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.
git clone --depth 1 https://github.com/XRenSiu/claude-code-forgeWrote 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/commands/xrensiu/claude-code-forge/brainstorming)<a href="https://agentmods.dev/commands/xrensiu/claude-code-forge/brainstorming"><img src="https://agentmods.dev/badge/commands/xrensiu/claude-code-forge/brainstorming/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/commands/xrensiu/claude-code-forge/brainstorming"><img src="https://agentmods.dev/badge/commands/xrensiu/claude-code-forge/brainstorming.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 | $0.00022 | $0.00521 |
| Opus 5.5 | $0.00009 | $0.00208 |
| Sonnet 5.5 | $0.00004 | $0.00104 |
| Haiku 4.5 | $0.00002 | $0.00052 |
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 1mo 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
Mission
通过苏格拉底式问答澄清需求，产出结构化的设计文档。
Usage
# 简短想法
/brainstorm "给 app 加个用户认证"
# 详细描述
/brainstorm "我们需要一个支持 OAuth2 和 JWT 的认证系统，企业客户要求 SOC2 合规"
# 从文件读取
/brainstorm @docs/ideas/payment-v2.txt
Behavior
- 激活
brainstormingskill - 执行 6 阶段流程：
- 阶段 1：侦察（静默分析代码库）
- 阶段 2：发散探索（2-3 个方案）
- 阶段 3：提问（每次一个问题）
- 阶段 4：收敛呈现（分块 200-300 字）
- 阶段 5：文档（保存 design-doc）
- 阶段 6：交接（指引下一步）
- 保存设计文档到
docs/designs/YYYY-MM-DD-<topic>.md
Output
- 设计文档：
docs/designs/YYYY-MM-DD-<topic>.md - 包含：问题定义、需求澄清记录、推荐方案、否决方案、风险、范围边界、成功指标
Next Steps
澄清完成后：
# 生成 PRD
/prd docs/designs/<file>.md
# 或全流程自动化
/pdforge --from-design docs/designs/<file>.md
When to Use
| 场景 | 使用 /brainstorm? |
|---|---|
| 需求模糊，需要探索 | ✅ 是 |
| 新功能设计 | ✅ 是 |
| 多种可能方案需要权衡 | ✅ 是 |
| 需求已经很清晰 | ❌ 直接用 /prd |
| 有现成的需求文档 | ❌ 直接用 /prd |
Anti-Patterns
🚫 不要在 brainstorming 完成后直接写代码 🚫 不要跳过 PRD 生成阶段 🚫 不要一次问多个问题 🚫 不要一次性输出完整设计
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.
- 1mo ago First seen · 67 lines · 22 tokens per session scan A 13b0e206e069
brainstorming is a command published in the GitHub repository XRenSiu/claude-code-forge (2 stars, last pushed 29d ago), licensed MIT. It adds 22 tokens to every session and 521 once invoked, about $0.0001 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 2026-09-05.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.