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 2428424081cn/Skill-mcp --skill architecture-decision-recordsgit clone --depth 1 https://github.com/2428424081cn/Skill-mcpWrote 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/2428424081cn/skill-mcp/architecture-decision-records)<a href="https://agentmods.dev/skills/2428424081cn/skill-mcp/architecture-decision-records"><img src="https://agentmods.dev/badge/skills/2428424081cn/skill-mcp/architecture-decision-records/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/2428424081cn/skill-mcp/architecture-decision-records"><img src="https://agentmods.dev/badge/skills/2428424081cn/skill-mcp/architecture-decision-records.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 | $0.00034 | $0.00498 |
| Opus 5 | $0.00017 | $0.00249 |
| Sonnet 5 | $0.00007 | $0.00100 |
| Haiku 4.5 | $0.00003 | $0.00050 |
Grade A, and why
architecture-decision-records 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 11d 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
架构决策记录 (ADR) 标准模板
每个重大技术决策必须以 Markdown 形式归档在 docs/adr/ADR-XXXX-标题.md 中。
ADR 结构规范
# ADR-0001: 采用 TanStack Query 替代 Redux 管理服务端状态
- **状态 (Status)**: [提议中 / 已采纳 / 已废弃 / 已被取代]
- **日期 (Date)**: 2026-08-24
- **决策者 (Deciders)**: 前端架构组
## 1. 背景与问题描述 (Context)
当前应用中大量使用 Redux Toolkit 手动维护 Loading、Error 以及缓存失效逻辑，导致 Boilerplate 冗长，频繁出现多组件间数据状态不一致的问题。
## 2. 决策驱动因素 (Decision Drivers)
- 减少 50% 以上的手动异步状态模板代码
- 原生支持 SWR（Stale-While-Revalidate）与自动轮询缓存
- 具备完善的 DevTools 与乐观更新支持
## 3. 备选方案对比 (Considered Options)
1. **方案 A: TanStack Query (React Query)**：专注于服务端异步状态，极简 Hook，零样板代码。
2. **方案 B: RTK Query**：集成在 Redux Toolkit 内，但配置复杂，学习成本较高。
3. **方案 C: SWR**：轻量但对复杂突变与乐观更新支持不如 TanStack Query 完备。
## 4. 最终决策结果 (Decision Outcome)
采纳 **方案 A (TanStack Query)**。
### 影响与正面后果 (Positive Consequences)
- 服务端数据拉取代码量减少 60%
- 自动处理组件卸载后的缓存清理
### 负面影响与应对措施 (Negative Consequences)
- 客户端纯 UI 状态（如弹窗开关、折叠面板）仍需保留轻量 Zustand 管理。
What ships with it
1 file 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.
- 11d ago First seen · 42 lines · 34 tokens per session scan A dfa58508a1be
architecture-decision-records is a skill published in the GitHub repository 2428424081cn/Skill-mcp (1 stars, last pushed 18d ago), licensed MIT. It adds 34 tokens to every session and 498 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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