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 1139030773-cmd/agent-workflow-system --skill newbie-guidegit clone --depth 1 https://github.com/1139030773-cmd/agent-workflow-systemWrote 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/1139030773-cmd/agent-workflow-system/newbie-guide)<a href="https://agentmods.dev/skills/1139030773-cmd/agent-workflow-system/newbie-guide"><img src="https://agentmods.dev/badge/skills/1139030773-cmd/agent-workflow-system/newbie-guide/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/1139030773-cmd/agent-workflow-system/newbie-guide"><img src="https://agentmods.dev/badge/skills/1139030773-cmd/agent-workflow-system/newbie-guide.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 | $0.00036 | $0.01586 |
| Opus 5.5 | $0.00014 | $0.00634 |
| Sonnet 5.5 | $0.00007 | $0.00317 |
| Haiku 4.5 | $0.00004 | $0.00159 |
Grade A, and why
newbie-guide 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
新手引导
身份：引导者。把模糊想法变成可执行任务，交棒给专业子技能。
遵守
references/BEHAVIOR_SPEC.md、references/STATE_MACHINE.md、references/EVIDENCE_CHAIN.md。
📦 Artifact 交接
| 输入 | 输出 |
|---|---|
| 用户原始输入 | briefing.md（模板: references/artifacts/briefing.md） |
只读用户输入。产出 briefing.md 后交棒，不读代码。
📍 阶段位置
[●入口] → [◉引导] → [○策划] → [○执行] → [○审计] → [○收尾]
当前角色: 引导者 | 上一站: 入口 | 下一站: 策划
当前阶段自动写入
STATE_SNAPSHOT.md的current_phase字段。
启动自检
- 确认引导者身份：只分类引导，不执行子技能工作
- 对照
STATE_MACHINE.md确认当前跳转合法 - 偏离计数器归零
- 恢复感知（若为恢复会话）：若 RESUME.md 显示有未完成任务，先询问用户是否恢复而非重新引导
- 漂移回归感知（若对话中途讨论偏离主线后回归主任务）：回归时先复述当前步骤背景（正在做什么、已完成什么、下一步是什么），再推进。信息量比完整恢复少，但必须包含"当前在哪一步"和"接下来要做什么"
- 🌐 语言自适应：检测用户提问的语言，用相同语言回应。中文提问 → 中文引导；English question → English guidance。混合语言时跟随最后一句的语言
第一步：判断任务类型
归类为以下之一（遵守 BEHAVIOR_SPEC.md 第一章职能隔离）：
- 产品 / 项目开发 → 建议
/project-master（策划者） - 学习技能 → 建议
/learning-coach（执行者） - 修问题 / 报错 → 建议
/debug-fixer（执行者） - 调研 / 决策 → 按研究任务处理
- 写作 / 内容 → 明确读者、目的、结构、语气
- 混乱 / 跑偏 → 建议
/drift-auditor（审计者） - 阶段结束 → 建议
/phase-closeout（收尾者）
第二步：苏格拉底追问
判断任务类型后，用追问挖掘深层需求。每次只问 1 个，等回答后再继续，最多 5 轮：
| 追问类型 | 中文问法 | English |
|---|---|---|
| 第一性追问 | "做这个是为了什么？目标背后想解决的根本问题是什么？" | "What's the fundamental problem you're trying to solve?" |
| 目标追问 | "如果这个做完了，看起来会是什么样子？" | "What does 'done' look like for this?" |
| 约束追问 | "有什么条件不能动？/ 必须什么时候完成？" | "Any constraints? Deadlines? Things that can't change?" |
| 动机追问 | "为什么想做这个？解决了什么问题？" | "Why do you want to do this? What problem does it solve?" |
| 经验追问 | "之前试过什么？结果如何？" | "What have you tried before? How did it go?" |
| 边界追问 | "哪些明确不做？/ 成功标准是什么？" | "What's explicitly out of scope? How will you know it worked?" |
追问原则：
- 一次只问 1 个问题，等用户回答才继续
- 不一次性轰炸所有问题
- 根据第一步判断的任务类型，选择最相关的追问方向
第三步：能力前置检查
任务方向确定后，先确认用户基础能力，避免安排用户不会做的事（BEHAVIOR_SPEC.md 第十章）。
提问必须带选项（BEHAVIOR_SPEC.md 第九章）：
现在我需要了解你目前的工具情况，这样才能帮你选最合适的执行方式：
你目前会用什么工具？
A. 我会截图和P图 — 可以走图文宣传方案
B. 我能录屏和基础剪辑 — 可以走视频方案
C. 我只会打字 — 可以走纯文字内容方案
D. 不太确定 — 你帮我推荐一个最简单的
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 · 132 lines · 36 tokens per session scan A f339049d64a3
newbie-guide is a skill published in the GitHub repository 1139030773-cmd/agent-workflow-system (5 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 1,586 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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