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 Immortalqx/my_codex_skills --skill phd-figure-designergit clone --depth 1 https://github.com/Immortalqx/my_codex_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/immortalqx/my_codex_skills/phd-figure-designer)<a href="https://agentmods.dev/skills/immortalqx/my_codex_skills/phd-figure-designer"><img src="https://agentmods.dev/badge/skills/immortalqx/my_codex_skills/phd-figure-designer/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/immortalqx/my_codex_skills/phd-figure-designer"><img src="https://agentmods.dev/badge/skills/immortalqx/my_codex_skills/phd-figure-designer.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 | $0.00115 | $0.01462 |
| Opus 5 | $0.00057 | $0.00731 |
| Sonnet 5 | $0.00023 | $0.00292 |
| Haiku 4.5 | $0.00012 | $0.00146 |
Grade A, and why
phd-figure-designer 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 11d 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 — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Figure Designer
Overview
A top-venue paper typically carries six to eight figures, with three carrying almost all the storytelling weight: the Motivated Example (Figure 1, on page 1 or the top of page 2), the Solution Overview (inside the Methodology section), and the Experimental Results figures (inside the Experiments section). Reviewers scan these three in under a minute to decide whether the paper is worth reading in detail; weak figures sink otherwise-strong papers.
This skill takes the user's intent (what they want to communicate) plus context (research area, method name, target venue) and returns the recommended paradigm, a layout sketch, labelling guidance, tool suggestion, and a quality-control audit against a universal rule set (vector format, font size, colour-blind-safe encoding, self-contained caption, honest axis ranges).
When to use this skill
- Before drawing any figure in a paper.
- The user asks to 'design a figure', 'draw Figure 1', 'plot experiment results', 'choose the right chart type'.
- The user has drawn a figure and wants a design audit.
- The user is unsure which figure type or paradigm to choose.
- Preparing camera-ready figures before submission.
When NOT to use this skill
- The user only wants generic plotting help (bar chart, line chart) outside a paper. Regular assistance suffices.
- The paper is not yet structured; use
phd-intro-drafterorphd-tech-paper-templatefirst to decide what figures the paper needs. - The user wants a review of an already-finished paper. Use
phd-pre-submission-reviewer.
Core procedure
Step 1: Figure-type identification
Decide which of the three core types the figure is. If the user's request does not match any, either it is a supporting figure (use the experimental-results guidance as a base) or it does not belong in the paper.
If the mode is figure-audit and the user has provided an image path, inspect the image with the available local image-viewing tool before proceeding to Step 2. Vision-based inspection enables the universal rule audit in Step 6 to check font legibility, colour palette, raster-vs-vector tells, and chartjunk directly rather than relying on user description. If no image is provided, continue in text-only mode and mark vision-only rules (font size, raster detection, colour palette) as "user must verify" in the final audit report.
What ships with it
6 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.
- 11d ago First seen · 127 lines · 115 tokens per session scan A 067fd17a5952
phd-figure-designer is a skill published in the GitHub repository Immortalqx/my_codex_skills (52 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 1,462 once invoked, about $0.0006 per session on Opus 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.
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