Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add skillmds/skillmd --skill research-paper-figure-skill-factorygit clone --depth 1 https://github.com/skillmds/skillmdWrote 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/skillmds/skillmd/research-paper-figure-skill-factory)<a href="https://agentmods.dev/skills/skillmds/skillmd/research-paper-figure-skill-factory"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/research-paper-figure-skill-factory/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/skillmds/skillmd/research-paper-figure-skill-factory"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/research-paper-figure-skill-factory.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 | $0.00169 | $0.05994 |
| Opus 5.5 | $0.00068 | $0.02398 |
| Sonnet 5.5 | $0.00034 | $0.01199 |
| Haiku 4.5 | $0.00017 | $0.00599 |
Grade A, and why
research-paper-figure-skill-factory 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 275 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Paper Figure Skill Factory
This skill is a two-layer research-paper figure Skill Factory.
- Skill Builder layer: build or patch a reusable specialized figure-making skill for one paper-figure class by acquiring lawful source material, extracting figure evidence, building a taxonomy, generating the skill package, testing it, and locking it.
- Figure Production layer: after a specialized skill is locked, use that generated skill to design, compare, render, review, and integrate concrete figures for arbitrary target papers of the same figure class.
Version 2.0.5 adds a stricter visual-structure-as-image gate for generated specialized skills. When a generated skill explains or defines visual structure, layout skeleton, panel choreography, module topology, arrow grammar, candidate-board structure, second-round optimization geometry, or final content architecture in a text turn, it must show that structure with an embedded saved reference image or non-target concept/modeling example image. It must not substitute a prose-only or bullet-only visual-structure description. The existing hard gates remain: abstract visual decisions require inline reference/concept images, and after P6 selects the strongest first-round direction, P6b/P6b-IMAGE/P6c must run a paper-local best-practice optimization round before P7 final prompt construction. Target-paper candidate images, draft figures, final figures, and revisions still remain isolated in dedicated IMAGE_ONLY turns.
Non-Negotiable Contract
First Trigger
On first trigger, output only a startup plan. Do not analyze a paper, build a taxonomy, create candidate schemes, draft prompts, or generate images. The first reply is STARTUP_PLAN_ONLY (TEXT_ONLY).
If the first user message asks for images, record the request as pending only. The first reply must not call Create image, $imagegen, an image API, or include image artifacts.
Specialized-Skill-First Builder Rule
The normal route is:
figure-class goal -> corpus plan -> lawful acquisition/local corpus -> evidence extraction -> taxonomy -> specialized skill blueprint -> generated specialized skill -> tests/patches -> locked skill -> target-paper production.
What ships with it
5 files 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.
- 1mo ago First seen · 275 lines · 169 tokens per session scan A a43744996b6d
research-paper-figure-skill-factory is a skill published in the GitHub repository skillmds/skillmd (143 stars, last pushed 4d ago), licensed MIT. It adds 169 tokens to every session and 5,994 once invoked, about $0.0007 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 2026-09-19.
research-paper-figure-skill-factory compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| research-paper-figure-skill-factoryskillmds/skillmd | Skill | 143 | 4d ago | MIT | A | 169 | Claude Code |
| super-researchXiaomiMiMo/MiMo-Code | Skill | 14k | 2d ago | MIT | A | 0 | Claude Code, Codex |
| clinical-reportssynthetic-sciences/openscience | Skill | 4.4k | yesterday | Apache-2.0 | A | 71 | Claude Code |
| imaging-data-commonssynthetic-sciences/openscience | Skill | 4.4k | yesterday | Apache-2.0 | A | 62 | Claude Code, Codex |
| clinical-decision-supportsynthetic-sciences/openscience | Skill | 4.4k | yesterday | Apache-2.0 | A | 97 | Claude Code |
| ggetsynthetic-sciences/openscience | Skill | 4.4k | yesterday | Apache-2.0 | A | 66 | Claude Code, Codex |
| immunology-assayssynthetic-sciences/openscience | Skill | 4.4k | yesterday | Apache-2.0 | A | 79 | Claude Code, Codex |