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 yyz666ai/Learning-Agent --skill visual-explainergit clone --depth 1 https://github.com/yyz666ai/Learning-AgentWrote 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/yyz666ai/learning-agent/visual-explainer)<a href="https://agentmods.dev/skills/yyz666ai/learning-agent/visual-explainer"><img src="https://agentmods.dev/badge/skills/yyz666ai/learning-agent/visual-explainer/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/yyz666ai/learning-agent/visual-explainer"><img src="https://agentmods.dev/badge/skills/yyz666ai/learning-agent/visual-explainer.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 | $0.00033 | $0.00291 |
| Opus 5.5 | $0.00013 | $0.00116 |
| Sonnet 5.5 | $0.00007 | $0.00058 |
| Haiku 4.5 | $0.00003 | $0.00029 |
Grade A, and why
visual-explainer 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.
What it actually says
可视化讲解
图只服务一个关键判断。流程、状态变化、调用顺序、分支和组件关系优先用 Mermaid，让学习者先看到全貌，再进入代码。
选择图型
- 执行步骤或条件分支：
flowchart流程图。 - 请求、回调或消息先后：
sequenceDiagram。 - 状态迁移：
stateDiagram-v2。 - 模块依赖或目录：
flowchart或classDiagram，保持节点少。
教学要求
- 图前先提出要观察的问题，图后只解释最关键的 2–4 个关系。
- 节点使用学习者能理解的中文，首次出现的代码名用括号补充。
- Mermaid 必须可以独立渲染；不把整份代码塞进图。
- LangGraph、请求链、状态机和并发顺序等主题，只要图能显著降低理解成本，就在代码之前安排图。
- 图后紧跟一个点击判断或最小代码映射，确认学习者能把图和代码对应起来。
What ships with it
1 file 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 · 24 lines · 33 tokens per session scan A 93640c2f7d3e
visual-explainer is a skill published in the GitHub repository yyz666ai/Learning-Agent (0 stars, last pushed 1mo ago), licensed MIT. It adds 33 tokens to every session and 291 once invoked, about $0.0001 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 2026-08-31.
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skill-creator
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
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Interview the user relentlessly about a decision until every branch of the design tree is resolved, then record the result as a short ADR.
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Atomic git commit with conventional message. Use when the user says "commit", "save my changes", "commit this", or wants to create a git commit. Stages specific files, writes a conventional commit message with body explaining non-obvious decisions. Never uses git add -A.
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Run and debug Python scripts in the project. Use when the user says "run python", "execute this script", "debug this py file", or wants to run/modify a .py file. Handles dependency checks, linting, execution, and error analysis.
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Aki's contextual rule router — route EVERY task turn before acting, by what the task means (the domain it touches and the kind of act — write, decide, audit, ship), with listed signals as evidence, never as the test. Domains: code quality and structure (any code file), docs and any .md, UI copy and i18n, frontend…