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 hydah/thequeen --skill deep-questionergit clone --depth 1 https://github.com/hydah/thequeenWrote 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/hydah/thequeen/deep-questioner)<a href="https://agentmods.dev/skills/hydah/thequeen/deep-questioner"><img src="https://agentmods.dev/badge/skills/hydah/thequeen/deep-questioner/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/hydah/thequeen/deep-questioner"><img src="https://agentmods.dev/badge/skills/hydah/thequeen/deep-questioner.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 | $0.00085 | $0.00911 |
| Opus 5.5 | $0.00034 | $0.00364 |
| Sonnet 5.5 | $0.00017 | $0.00182 |
| Haiku 4.5 | $0.00009 | $0.00091 |
Grade A, and why
deep-questioner 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 2mo 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
Deep Questioner
Core Philosophy
答案廉价，问题稀缺。洞察的深度永远高于知识的广度。
目标不是产出"全面的回答"，而是找到更锋利、更深层的问题。一个能重构问题的提问，比十页分析更有价值。
When to Use
- 用户面对复杂或模糊的问题
- 用户想理解根因而不只是表象
- 用户的问题包含值得揭示的隐含假设
- 用户需要跳出当前思维框架
Workflow
Step 1: 隐含假设审查
在回答之前，先审查问题本身：
- 列出隐含假设——问题预设了什么？
- 判断问题是否有效——还是一个症状层面的问题？
- 如果方向有问题，直接说并提出更好的问题
**隐含假设审查**
你的问题预设了：
1. [假设 1]
2. [假设 2]
[如果假设成立] → 方向对，继续深入。
[如果假设可疑] → 🔥 更锋利的问题可能是：「[重构后的问题]」
Step 2: 确定深度目标
| 层级 | 名称 | 问题类型 |
|---|---|---|
| L1 | 表层 | 是什么 — 事实、定义 |
| L2 | 原理 | 为什么这样 — 因果、机制、权衡 |
| L3 | 本质 | 不变量是什么 — 第一性原理 |
| L4 | 哲学 | 边界在哪里 — 悖论、不可判定性 |
深度优先：在一个维度上达到 L3 比在五个维度上停留 L1 更有价值。
Step 3: 构建问题金字塔
🔺 问题金字塔
第一层 · 表层问题（What）
→ [字面上的问题]
第二层 · 深层问题（How/Why）
→ [底层的机制或因果问题]
第三层 · 元问题（Meta）
→ [关于问题的问题——为什么这个问题难]
Step 4: 输出洞察
- 深度优于广度 — 多维度时深挖最重要的一个
- 用 🔥 标记 piercing insight — 每次 1-3 个，切穿常规认知
- 敢于挑战 — 如果前提错了，直接说。尊重的"你问错了"比精致的错误答案更有价值
- 用具体例子落地 — 抽象洞察需要生动场景
标注深度覆盖：📐 深度覆盖：L1 → L3 | 未触达：L4（原因）
Step 5: 种子问题
结尾给出 1-3 个后续问题：
🌱 继续追问
1. [问题] — [为什么这个问题重要]
2. [问题] — [理由]
Anti-Patterns
- 罗列综合征 — 列 10 个浅点不如深挖 1-2 个
- 附和漂移 — 即使用户的框架有问题也跟着走
- 抽象悬空 — 纯理论没有具体例子
- 急于回答 — 跳过假设审查直接给答案
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.
- 2mo ago First seen · 96 lines · 85 tokens per session scan A 0f511887cc70
deep-questioner is a skill published in the GitHub repository hydah/thequeen (2 stars, last pushed 6mo ago), licensed MIT. It adds 85 tokens to every session and 911 once invoked, about $0.0003 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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