Codeg is a collaborative workspace that brings multiple AI coding agents and their sessions into one searchable application. Developers use it to coordinate agents, delegate work, and review unattended coding tasks across desktop, server, Docker, and mobile clients. The catalogue entries are skills and instructions for workflows within Codeg.
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 xintaofei/codeg --skill scientific-schematicsgit clone --depth 1 https://github.com/xintaofei/codegWrote 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/xintaofei/codeg/scientific-schematics)<a href="https://agentmods.dev/skills/xintaofei/codeg/scientific-schematics"><img src="https://agentmods.dev/badge/skills/xintaofei/codeg/scientific-schematics/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/xintaofei/codeg/scientific-schematics"><img src="https://agentmods.dev/badge/skills/xintaofei/codeg/scientific-schematics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00068 | $0.05500 |
| Opus 5.5 | $0.00027 | $0.02200 |
| Sonnet 5.5 | $0.00014 | $0.01100 |
| Haiku 4.5 | $0.00007 | $0.00550 |
Grade A, and why
scientific-schematics 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- scientific-schematics — 100% identical, 0 lines differ
- scientific-schematics — 100% identical, 0 lines differ
- scientific-schematics — 94% identical, 4 lines differ
- scientific-schematics — 92% identical, 5 lines differ
- scientific-schematics — 91% identical, 43 lines differ
- scientific-schematics — 91% identical, 44 lines differ
- scientific-schematics — 91% identical, 5 lines differ
- scientific-schematics — 91% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 619 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientific Schematics and Diagrams
Overview
Scientific schematics and diagrams transform complex concepts into clear visual representations for publication. This skill uses Nano Banana 2 AI for diagram generation with Gemini 3.1 Pro Preview quality review.
How it works:
- Describe your diagram in natural language
- Nano Banana 2 generates publication-quality images automatically
- Gemini 3.1 Pro Preview reviews quality against document-type thresholds
- Smart iteration: Only regenerates if quality is below threshold
- Publication-ready output in minutes
- No coding, templates, or manual drawing required
Quality Thresholds by Document Type:
| Document Type | Threshold | Description |
|---|---|---|
| journal | 8.5/10 | Nature, Science, peer-reviewed journals |
| conference | 8.0/10 | Conference papers |
| thesis | 8.0/10 | Dissertations, theses |
| grant | 8.0/10 | Grant proposals |
| preprint | 7.5/10 | arXiv, bioRxiv, etc. |
| report | 7.5/10 | Technical reports |
| poster | 7.0/10 | Academic posters |
| presentation | 6.5/10 | Slides, talks |
| default | 7.5/10 | General purpose |
Simply describe what you want, and Nano Banana 2 creates it. All diagrams are stored in the figures/ subfolder and referenced in papers/posters.
Quick Start: Generate Any Diagram
Create any scientific diagram by simply describing it. Nano Banana 2 handles everything automatically with smart iteration:
# Generate for journal paper (highest quality threshold: 8.5/10)
python scripts/generate_schematic.py "CONSORT participant flow diagram with 500 screened, 150 excluded, 350 randomized" -o figures/consort.png --doc-type journal
# Generate for presentation (lower threshold: 6.5/10 - faster)
python scripts/generate_schematic.py "Transformer encoder-decoder architecture showing multi-head attention" -o figures/transformer.png --doc-type presentation
# Generate for poster (moderate threshold: 7.0/10)
python scripts/generate_schematic.py "MAPK signaling pathway from EGFR to gene transcription" -o figures/mapk_pathway.png --doc-type poster
# Custom max iterations (max 2)
python scripts/generate_schematic.py "Complex circuit diagram with op-amp, resistors, and capacitors" -o figures/circuit.png --iterations 2 --doc-type journal
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.
- 1mo ago First seen · 619 lines · 68 tokens per session scan A d8d1d9dc913d
scientific-schematics is a skill published in the GitHub repository xintaofei/codeg (4,087 stars, last pushed yesterday), licensed Apache-2.0. It adds 68 tokens to every session and 5,500 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.
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