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/paper-illustrationWrote 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/paper-illustration)<a href="https://agentmods.dev/skills/raja21068/autoresearch/paper-illustration"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/paper-illustration/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/paper-illustration"><img src="https://agentmods.dev/badge/skills/raja21068/autoresearch/paper-illustration.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 | $0.00067 | $0.07523 |
| Opus 5 | $0.00034 | $0.03762 |
| Sonnet 5 | $0.00013 | $0.01505 |
| Haiku 4.5 | $0.00007 | $0.00752 |
Grade A, and why
paper-illustration scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
RESPONSE=$(curl -s --max-time 90 \ This is a copy
98% identical to paper-illustration — 28 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 — 721 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paper Illustration: Multi-Stage Claude-Supervised Figure Generation
Generate publication-quality illustrations using a multi-stage workflow with Claude as the STRICT supervisor/reviewer.
Core Design Philosophy
┌──────────────────────────────────────────────────────────────────────────┐
│ MULTI-STAGE ITERATIVE WORKFLOW │
├──────────────────────────────────────────────────────────────────────────┤
│ │
│ User Request │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Claude │ ◄─── Step 1: Parse request, create initial prompt │
│ │ (Planner) │ │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Gemini │ ◄─── Step 2: Optimize layout description │
│ │ (gemini-3-pro)│ - Refine component positioning │
│ │ Layout │ - Optimize spacing and grouping │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Gemini │ ◄─── Step 3: CVPR/NeurIPS style verification │
│ │ (gemini-3-pro)│ - Check color palette compliance │
│ │ Style │ - Verify arrow and font standards │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Paperbanana │ ◄─── Step 4: Render final image │
│ │ (gemini-3- │ - High-quality image generation │
│ │ pro-image) │ - Internal codename: Nano Banana Pro │
│ └──────┬──────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ │
│ │ Claude │ ◄─── Step 5: STRICT visual review + SCORE (1-10) │
│ │ (Reviewer) │ - Verify EVERY arrow direction │
│ │ STRICT! │ - Verify EVERY block content │
│ └──────┬──────┘ - Verify aesthetics & visual appeal │
│ │ │
│ ▼ │
│ Score ≥ 9? ──YES──► Accept & Output │
│ │ │
│ NO │
│ │ │
│ ▼ │
│ Generate SPECIFIC improvement feedback ──► Loop back to Step 2 │
│ │
└──────────────────────────────────────────────────────────────────────────┘
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.
- 7d ago First seen · 721 lines · 67 tokens per session scan A 66bfe6f2e1dc
paper-illustration is a skill published in the GitHub repository raja21068/AutoResearch (2 stars, last pushed 3mo ago), licensed MIT. It adds 67 tokens to every session and 7,523 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 98% identical to paper-illustration, differing in 28 lines, and is treated as a copy.
Other skills, from other repositories
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…
plotting-agent
Step 2 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the visualization plan from outline.json — render plots and conceptual diagrams from experimentallog.md and idea.md, optionally refine via VLM critique loop, and produce context-aware captions. Runs in parallel with the literature-review-agent. TRIGGER…
content-refinement-agent
Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate…
section-writing-agent
Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimentallog.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges…
outline-agent
Step 1 of the PaperOrchestra pipeline (arXiv:2604.05018). Convert (idea.md, experimentallog.md, template.tex, conferenceguidelines.md) into a strict JSON outline containing a plotting plan, literature search plan (Intro + Related Work), and section-level writing plan with citation hints. TRIGGER when the orchestrator…
paper-autoraters
Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the…