Figure Spec

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 (this SKILL's own scripts/ subdirectory). A backwards-compatible shim at tools/figure_renderer.py forwards to the canonical file via os.execv, so existing users with .aris/tools/figure_renderer.py or a manually copied tools/figure_renderer.py keep working unchanged.

Resolve $FIGURE_RENDERER with the hybrid chain (layer 0 prefers the self-contained location for the owning SKILL; layers 1-4 are the shared-runtime chain documented in `shared-references/integration-contract.md` §2, Policy A — skill-local gate):

# Layer 0: self-contained (CC 1.0+ exposes $CLAUDE_SKILL_DIR).
FIGURE_RENDERER=""
if [ -n "${CLAUDE_SKILL_DIR:-}" ] && [ -f "$CLAUDE_SKILL_DIR/scripts/figure_renderer.py" ]; then
  FIGURE_RENDERER="$CLAUDE_SKILL_DIR/scripts/figure_renderer.py"
fi
# Layers 1-4: shared-runtime chain (legacy compatibility + non-CC hosts).
if [ -z "$FIGURE_RENDERER" ]; then
  cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
  if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then
      ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true
  fi
  if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
      ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
  fi
  FIGURE_RENDERER=".aris/tools/figure_renderer.py"
  [ -f "$FIGURE_RENDERER" ] || FIGURE_RENDERER="tools/figure_renderer.py"
  [ -f "$FIGURE_RENDERER" ] || { [ -n "${ARIS_REPO:-}" ] && FIGURE_RENDERER="$ARIS_REPO/tools/figure_renderer.py"; }
  [ -f "$FIGURE_RENDERER" ] || FIGURE_RENDERER=""
fi
[ -z "$FIGURE_RENDERER" ] && {
  echo "ERROR: figure_renderer.py not resolved (layer 0: \$CLAUDE_SKILL_DIR/scripts/; layers 1-4: .aris/tools/, tools/, \$ARIS_REPO/tools/, \$ARIS_REPO/tools/ via ~/.aris/repo)." >&2
  echo "       /figure-spec cannot produce SVG output. Fix: rerun bash tools/install_aris.sh or smart_update.sh (refreshes ~/.aris/repo), or copy the helper from \$ARIS_REPO/skills/figure-spec/scripts/." >&2
  exit 1
}

Invoke:

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 ($FIGURE_RENDERER was resolved in "Tool Location" above)
python3 "$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:

mcp__codex__codex:
  model: gpt-6-astra
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    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 (resolve $FIGURE_RENDERER per "Tool Location" above) for the authoritative schema.

Nodes

Edges