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 konglong87/superPM --skill pm-brainstormgit clone --depth 1 https://github.com/konglong87/superPMWrote 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/konglong87/superpm/pm-brainstorm)<a href="https://agentmods.dev/skills/konglong87/superpm/pm-brainstorm"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-brainstorm/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/konglong87/superpm/pm-brainstorm"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-brainstorm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00123 | $0.06338 |
| Opus 5.5 | $0.00049 | $0.02535 |
| Sonnet 5.5 | $0.00025 | $0.01268 |
| Haiku 4.5 | $0.00012 | $0.00634 |
Grade A, and why
pm-brainstorm 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 26d 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 — 692 lines — stays where its author put it; the contents beside it link to each section on GitHub.
输入与模式
先判断用户要的是简短讨论还是正式创意方案库。只讨论时不写文件、不创建目录;已有相关且有效的资料可复用,无需重复提问。仅在正式生成方案库且确认目标位置后创建 docs/01-需求调研/。只问缺失的关键信息,用户随时可以停止或跳过某种思考方法;不要把跳过的探索说成完成。
跨 Agent 交互规则
当流程要求与用户交互时:
- 如果当前环境支持 AskUserQuestion,使用 AskUserQuestion(最佳体验)。
- 如果当前环境不支持 AskUserQuestion,必须用普通聊天消息提出同样问题。
- 一次只问一个问题。
- 提问后必须停止当前回合,等待用户回答(STOP and WAIT)。
- 只讨论时不写文件;正式产物仅在用户要求并确认路径后写入。
- 已有相关且有效的 docs 可以复用,无需重复提问;不相关资料不能替代证据。
执行流程
下述为正式方案库模式的详细步骤。轻量讨论只做用户关心的部分,直接在聊天中给出可验证的方向;用户可停止或跳过方法步骤。跳过时标注未探索的部分,不伪称流程已完成。
例外:在步骤 3.3 中,用户选择 G“跳过整个挑战环节”时,跳过该环节并直接进入下个步骤。
步骤 1: 读取前置数据
如果存在需求调研报告:
使用 Read 工具读取 docs/01-需求调研/需求调研报告.md 作为参考。
若无法从内容判断已有报告是否与当前产品相关,再询问用户;相关且有效的内容直接复用。
使用 AskUserQuestion 询问:
检测到已有需求调研报告,是否适用于当前产品?
A) 适用 — 基于已有报告继续,但仍需确认关键信息是否准确 B) 不适用 / 不确定 — 进入快速模式,重新收集基础信息
跨 Agent 兜底:如果当前环境不支持 AskUserQuestion,用普通聊天消息提出同样问题,然后 STOP and WAIT for user response。
- 用户确认适用 → 提取已有信息;只问缺失或过期的关键内容,无需逐项重复确认。
- 用户确认不适用 / 不确定 → 进入快速模式,使用 AskUserQuestion 收集基础信息。
如果没有需求调研报告:
进入快速模式,使用 AskUserQuestion 收集基础信息:
仅在这些信息确实缺失且决定方向时逐一提问;已给出的信息直接使用。
📝 快速模式 - 请提供基础信息:
问题1:产品名称是什么? → STOP and WAIT for user response
问题2:目标用户是谁? → STOP and WAIT for user response
问题3:核心痛点是什么? → STOP and WAIT for user response
跨 Agent 兜底:如果当前环境不支持 AskUserQuestion,用普通聊天消息提出同样问题,然后 STOP and WAIT for user response。
步骤 2: 确定发散方向
使用 AskUserQuestion 询问:
正式方案库需要确定发散方向;轻量讨论可基于现有信息给初步选项,并标注假设。
🎯 我要探索产品的哪个方向?
A) 产品功能创新 - 探索核心功能、差异化特性 B) 用户增长方案 - 如何获取、激活、留存用户 C) 商业模式设计 - 如何变现、构建可持续商业模式 D) 用户体验优化 - 如何提升易用性、满意度 E) 灵感火花激发 - 我不知道做什么,需要AI帮我找灵感(v2.1新增) F) 其他方向(请手动输入)
跨 Agent 兜底:如果当前环境不支持 AskUserQuestion,用普通聊天消息提出同样问题,然后 STOP and WAIT for user response。
用户选择后,记录到变量 BRAINSTORM_DIRECTION
如果选择"灵感火花激发"(v2.1新增):
适用场景:用户不知道做什么产品,需要AI主动激发灵感
执行流程:
步骤 2.1: 灵感维度扫描 & Subagent 并行搜索(核心优化)
AI 提示用户:
🔍 灵感火花激发引擎启动(Subagent 并行模式)
我将同时从4个维度搜索灵感源,请稍候...
- 行业痛点 → Subagent 1
- 技术趋势 → Subagent 2
- 生活场景 → Subagent 3
- 跨界灵感 → Subagent 4
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.
- 26d ago Changed · -9 lines 8a54bc621e9e
- 1mo ago Changed · +9 lines 22f4c6c845dd
- 1mo ago First seen · 692 lines · 123 tokens per session scan A bfd389c6d66a
pm-brainstorm is a skill published in the GitHub repository konglong87/superPM (77 stars, last pushed 7d ago), licensed MIT. It adds 123 tokens to every session and 6,338 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-08-30.
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