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.
git clone --depth 1 https://github.com/an8079/take-skillsWrote 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/commands/an8079/take-skills/takes-deep-interview)<a href="https://agentmods.dev/commands/an8079/take-skills/takes-deep-interview"><img src="https://agentmods.dev/badge/commands/an8079/take-skills/takes-deep-interview.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 5.1 | $0.00034 | $0.00679 |
| Opus 5 | $0.00017 | $0.00340 |
| Sonnet 5 | $0.00007 | $0.00136 |
| Haiku 4.5 | $0.00003 | $0.00068 |
Grade A, and why
deep-interview 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 8d 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-interview - 苏格拉底式访谈
通过系统性提问理清模糊需求，生成高质量规格文档。
使用方式
/deep-interview
或
深度访谈
苏格拉底访谈
需求访谈
工作流程
- 问题探索 - 通过提问探索用户想法
- 维度覆盖 - 确保 8 个维度完整
- 模糊度评估 - 实时评估需求模糊度
- 规格生成 - 模糊度 ≤20% 时生成文档
- 用户确认 - 用户审核确认规格
8 维度完整性检查
| 维度 | 说明 |
|---|---|
| 业务理解 | 要解决什么问题、为谁解决、为什么现有方案不行 |
| 功能边界 | MVP 范围、哪些不做、边界情况 |
| 技术可行性 | 技术栈、架构方向、技术难点 |
| 数据流 | 数据从哪来、存到哪、怎么流转 |
| 接口契约 | 对外接口、输入输出 |
| 异常处理 | 错误场景、处理策略 |
| 非功能需求 | 性能、安全、可维护性 |
| 部署交付 | 部署方式、环境要求、交付物 |
模糊度评分机制
| 模糊度 | 状态 | 说明 |
|---|---|---|
| >50% | 高 | 需求严重模糊，继续提问 |
| 20-50% | 中 | 有一定模糊，需要澄清 |
| ≤20% | 低 | 通过，可以生成规格 |
输出内容
规格文档格式
# 规格文档
## 基本信息
- 项目名称：
- 创建日期：
- 模糊度评分：
## 业务理解
...
## 功能边界
...
## 技术可行性
...
## 数据流
...
## 接口契约
...
## 异常处理
...
## 非功能需求
...
## 部署交付
...
与 /auto-interview 的区别
| 维度 | /deep-interview | /auto-interview |
|---|---|---|
| 方式 | 苏格拉底提问 | 双 Agent 辩论 |
| 参与 | 用户与 AI | Agent A (PM) vs Agent B (架构师) |
| 输出 | 规格文档 | 更详细的技术架构文档 |
| 适用 | 需求模糊、需理清 | 需求相对清晰 |
提示： 使用 /deep-interview 当你不确定需求时，它会通过提问帮你理清思路。
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.
- 8d ago First seen · 102 lines · 34 tokens per session scan A efe188686454
deep-interview is a command published in the GitHub repository an8079/take-skills (4 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 679 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.