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 agentmods add skills/misonl/ling/architecturenpx skills add MisonL/Ling --skill architecturegit clone --depth 1 https://github.com/MisonL/LingWhat 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 | $0.00059 | $0.00643 |
| Opus 5 | $0.00030 | $0.00321 |
| Sonnet 5 | $0.00012 | $0.00129 |
| Haiku 4.5 | $0.00006 | $0.00064 |
Grade A, and why
architecture 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 3d 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.
This is a copy
80% identical to architecture — 68 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Architecture Decision Framework（架构决策框架）
"Requirements drive architecture. Trade-offs inform decisions. ADRs capture rationale."（需求驱动架构，权衡决定结论，ADR 记录依据。）
Selective Reading Rule（选择性阅读规则）
Read ONLY files relevant to the request（仅阅读与请求相关的文档）！ Check the content map, find what you need（查阅内容地图，找到所需信息）。
| File（文件） | Description（描述） | When to Read（阅读时机） |
|---|---|---|
context-discovery.md |
Questions to ask, project classification（提问列表、项目分类） | Starting architecture design（开始架构设计） |
trade-off-analysis.md |
ADR templates, trade-off framework（ADR 模板、权衡分析框架） | Documenting decisions（记录决策） |
pattern-selection.md |
Decision trees, anti-patterns（决策树、反模式） | Choosing patterns（选择模式） |
examples.md |
MVP, SaaS, Enterprise examples（示例） | Reference implementations（参考实现） |
patterns-reference.md |
Quick lookup for patterns（模式速查） | Pattern comparison（模式对比） |
Related Skills（相关技能）
| Skill（技能） | Use For（用途） |
|---|---|
@[skills/database-design] |
Database schema design（数据库模式设计） |
@[skills/api-patterns] |
API design patterns（API 设计模式） |
@[skills/deployment-procedures] |
Deployment architecture（部署架构） |
Core Principle（核心原则）
"Simplicity is the ultimate sophistication."（至简即至繁）
- Start simple（从简单开始）。
- Add complexity ONLY when proven necessary（仅在必要时增加复杂性）。
- You can always add patterns later（随时可补充模式）。
- Removing complexity is MUCH harder than adding it（移除复杂性远比增加难）。
Validation Checklist（验证检查清单）
Before finalizing architecture（最终确定架构之前）：
- Requirements clearly understood（需求已清晰理解）。
- Constraints identified（约束条件已明确）。
- Each decision has trade-off analysis（每项决策有权衡分析）。
- Simpler alternatives considered（已考虑更简单替代方案）。
- ADRs written for significant decisions（重大决策已编写 ADR）。
- Team expertise matches chosen patterns（团队能力与模式匹配）。
What ships with it
5 files 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.
- 3d ago First seen · 58 lines · 59 tokens per session scan A 99e7f5aa5159
architecture is a skill published in the GitHub repository MisonL/Ling (9 stars, last pushed 5mo ago), licensed MIT. It adds 59 tokens to every session and 643 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 80% identical to architecture, differing in 68 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…