Borrowing it
Nothing to install: this file belongs to ChenZiHong-Gavin/llm-tech-report. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ChenZiHong-Gavin/llm-tech-report/main/.claude/skills/logo-generation/SKILL.mdgit clone --depth 1 https://github.com/ChenZiHong-Gavin/llm-tech-reportWrote 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/chenzihong-gavin/llm-tech-report/logo-generation)<a href="https://agentmods.dev/skills/chenzihong-gavin/llm-tech-report/logo-generation"><img src="https://agentmods.dev/badge/skills/chenzihong-gavin/llm-tech-report/logo-generation/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/chenzihong-gavin/llm-tech-report/logo-generation"><img src="https://agentmods.dev/badge/skills/chenzihong-gavin/llm-tech-report/logo-generation.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 | $0.00074 | $0.00729 |
| Opus 5 | $0.00037 | $0.00365 |
| Sonnet 5 | $0.00015 | $0.00146 |
| Haiku 4.5 | $0.00007 | $0.00073 |
Grade A, and why
logo-generation 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 10d 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.
What it actually says
Logo Generation (Timeline)
为 llm-tech-report 仓库生成时间轴 logo。所有模型名按发布时间堆叠在横轴上下，自动避免文字重叠。
文件结构
.claude/skills/logo-generation/
├── SKILL.md # 本文件
└── scripts/
├── generate.py # 入口：配置 + 调用
├── models.py # 数据：所有 (year, name, company) 条目
└── draw.py # 绘图：时间轴 + 防重叠算法
输出：仓库根目录 logo.png
依赖
pip install matplotlib numpy
使用
# 在仓库根目录下运行
python .claude/skills/logo-generation/scripts/generate.py
输出 logo.png（约 600KB，20x11 英寸，300 DPI）。
如何更新
新增模型
编辑 scripts/models.py，在 MODELS 列表中添加：
(2025.50, "ModelName", "CompanyKey"),
- year：
YYYY + MM/12（如 2025-06 =2025.50） - name：尽量简短（
DS-R1而非DeepSeek-R1） - company：必须在
generate.py的COLORS中存在
无需手动排序，脚本自动按时间排。
新增公司
generate.py的COLORS字典添加"NewCo": "#hex"draw.py的legend列表添加公司名（如需图例显示）
调整里程碑
generate.py 的 LANDMARKS 集合增减即可。里程碑字号 7.5pt，普通 5.8pt。
防重叠算法
- 纯数据坐标估算，不调用 renderer，秒级完成
- 文字宽 =
字符数 x 字号 x 0.55 / 72 x (x_range / fig_w) - 文字高 =
字号 x 1.2 / 72 x (y_range / fig_h) - 奇偶交替分配轴上/下，碰撞则逐层外推（步长 0.24），最多 30 层
注意事项
- 名称要短——长名字挤占横向空间，缩写优先
- 2023-2025 最拥挤——新增模型可微调 year 小数避免扎堆
- 模型超 ~150 个——需加大
FIG_H或减小y_step - 暗色背景——避免用太深的颜色（纯黑、深灰）
- 每次改数据后重跑——肉眼确认无溢出
What ships with it
4 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.
- 10d ago First seen · 77 lines · 74 tokens per session scan A 09f8f71f6ace
logo-generation is a skill published in the GitHub repository ChenZiHong-Gavin/llm-tech-report (37 stars, last pushed 22d ago), licensed MIT. It adds 74 tokens to every session and 729 once invoked, about $0.0004 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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