figure-spec

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• SKILL.md

SKILL.md (first lines)

# FigureSpec: Deterministic JSON → SVG Figure Generation

> **Before each reviewer slot**, follow [Selectable Reviewers](../shared-references/reviewer-backends.md):
> resolve invocation → `.aris/reviewer.json` → Codex default (fresh native reviewer)
> with `reviewer_route.py` through the canonical helper resolver, preserving this
> slot's tier, fresh contexts, source/image inputs and deterministic gates.
> Codex examples/pins below apply only to `backend=codex`; other routes use their
> own first-call/reply contract. Save the route and actual returned identity;
> unavailable selections are `REVIEW_UNAVAILABLE`. Classify acceptance from actual
> families/completion/scope. Forward invocation reviewer/model/effort overrides
> to reviewer-bearing children. Generation workers are unchanged.


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):

```bash
# 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)**:
```json
{
  "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)**:
```json
{

From wanshuiyin/Auto-claude-code-research-in-sleep, MIT.

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