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/brainstormingnpx skills add MisonL/Ling --skill brainstorminggit 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 | $0.00053 | $0.01589 |
| Opus 5 | $0.00026 | $0.00794 |
| Sonnet 5 | $0.00011 | $0.00318 |
| Haiku 4.5 | $0.00005 | $0.00159 |
Grade A, and why
brainstorming 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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
头脑风暴与沟通协议
MANDATORY（强制要求）： 适用于复杂/模糊的请求、新功能开发或系统更新。
苏格拉底之门 —— 强制执行
触发时机
| 模式 | 行动 |
|---|---|
| 在没有细节的情况下要求“构建/创建/制作 [某物]” | 提出 3 个问题 |
| 涉及复杂功能或架构设计 | 在交付方案前先进行澄清 |
| 针对现有系统的更新/变更请求 | 确认影响范围 |
| 需求描述过于模糊 | 询问目的、用户群体及约束条件 |
强制要求：实施前的 3 个问题
- STOP（停止） —— 不得立即开始编写代码。
- ASK（提问） —— 至少提出 3 个关键问题：
- 目的：您要解决什么问题？
- 用户：谁将使用这个功能？
- 范围：哪些是 Must-have（必选），哪些是 Nice-to-have（可选）？
- WAIT（等待） —— 在收到明确回复后再继续后续步骤。
动态提问生成
** 严禁使用静态模板。** 请查阅 dynamic-questioning.md 了解核心原则。
核心原则
| 原则 | 含义 |
|---|---|
| 提问揭示后果 | 每个问题都应关联到一个架构决策 |
| 背景先于内容 | 首先理解是全新开发、功能增强、重构还是调试背景 |
| 最小可行问题 | 每个问题都必须能够排除掉某些特定的实现路径 |
| 生成数据而非假设 | 不要猜测 —— 带着 trade-offs（权衡）方案去询问 |
提问生成流程
1. 解析请求 -> 提取领域、功能点、规模指标
2. 识别决策点 -> 区分阻塞性决策与可延后决策
3. 生成问题 -> 优先级：P0 (阻塞) > P1 (高杠杆) > P2 (可选)
4. 格式化权衡方案 -> 包含：内容、原因、选项、默认值
提问格式（强制要求）
### [优先级] **[决策点]**
**问题：** [清晰简洁的问题描述]
**为什么要问：**
- [由此涉及的架构后果]
- [影响范围：成本/复杂度/工期/规模]
**备选方案：**
| 选项 | 优点 (+) | 缺点 (-) | 适用场景 |
|--------|------|------|----------|
| A | ... | ... | ... |
**若未指定：** [默认执行方案 + 理由]
关于特定领域的详细问题库与算法，请参阅：dynamic-questioning.md
进度汇报原则
原则： 透明度建立信任。状态必须是可见且可操作的。
状态看板格式
| Agent（智能体） | 状态 | 当前任务 | 进度 |
|---|---|---|---|
| [代理名称] | [OK] [RUN] ⏳[FAIL] [WARN] | [任务描述] | [% 或 数量] |
状态图标含义
| 图标 | 含义 | 使用场景 |
|---|---|---|
| [OK] | 已完成 | 任务已成功结束 |
| [RUN] | 运行中 | 正在执行相应指令 |
| ⏳ | 等待中 | 已阻塞，正在等待依赖项 |
| [FAIL] | 错误 | 任务失败，需要人工干预 |
| [WARN] | 警告 | 存在潜在问题，但不影响阻塞 |
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.
- 2d ago First seen · 165 lines · 53 tokens per session scan A 803507b0d7db
brainstorming is a skill published in the GitHub repository MisonL/Ling (9 stars, last pushed 5mo ago), licensed MIT. It adds 53 tokens to every session and 1,589 once invoked, about $0.0003 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 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…