@JetBrains

创作者 · JetBrains

最近更新 · 2026年8月24日

brainstorming

审查 · 69已收录

Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a nontrivial design decision. Turns the user's request into a validated design — recorded as a spec-graph task-spec — before any implementation. Do not skip th

OpenAgentSkill 信任评分
69/100

仅限沙盒

质量62/100
审计79/100
Stars38
Verified installs0

安装目标

Codex 安装提示词

Install the "brainstorming" agent skill from https://github.com/JetBrains/thinkrail/tree/main/packages/pi-thinkrail-workflow/skills/brainstorming. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a nontrivial design decision. Turns the user's request into a validated design — recorded as a spec-graph task-spec — before any implementation. Do not skip this because a change looks small. After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {"event_id":"install_<unique-id>","skill_slug":"jetbrains-brainstorming","task":"Install brainstorming","agent":"codex","outcome":"success","install_used":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.

供给资产档案

研究与知识工作

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

浏览赛道

场景

研究 Agent

I need my agent to research a topic, compare sources, and produce a concise report.

适配 Agent

Claude Code + CLI + Codex

适用于 Codex、Claude Code、Cursor、CLI 或自定义 Agent。

安装

就绪

npx skills add JetBrains/thinkrail --skill brainstorming

维护状态

新鲜

今天有推送

风险

需审查

Financial research output is not financial advice; require human review before any live investment decision

GitHub 质量

38

62/100 质量 · 77/100 信任

覆盖标签

研究研究 Agentagent-skill

审查说明

Financial research output is not financial advice; require human review before any live investment decision · Low GitHub adoption signal

Agent 采用评分卡

一眼查看信任、审计与安装准备度

这些分数综合公开仓库元数据、OpenAgentSkill 审查信号、维护新鲜度与安装准备度。它用于候选筛选,不替代人工审查。

质量

有潜力
62

有用的候选项,但采用前应与替代方案比较。

信任

仅限沙盒
69

有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。

审计

需审查
79

对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。

OpenAgentSkill 信任评分 v5

安装前需人工审查

仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

38 个 GitHub Stars

仓库活跃度

38 个 Star,8 个 Fork

维护状态

今天有推送

许可证

Apache-2.0

安装

npx skills add JetBrains/thinkrail --skill brainstorming

安装安全性

标准软件包或运行时安装路径

权限范围

文件系统或文档访问

Agent 结果

暂未有 Agent 结果数据

文档

Usable metadata, review docs

风险摘要

生产前审查

  • Financial research output is not financial advice; require human review before any live investment decision.
  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 38 GitHub stars

安装准备度

安装路径可用

  • 安装路径可用
  • 仓库证据可用
  • 已声明许可证
  • 暂无 Agent 验证结果证据

Agent 可读元数据

这个 Skill 的机器可读决策数据。

使用此区块或内嵌 JSON 判断 Agent 是否应安装该 Skill、选择替代方案,或先请求人工审查。

View technical data+

适用任务

  • 研究 Agent 工作流
  • Claude Code 团队
  • builders willing to evaluate younger projects
  • 检索来源

适用 Agent

CodexClaude CodeCursorOpenAgentSkill CLICLI

安装决策

命令
npx skills add JetBrains/thinkrail --skill brainstorming
策略
审查
人工审查

信任与风险

信任
69/100
审计
79/100
风险级别
需审查

结果闭环

端点
/api/agent/outcome
事件 ID
resolve
结果
5

安装命令

npx skills add JetBrains/thinkrail --skill brainstorming

不适用场景

  • 需要厂商支持 SLA 的团队
  • production agents without a repository review
  • Low GitHub adoption signal
  • 暂未有 OpenAgentSkill 使用反馈数据
  • Financial research output is not financial advice; require human review before any live investment decision

Agent 安全 v2

63/100 · 安装前审查

已审查并附权限说明审查

可用候选,但 Agent 在安装前应展示权限与审计说明。

在真实工作区安装前需要人工批准。

通过 API 解析

网络访问

Skill 可能访问远程页面、API、仓库或外部服务。

文件系统访问

Skill 可能读取或写入项目文件、文档、生成产物或本地工作区状态。

  • Financial research output is not financial advice; require human review before any live investment decision

Agent 解析计划

让 Agent 在安装前验证匹配度。

Resolve API 返回首选 Skill、替代方案、安全策略、审计说明、安装目标和可直接执行的提示词,无需抓取此页面。

打开文本计划

Agent 应检查

  • 从 Resolve API 检查任务匹配与替代方案。
  • 检查审计评分、信任评分和安全策略警告。
  • 检查 Codex、Claude Code、Cursor 或 CLI 的安装目标兼容性。

复制提示词

Task: Use brainstorming in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20brainstorming%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jetbrains-brainstorming/install
Install command: npx skills add JetBrains/thinkrail --skill brainstorming
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.

Agent 交接

把安装路径交给 Agent,而不是再给一个目录页。

通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。

打开安装 API

Agent 提示词

Use brainstorming for this task. Review https://www.openagentskill.com/api/skills/jetbrains-brainstorming/install, then install with: npx skills add JetBrains/thinkrail --skill brainstorming

Registry 元数据

用于自动选择 Skill 的 Agent 可读档案。

本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号,让 Agent 无需抓取界面即可排序。

打开 Manifest

适配 Agent

61/100

研究 Agent

平台

Claude Code

审计报告

需审查 · 79/100

对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。

查看审计报告查看评估报告

Agent 决策面板

Fallback candidate for Research agents

先用此 Skill 做原型验证,并保留备选方案。

61
就绪度
原型验证
阶段

栈中角色

备选候选

主要匹配

研究 Agent

信任标签

先做原型验证

安装路径

命令已就绪

适用场景

  • 研究 Agent 工作流
  • Claude Code 团队
  • builders willing to evaluate younger projects

证据

  • 仓库近期活跃
  • 已提供安装命令或 GitHub 仓库
  • 62/100 质量档案

先审查

  • Low GitHub adoption signal
  • 暂未有 OpenAgentSkill 使用反馈数据

实施路径

  1. 1在沙盒 Agent 中安装它,并端到端完成一次研究 Agent任务。
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

信任档案

仅限沙盒

有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。

69
OpenAgentSkill 信任评分

GitHub 采用度

检查

38 个 GitHub Stars

Star/Fork 活跃度

检查

38 个 Star,8 个 Fork; 当前元数据中没有议题活跃度信息

近期维护

通过

今天有推送

许可证清晰度

通过

Apache-2.0

积极信号

  • AI 审查已通过
  • 安装路径可用
  • 仓库证据可用
  • 近期维护的仓库
  • 安装命令未发现明显高风险模式
  • 结果闭环已就绪,但需要首次真实 Agent 运行

安装前审查

  • Financial research output is not financial advice; require human review before any live investment decision.
  • Low GitHub adoption signal
  • Quality score needs review
  • GitHub adoption: 38 GitHub stars
  • Stars/forks activity: 38 stars, 8 forks; issue activity unavailable in current metadata
  • 暂未有真实 Agent 结果报告
  • 无人值守安装前需要人工审查

建议操作

仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。

质量档案

有潜力 适用于 Agent 工作流的候选

有用的候选项,但采用前应与替代方案比较。

62
GitHub Stars
38
新鲜度
今天
安装就绪
许可证
Apache-2.0
安装前审查: Low GitHub adoption signal

工作流匹配

在这些场景使用此 Skill

工作流匹配

加入完整工作流

替代方案短名单

安装前对比

可能适合该任务的相近 Skill。

对比全部

概览

--- name: brainstorming description: "Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a nontrivial design decision. Turns the user's request into a validated design — recorded as a spec-graph task-spec — before any implementation. Do not skip this because a change looks small." ---

# Brainstorming

## Brainstorm before you build

- Before starting any creative or feature work — a new feature, added functionality, a behavioral change, a nontrivial design decision — stop and run this workflow before writing implementation code. - The aim: turn the request into a validated design, recorded as a spec-graph `task-spec`, that the user has explicitly approved — not a guess you implement and hope lands. - Never implement during brainstorming. If you catch yourself opening a source file to make a change before the design is approved, stop.

## Anti-pattern: "this is too small to need this"

Every request goes through this, however small it looks. A one-line config change and a new subsystem both benefit from a few minutes of "what does the user actually want and why" — that is where wrong assumptions get caught cheaply. Scale the *depth* to the task; never skip the workflow entirely.

## The workflow

1. **Orient.** Use the spec-graph skill's tools first — `spec_grep`/`spec_get`/`spec_graph` — to find what the project already says about the area; read code second, to confirm details. 2. **Scope check.** If the request bundles multiple independent features or subsystems, say so and brainstorm them one at a time (or in parallel sub-sessions, the user's call) — don't blend unrelated decisions into one task-spec. 3. **Open a task-spec.** As soon as you understand roughly what's being asked, `spec_create` a `task-spec` at **`.thinkrail/context/TASK-<slug>.md`** (id, title, status: draft, parent: the nearest relevant module) to hold the design as it develops. `.thinkrail/context/` is the workspace's gitignored scratch dir (host-seeded, zero git footprint) yet stays scannable by the spec tools — the home for every temp doc, never committed. This file is the one artifact — update it live as decisions land; don't also keep a separate scratch doc. This works even in a project with no existing spec graph: a `task-spec` only needs frontmatter `id` and `type` to be a valid spec, no pre-existing graph required — don't skip this step just because nothing else in the project is specced yet. 4. **Clarify.** Ask what you need via `ask_user_question`, composing rounds per the **asking-user-questions** concept skill — read it before the first round. Resolve a full round, update the task-spec with what you learned, and only open a new round if the answers raised a genuinely new question. Per that concept's degradation norms, skipped questions or a host with no UI are not blockers: record your best-guess assumptions in the task-spec, explicitly marked unconfirmed, and continue. 5. **Propose approaches.** Once the ask is clear, write 2-3 approaches into the task-spec with trade-offs and a recommendation. When approaches are easiest to compare side by side, ask via a single-select `ask_user_question` with each approach as an option (label = approach name, description = its trade-off) instead of prose alone. 6. **Present the design.** Write it into the task-spec in sections scaled to their complexity; confirm with the user as each section lands, not only at the end. 7. **Self-review.** Before asking for final sign-off, reread the task-spec for: placeholders/TBDs, sections that contradict each other, scope that's actually multiple task-specs, and ambiguous requirements — fix what you find, don't just flag it. 8. **Promote.** When the design settles a boundary, contract, or decision that belongs in a durable spec, fold it into the relevant module's `SPEC.md` now — `spec_create` for a new module, `spec_update` for its frontmatter (draft → active as it firms up), `edit` for prose. Run `spec_validate` after structural changes. 9. **Final review, then build.** Ask the user to review the (now-promoted) design once more. Once approved, implement directly against it — there is no separate plan-writing step here. Before handing off, self-review the implementation diff the way step 7 reviewed the spec: no silent lint/type suppressions (a gate error is a design signal — question the flagged state or dependency before guarding it; any genuinely-needed suppression gets explicit user sign-off first), no nontrivial derivation duplicated across files (centralize it), no rationale left as code comments (near-zero comments: decisions and invariants go to the owning spec per the writing-specs bar; only lint directives and rare one-line hazard notes survive), and when the change replaced a pattern, sweep the repo for remnants of the old one. Keep the task-spec and the durable specs honest as the code lands, and retire the task-spec once **the work itself** is done, not merely once the design was promoted.

## What a good task-spec looks like

- Scoped to one piece of work — if it's accreting unrelated decisions, split it. - States the request, the decision(s) made and why, the approaches considered and why they were or weren't picked, and anything the user explicitly deferred or declined to answer. - Gets promoted, not copied: once a decision belongs in a module's `SPEC.md`, move it there and reference it from the task-spec rather than keeping two copies that can drift.

技术详情

版本
1.0.0
许可证
Apache-2.0
最近更新
2026年8月24日
发布时间
2026年8月24日

决策摘要

备选候选

61
就绪
原型验证
阶段

仓库近期活跃

审计

安装审查

安装与采用审查

79
需审查
安全性
86/100
维护状态
100/100
安装
92/100
打开完整审计查看评估报告

Agent 验证证据

Agent 验证证据

来自解析、审查、安装和一次小范围运行后的结果报告。

0
已验证
Needs first agent run自动安装: 先审查最近: 未知
成功率
近期失败
结果
0
输出质量
失败
0
不相关
0
安装次数
0
风险拦截
0
需要配置
0
生产环境
0

暂时没有 Agent 结果数据。首次 Agent 执行可以通过 /api/agent/outcome 报告成功、需要设置、风险拦截、失败或不相关。

安装

加入 Agent 工作流

免费且开源. 在生产 Agent 中安装前请先审查报告。

增长闭环

分享工具包

X

为 brainstorming 准备的场景化草稿,可手动发布到 X。

策展说明
A practical pick for design or creative work:

brainstorming: Use this BEFORE any creative or feature work: building a new feature, adding functionality, changing behavior, or making a...

38 stars

https://www.openagentskill.com/skills/jetbrains-brainstorming?ref=x
打开 X 草稿
可选:带安装命令的回复
Listing + install path for brainstorming:
https://www.openagentskill.com/skills/jetbrains-brainstorming?ref=x

Install: npx skills add JetBrains/thinkrail --skill brainstorming
打开回复草稿

收录来源

Registry 收录

可认领

此列表来自公开来源,维护者认领获批前不会标记为官方。

创作者
JetBrains
收录方
OpenAgentSkill 社区索引

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认领此 Skill

所有者认领

认领此 Skill 页面

这条 Registry 收录 列表归属于 JetBrains,但尚未标记为官方。认领后可增加已验证所有者信号,使后续发布、安装和审计更新更值得信赖。

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/jetbrains-brainstorming?metric=listed&label=Listed)](https://www.openagentskill.com/skills/jetbrains-brainstorming)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/jetbrains-brainstorming?metric=trust&label=Trust)](https://www.openagentskill.com/skills/jetbrains-brainstorming)
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[![Agent Proven](https://www.openagentskill.com/api/badge/jetbrains-brainstorming?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/jetbrains-brainstorming)

作者

J

JetBrains

@jetbrains

平台适配

健康信号

GitHub Stars
38
质量评分
34/100
最近 GitHub 推送
2026年8月24日
框架提示
未知
OpenAgentSkill 浏览量
0
复制安装命令
0
跳转点击
0

社区信号

告诉我们这个 Skill 是否对你的 Agent 工作流有帮助。汇总反馈会持续改善排序。

信任与安全

仅限沙盒

69
  • GitHub 采用度38 个 GitHub Stars检查
  • Star/Fork 活跃度38 个 Star,8 个 Fork; 当前元数据中没有议题活跃度信息检查
  • 近期维护今天有推送通过
  • 许可证清晰度Apache-2.0通过
  • README/SKILL.md 完整度公开元数据需要更完整的 README/SKILL.md 上下文信息
  • 依赖与运行时风险公开元数据中未发现主要依赖风险提示通过