, chains)
Grouping: do entities cluster into named regions?
Hierarchy vs network: stacked layers, left-to-right flow, or central hub?

Step 2: Draft the FigureSpec JSON

Canvas sizing guide:

Single-column figure: ~500×350 px
Two-column (full-width): ~900×500 px
Tall topology: ~700×700 px

Start from a template based on the diagram type:

Architecture (stacked rows):

{
  "canvas": {"width": 900, "height": 520},
  "nodes": [
    {"id": "layer1_label", "label": "Layer 1", "x": 450, "y": 60, ...},
    {"id": "node_a", "label": "A", "x": 180, "y": 120, ...},
    {"id": "node_b", "label": "B", "x": 350, "y": 120, ...}
  ],
  "edges": [...],
  "groups": [
    {"label": "Layer 1", "node_ids": ["node_a", "node_b"], "fill": "#F0F9FF", "stroke": "#BAE6FD"}
  ]
}

Workflow (left-to-right chain):

{
  "canvas": {"width": 900, "height": 300},
  "nodes": [
    {"id": "step1", "label": "Step 1", "x": 100, "y": 150, "shape": "rounded"},
    {"id": "step2", "label": "Step 2", "x": 280, "y": 150, "shape": "rounded"}
  ],
  "edges": [
    {"from": "step1", "to": "step2", "label": "produces"}
  ]
}

Decision diamond:

{"id": "check", "label": "Passes?", "shape": "diamond", "x": 450, "y": 200}

Step 3: Render and Validate

# Validate first
python3 "$FIGURE_RENDERER" validate /tmp/spec.json

# Render to SVG
python3 "$FIGURE_RENDERER" render /tmp/spec.json --output figures/fig_arch.svg

# Convert to PDF for LaTeX inclusion
rsvg-convert -f pdf figures/fig_arch.svg -o figures/fig_arch.pdf

If validation fails, inspect the error (missing field, duplicate ID, overlap warning, invalid hex color) and fix the JSON.

Step 4: Visual Review

Open the SVG/PDF and check:

No overlaps: nodes don't collide with each other or group boundaries
Readability: font sizes are consistent, labels aren't clipped
Edge clarity: arrows hit nodes at clean angles, labels near edges are legible
Group alignment: background rectangles frame their members cleanly
Color distinction: categories are visually distinct in both color and grayscale

If issues found, edit the JSON spec (never the generated SVG) and re-render.

Step 5: Iterate with Codex Review (Optional, for High-Stakes Figures)

For paper architecture figures, invoke cross-model review:

spawn_agent:
  model: gpt-5.5
  reasoning_effort: xhigh
  message: |
    Review this SVG figure for a technical paper (architecture / workflow diagram).

    Spec file: /path/to/spec.json
    Rendered: /path/to/fig.svg

    Evaluate:
    1. Clarity (C): can a reader understand the system from this figure alone?
    2. Readability (R): font sizes, label placement, visual hierarchy
    3. Semantic accuracy (S): do relationships match the described system?

    Score each axis 1-10 and list specific issues to fix.

Iterate until all three axes ≥ 7/10. The ARIS tech report figures went through 5 rounds of this loop to reach C:7/R:7/S:8.

Schema Quick Reference

Run python3 "$FIGURE_RENDERER" schema for the authoritative schema.

Nodes

| Field | Required | Default | Notes | |-------|----------|---------|-------| | id | ✓ | — | Unique | | label | ✓ | — | \n for multi-line | | x, y | ✓ | — | Center coordinates | | width, height | | 120, 50 | | | shape | | rounded | rect / rounded / circle / ellipse / diamond | | fill, stroke | | auto from palette | #RRGGBB | | text_color | | #333333 | | | font_size | | 14 | Override style default |

Edges

| Field | Default | Notes | |-------|---------|-------| | from, to | required | Same = self-loop | | label | — | Short edge label | | `sty

figure-spec

Como adicionar

Cole no README do seu repo

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