Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/raja21068/AutoResearchnpx agentmods add skills/raja21068/autoresearch/figure-specWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/raja21068/autoresearch/figure-spec)<a href="https://agentmods.dev/skills/raja21068/autoresearch/figure-spec"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/figure-spec/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/raja21068/autoresearch/figure-spec"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/figure-spec.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00080 | $0.02283 |
| Opus 5 | $0.00040 | $0.01141 |
| Sonnet 5 | $0.00016 | $0.00457 |
| Haiku 4.5 | $0.00008 | $0.00228 |
Grade A, and why
figure-spec scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to figure-spec — 65 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
tools/figure_renderer.py (from ARIS root). Invoke via:
python3 tools/figure_renderer.py render <spec.json> --output <out.svg>
python3 tools/figure_renderer.py validate <spec.json>
python3 tools/figure_renderer.py 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?
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 222 lines · 80 tokens per session scan A a0202cb157bd
figure-spec is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 80 tokens to every session and 2,283 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to figure-spec, differing in 65 lines, and is treated as a copy.
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