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 neuromechanist/research-skills --skill ai-full-figuregit clone --depth 1 https://github.com/neuromechanist/research-skillsWrote 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/neuromechanist/research-skills/ai-full-figure)<a href="https://agentmods.dev/skills/neuromechanist/research-skills/ai-full-figure"><img src="https://agentmods.dev/badge/skills/neuromechanist/research-skills/ai-full-figure/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/neuromechanist/research-skills/ai-full-figure"><img src="https://agentmods.dev/badge/skills/neuromechanist/research-skills/ai-full-figure.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00155 | $0.02295 |
| Opus 5.5 | $0.00062 | $0.00918 |
| Sonnet 5.5 | $0.00031 | $0.00459 |
| Haiku 4.5 | $0.00015 | $0.00230 |
Grade A, and why
ai-full-figure 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Full Figure
Generate single panels or whole multi-panel figures with the image model (gpt-image-2 through the Codex CLI, or the OpenAI Images API when a key exists),
with the titles, panel letters, and short labels rendered by the model at large size,
then run figures:figure-qa and iterate until the figure ships.
The output is an opaque PNG per panel plus a composed SVG, PDF, and PNG when more than one panel is built.
Sister skills are invoked by their fully qualified figures:<skill> names through the host's skill-invocation mechanism. Keep host-specific command or tool syntax in the host adapter, not in shared skill instructions.
The workflow
- Bible first. A project keeps one
figures/theme.json. If it does not exist, invokefigures:figure-biblebefore generating anything. The theme supplies the palette, typography, style and negative tokens, the text limits, and the Codex model settings. - Generate. One panel with
generate_figure.py, or a whole figure withbuild_figure.pyand a spec file. Text that the model should render is passed as verbatim items with a role and a placement. - QA. Invoke
figures:figure-qaon every candidate, passing the expected verbatim strings, the theme path, and the journal. The report ends with a JSON block whosestatusandfindings[].actiondrive the next step. - Iterate. Follow the iterate-loop reference shipped with
figures:figure-qa(iterate-loop.md): N candidates in parallel, QA in one parallel dispatch, pick the best, apply one targeted change, re-QA, stop atshipor after three iterations.
When to use this skill
Reach for ai-full-figure when the figure's picture is the point: a headset on a head, a microscope, an anatomical scene, a schematic scene for a poster or a talk, or a graphical-abstract panel.
Route elsewhere when the figure is information-dense:
| Figure type | Skill |
|---|---|
| Data plot with axes and numerals | figures:plot-styling, then figures:scientific-figure to compose |
| Boxes, arrows, and process flow with many labels | figures:svg-primitives |
| Flat icon to drop into another figure | figures:transparent-icons |
| Composing panels that already exist | figures:scientific-figure |
What ships with it
7 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 Changed · +23 lines · -11 tokens per session 267c537b9693
- 2mo ago First seen · 128 lines · 166 tokens per session scan A 32261a2c22c9
ai-full-figure is a skill published in the GitHub repository neuromechanist/research-skills (51 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 155 tokens to every session and 2,295 once invoked, about $0.0006 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.
ai-full-figure compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| ai-full-figureneuromechanist/research-skills | Skill | 51 | yesterday | BSD-3-Clause | A | 155 | Claude Code |
| godot-optimizationjame581/GodotPrompter | Skill | 933 | 6d ago | MIT | A | 28 | Claude Code |
| food-analyzermickpletcher/AI-Skills | Skill | 20 | 3d ago | MIT | A | 41 | Claude Code, Codex |
| astrophotography-processingmickpletcher/AI-Skills | Skill | 20 | 3d ago | MIT | A | 44 | Claude Code |
| scienceworld-measurement-takerzjunlp/SkillNet | Skill | 1.4k | 5d ago | MIT | A | 81 | Claude Code, Codex |
| scienceworld-threshold-evaluatorzjunlp/SkillNet | Skill | 1.4k | 5d ago | MIT | A | 69 | Claude Code, Codex |
| scienceworld-animal-identifierzjunlp/SkillNet | Skill | 1.4k | 5d ago | MIT | A | 83 | Claude Code, Codex |