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 ttguy0707/codojo --skill dojo-plangit clone --depth 1 https://github.com/ttguy0707/codojoWrote 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/ttguy0707/codojo/dojo-plan)<a href="https://agentmods.dev/skills/ttguy0707/codojo/dojo-plan"><img src="https://agentmods.dev/badge/skills/ttguy0707/codojo/dojo-plan/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/ttguy0707/codojo/dojo-plan"><img src="https://agentmods.dev/badge/skills/ttguy0707/codojo/dojo-plan.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 | $0.00364 | $0.02637 |
| Opus 5.5 | $0.00146 | $0.01055 |
| Sonnet 5.5 | $0.00073 | $0.00527 |
| Haiku 4.5 | $0.00036 | $0.00264 |
Grade A, and why
dojo-plan 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dojo-plan — S2 计划生成
一句话定位：根据能力评估结果，生成个性化学习计划和进度跟踪表。
何时使用
- ✅ S1 能力评估已完成，
open-questions.md已填答案 - ✅ 用户说"生成学习计划"、"制定计划"
- ❌
open-questions.md不存在或未填答案 → 转dojo-assess - ❌
task.md已存在且用户想开始学习 → 转dojo-teach
前置条件
<repo-root>/.codojo/open-questions.md存在且末尾包含<!-- ASSESS_DONE -->标记
若前置条件不满足，提示用户并转到 dojo-assess。
工作流
Step 1：分析评估结果
读取 open-questions.md，提取：
- 用户已掌握的技术点（标记为"熟练掌握"）
- 用户部分掌握的技术点（"用过但不熟练"）
- 用户完全不了解的技术点（"完全不了解"/"听说过但没用过"）
- 用户的自由补充（学习目标、时间安排等）
Step 2：项目深度分析
在 Step 1 基础上，深入分析项目代码，梳理：
- 项目的模块划分和层次结构
- 模块间的依赖关系（先学什么后学什么）
- 每个模块涉及的核心概念和知识点
- 适合用来做实践练习的代码片段
Step 3：生成 task.md
根据 Step 1 + Step 2 生成 <repo-root>/.codojo/task.md：
强制规则：第一个知识点必须是「模块 0：项目全景」
无论用户水平高低，task.md 的第一个知识点固定为「0.1 项目全景」，让用户在钻进具体知识点之前先建立全局认知（先森林、后树木）。这是横向的整体视角，与后续逐个知识点的纵向深入互补。
「模块 0」是纯理论知识点（无实践任务），轻量讲解，1 个知识点讲完即可，内容聚焦三点：
- 分层结构：项目从根目录开始分了哪几层 / 哪几个模块
- 模块职责：每个模块/层各自负责什么
- 为什么这样分：这种分层/分包背后的设计意图（解耦、复用、职责单一等）
它由 S3 正常教学流程讲解，理论讲完用户确认理解即视为完成，不需要实践环节；用户学到一半想回看全局时，让 AI 重讲「0.1 项目全景」即可（S3 中断恢复机制本就支持跳回任意知识点）。
# 学习计划
> 根据你的能力评估自动生成，已掌握内容已跳过或简化。
## 学习概要
- **总知识点数**：N 个
- **预计学时**：约 X 小时
- **学习路径**：<简述先后顺序的逻辑>
## 模块 0：项目全景
### 0.1 项目全景：分层结构与模块关系
- **类型**：纯理论（无实践任务）
- **难度**：⭐
- **前置知识**：无
- **学习目标**：建立项目整体认知——知道项目分了哪几层/模块、各自职责、为什么这样分
- **理论要点**：从项目根目录出发，讲清分层结构、每个模块的职责、以及这种分层/分包的设计意图
- **涉及文件**：<项目根目录结构 + 各模块入口文件路径>
## 模块 1：<模块名>
### 1.1 <知识点名称>
- **类型**：理论 + 实践
- **难度**：⭐ / ⭐⭐ / ⭐⭐⭐
- **前置知识**：<依赖的知识点，如有>
- **学习目标**：<学完后能做什么>
- **理论要点**：<概述要讲什么>
- **实践任务**：<概述要做什么改动>
- **涉及文件**：<相关源文件路径>
### 1.2 <知识点名称>
...
## 模块 2：<模块名>
...
计划设计原则：
- 第一个知识点固定为「0.1 项目全景」，且无论用户水平高低都不可跳过、不可省略
- 已掌握的技术点标记为"跳过"或"快速回顾"（不占主要学时）
- 部分掌握的安排"巩固 + 补缺"
- 完全不了解的安排"完整讲解 + 充分实践"
- 知识点顺序遵循依赖关系（先基础后进阶）
- 每个知识点都要有理论和实践两部分
- 实践任务必须基于项目真实代码，而非虚构示例
Step 4：生成 schedule.md
生成 <repo-root>/.codojo/schedule.md：
# 学习进度
> 由 AI 自动维护，记录你的学习进度。
## 总进度
📊 ░░░░░░░░░░ 0% (0/N 知识点)
## 详细进度
| # | 知识点 | 状态 | 理论 | 实践 | 完成时间 |
|---|---|---|---|---|---|
| 0.1 | 项目全景：分层结构与模块关系 | ⚪ 未开始 | ⚪ | — | - |
| 1.1 | <名称> | ⚪ 未开始 | ⚪ | ⚪ | - |
| 1.2 | <名称> | ⚪ 未开始 | ⚪ | ⚪ | - |
| 2.1 | <名称> | ⚪ 未开始 | ⚪ | ⚪ | - |
| ... | ... | ... | ... | ... | ... |
**注意**：
- `0.1 项目全景` 必须作为第一行，且 N（总知识点数）需把它计入。
- `0.1` 是纯理论知识点，无实践环节，其「实践」列固定填 `—`（破折号，表示不适用）。S3 教学时该知识点理论讲完、用户确认理解即标记为 ✅ 已完成，不进入实践环节。
## 学习日志
<!-- AI 在每次教学后追加记录 -->
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 · 210 lines · 364 tokens per session scan A cbf400dece4d
dojo-plan is a skill published in the GitHub repository ttguy0707/codojo (71 stars, last pushed 3mo ago), licensed MIT. It adds 364 tokens to every session and 2,637 once invoked, about $0.0015 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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