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 agentmods add skills/yyz666ai/learning-agent/project-practicenpx skills add yyz666ai/Learning-Agent --skill project-practicegit 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/project-practice)<a href="https://agentmods.dev/skills/yyz666ai/learning-agent/project-practice"><img src="https://agentmods.dev/badge/skills/yyz666ai/learning-agent/project-practice.svg" alt="Measured on agentmods" 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 | $0.00044 | $0.00642 |
| Opus 5 | $0.00022 | $0.00321 |
| Sonnet 5 | $0.00009 | $0.00128 |
| Haiku 4.5 | $0.00004 | $0.00064 |
Grade A, and why
project-practice 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 4d 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
项目实战
以「能跑通的最小项目」为单位，边做边补知识缺口，而不是先学完所有概念再动手。
执行流程
- 确认项目目标与验收标准：项目做什么、做完能演示什么、代码规模上限（初学者 50–200 行起步）。
- 按任务复杂度拆成可运行的里程碑；用户指定三步或其他合理数量时遵循该要求，不固定套用 4–6 步。每个里程碑只补最小知识缺口（缺口对应 concept-map 里的概念）。
- 每个里程碑循环：讲这一步需要的概念（可路由
concept-teaching）→ 写代码 → 运行验证 → 提交一条「里程碑证据」。 - 练习项目建在
$USER_DIR/projects/<项目名>/（一个项目一个文件夹）；已有课件指定 practice_path 时沿用该路径，不另造目录。学习者用编辑器打开该文件夹；临时小练习放$USER_DIR/workspace/demos/。 - 每个里程碑结束做小结：学会了什么、踩了什么坑（坑可提炼为知识原子候选，交策展）。
- 全部完成后，让学习者独立做一个「变体小项目」（改需求），作为掌握证据（对齐 L5 独立迁移门禁）。
边界
- 生成 Go 并发、HTTP 请求取消或资源泄漏练习时，先读本 Skill 的
references/go-cancellation.md。必须分别核对正常完成、主动取消和清理三条路径，不能因为“立即取消”就假称所有功能正确。 - 要求
go test等命令时，说明测试文件、模块初始化、执行位置及观察结果；按课件提供的真实练习目录操作，不让用户另猜目录。 - 不替学习者写完整代码；里程碑里只给「刚好够」的提示（遵守
references/hint-policy.md的 L0–L5）。 - 项目复杂度必须匹配当前掌握度；出现明显知识断层时退回补概念。
- 依赖安装、外部 API、密钥一律先确认，不擅自引入。
状态边界
- 里程碑通过写入
milestone_completed事件；「变体项目独立完成」才算该项目的掌握证据。 - 运行通过不等于掌握——还需学习者解释「为什么这么写」。
What ships with it
2 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.
- 4d ago First seen · 31 lines · 44 tokens per session scan A ae1210a44f62
project-practice is a skill published in the GitHub repository yyz666ai/Learning-Agent (1 stars, last pushed 4d ago), licensed MIT. It adds 44 tokens to every session and 642 once invoked, about $0.0002 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 2026-08-31.
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