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-structure-thinking)<a href="https://agentmods.dev/commands/an8079/take-skills/takes-structure-thinking"><img src="https://agentmods.dev/badge/commands/an8079/take-skills/takes-structure-thinking.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 | $0.00025 | $0.00782 |
| Opus 5 | $0.00013 | $0.00391 |
| Sonnet 5 | $0.00005 | $0.00156 |
| Haiku 4.5 | $0.00003 | $0.00078 |
Grade A, and why
structure_thinking 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 7d 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
/structure_thinking - 架构思维分析
顶级架构师视角的深度架构分析，直接扫描项目代码结构，评估系统的可扩展性、可维护性、性能、安全性等。无需规格文档，直接分析项目代码。
使用方式
/structure_thinking
或
架构分析
分析架构
架构思维
项目架构
看看项目结构
前置条件
无需规格文档。直接扫描当前项目代码进行分析：
- 扫描
src/目录结构 - 扫描
scripts/目录 - 扫描
commands/目录 - 分析
package.json依赖关系 - 扫描
rules/和agents/配置
分析维度
| 维度 | 说明 | 权重 |
|---|---|---|
| 架构完整性 | 模块划分是否清晰、边界是否明确 | 20% |
| 可扩展性 | 是否易于添加新功能、模块解耦程度 | 20% |
| 可维护性 | 代码组织、命名规范、文档完整性 | 15% |
| 性能设计 | 是否有性能瓶颈、是否有缓存策略 | 15% |
| 安全性 | 是否有安全漏洞、是否有防护机制 | 15% |
| 技术选型 | 技术栈是否合理、是否有更好的选择 | 15% |
输出内容
架构分析报告
| 内容 | 说明 |
|---|---|
| 架构评分 | 1-10 分 |
| 优势分析 | 当前架构的优势 |
| 问题识别 | 发现的问题列表 |
| 风险评估 | 潜在风险 |
| 改进建议 | 具体改进方案 |
架构图（如果需要）
┌─────────────────────────────────────┐
│ 系统架构图 │
├─────────────────────────────────────┤
│ ┌─────────┐ ┌─────────┐ │
│ │ Frontend │───▶│ Backend │ │
│ └─────────┘ └─────────┘ │
│ │ │
│ ┌────────────┼────────────┐ │
│ │ │ │ │
│ ┌──────▼──┐ ┌─────▼────┐ ┌────▼────┐│
│ │ Database │ │ Cache │ │ Queue ││
│ └─────────┘ └──────────┘ └────────┘│
└─────────────────────────────────────┘
与 /auto-interview 的关系
| 维度 | /structure_thinking | /auto-interview |
|---|---|---|
| 输入 | 项目代码（直接分析） | 需求描述（生成规格文档） |
| 目标 | 分析现有架构 | 生成产品规格 |
| 时机 | 了解现有系统 | 新项目启动 |
两者独立，用户手动选择使用哪个。
提示： /structure_thinking 直接分析项目代码，无需准备文档。
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.
- 7d ago First seen · 89 lines · 25 tokens per session scan A eb1a537e2eb8
structure_thinking is a command published in the GitHub repository an8079/take-skills (4 stars, last pushed 5mo ago), licensed MIT. It adds 25 tokens to every session and 782 once invoked, about $0.0001 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.