ARIS is a collection of Markdown-based skills that define a workflow for autonomous machine-learning research, including idea discovery, experiment automation, and review loops. Researchers and AI coding agents use it across tools such as Claude Code, Codex, Cursor, and OpenClaw without depending on a single framework. The catalogue entries are ARIS workflow skills and agents.
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/wanshuiyin/Auto-claude-code-research-in-sleepnpx agentmods add skills/wanshuiyin/auto-claude-code-research-in-sleep/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/wanshuiyin/auto-claude-code-research-in-sleep/figure-spec)<a href="https://agentmods.dev/skills/wanshuiyin/auto-claude-code-research-in-sleep/figure-spec"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/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/wanshuiyin/auto-claude-code-research-in-sleep/figure-spec"><img src="https://agentmods.dev/badge/skills/wanshuiyin/auto-claude-code-research-in-sleep/figure-spec.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.03266 |
| Opus 5.5 | $0.00032 | $0.01306 |
| Sonnet 5.5 | $0.00016 | $0.00653 |
| Haiku 4.5 | $0.00008 | $0.00327 |
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 10d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- figure-spec — 100% identical, 17 lines differ
- figure-spec — 89% identical, 78 lines differ
How it starts
The opening of the file, as written. The whole thing — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.
FigureSpec: Deterministic JSON → SVG Figure Generation
Before each reviewer slot, follow Selectable Reviewers: resolve invocation →
.aris/reviewer.json→ Claude Code default (Codex MCP) withreviewer_route.pythrough the canonical helper resolver, preserving this slot's tier, fresh contexts, source/image inputs and deterministic gates. Codex examples/pins below apply only tobackend=codex; other routes use their own first-call/reply contract. Save the route and actual returned identity; unavailable selections areREVIEW_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
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago Changed · +13 lines f5b4b8e806a0
- 1mo ago Changed 090a28a1b646
- 2mo ago First seen · 263 lines · 80 tokens per session scan A 93c1018de5e7
figure-spec is a skill published in the GitHub repository wanshuiyin/Auto-claude-code-research-in-sleep (18,302 stars, last pushed today), licensed MIT. It adds 80 tokens to every session and 3,266 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
ui_design
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drawio-reconstruction
Reconstructs reference images into high-fidelity, editable Draw.io files with rendered previews: native Draw.io elements carry text and structure, SVG covers simple icons that match the reference, and cropped or transparent PNGs preserve complex visuals. Use when the user wants a diagram image, research figure…
figure-designer
Advises on the design of the three core figures in a technical paper: the Motivated Example (Figure 1), the Solution Overview (Methodology), and the Experimental Results figures. Recommends the right design paradigm, layout, labelling, and tool for each figure type, then runs a quality-control audit. Use when the user…
do-not-ship-an-empty-surface
An empty screen, tab or category is a promise the product is not keeping. Hide it until it has content, or do not build it yet.
warn-before-the-thing-you-are-warning-about
A caution placed after the action it concerns arrives after the reader has already decided. Put it above, and make it undismissable when it matters.
visual-architect
Transform research papers into professional visual schemas. Analyzes paper logic, selects optimal layout patterns, and generates detailed prompts for AI image generation.