Borrowing it
Nothing to install: this file belongs to funiq-lab/your-mind. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/funiq-lab/your-mind/main/.claude/commands/collect.mdgit clone --depth 1 https://github.com/funiq-lab/your-mindWrote 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/funiq-lab/your-mind/collect)<a href="https://agentmods.dev/commands/funiq-lab/your-mind/collect"><img src="https://agentmods.dev/badge/commands/funiq-lab/your-mind/collect/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/funiq-lab/your-mind/collect"><img src="https://agentmods.dev/badge/commands/funiq-lab/your-mind/collect.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 | $0.00011 | $0.01173 |
| Opus 5.5 | $0.00004 | $0.00469 |
| Sonnet 5.5 | $0.00002 | $0.00235 |
| Haiku 4.5 | $0.00001 | $0.00117 |
Grade A, and why
collect 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 2mo 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.
资讯收集命令
用户提供了一条资讯,请按以下步骤处理。
输入解析
用户输入可能包含以下元素(灵活组合):
| 元素 | 识别方式 | 必需性 |
|---|---|---|
| 网址 | 以 http:// 或 https:// 开头 | 可选 |
| 内容 | 文字描述(无网址时必需) | 条件必需 |
| 感想 | 用户的评论/思考 | 必需 |
| 标签 | 以 # 开头的词 | 可选 |
处理步骤
1. 解析输入
从用户消息中提取:
- 网址:如有 URL,提取出来
- 标签:如有 #开头 的词,提取为标签
- 内容与感想:剩余文字,根据语义判断哪部分是内容描述,哪部分是个人感想
2. 获取内容(如有网址)
如果用户提供了网址,按以下顺序尝试获取内容:
方式一:WebFetch(首选)
使用 WebFetch 获取:
- 文章标题
- 主要内容摘要(3-5 个要点)
方式二:Playwright(备选)
如果 WebFetch 失败(返回错误、无法访问、内容为空),使用 Playwright 模拟浏览器访问:
1. 使用 mcp__playwright__browser_navigate 打开网址
2. 等待页面加载完成
3. 使用 mcp__playwright__browser_snapshot 获取页面内容
4. 从快照中提取标题和主要内容
5. 使用 mcp__playwright__browser_close 关闭浏览器
常见需要 Playwright 的网站:
- X/Twitter (x.com, twitter.com)
- 需要登录的社交媒体
- 动态加载内容的 SPA 网站
- 有反爬虫机制的网站
方式三:用户描述(兜底)
如果以上方式都失败,使用用户提供的内容描述作为摘要。
3. 判断来源类型
根据输入判断来源类型:
- 有 URL →
网络文章 - 提到书籍/阅读 →
书籍摘录 - 提到播客/视频/听 →
播客/视频 - 提到聊天/对话/朋友说 →
对话记录 - 其他 →
个人观察
4. 智能打标签
如果用户没有提供标签,根据内容智能生成 2-4 个标签:
常用标签参考:
- 技术:
#AI#编程#产品#工具#开源 - 商业:
#金融#创业#投资#商业模式#市场 - 成长:
#思维#效率#习惯#认知#学习 - 生活:
#健康#阅读#旅行#生活方式
如果用户提供了标签,可以在此基础上补充建议(但以用户指定的为主)。
5. 确定归档文件
计算当前日期所属的周(周一到周日):
- 文件名格式:
YYYYMMDD-YYYYMMDD.md - 文件路径:
news/YYYYMMDD-YYYYMMDD.md
6. 写入资讯
有链接格式:
## [标题](URL)
**标签**:#标签1 #标签2
**来源**:网络文章
**收录时间**:YYYY-MM-DD HH:mm
### 内容摘要
> 要点1
> 要点2
> 要点3
### 个人感想
{用户的感想}
---
无链接格式:
## 标题(根据内容生成简短标题)
**标签**:#标签1 #标签2
**来源**:{来源类型}
**收录时间**:YYYY-MM-DD HH:mm
### 内容
{用户提供的内容}
### 个人感想
{用户的感想}
---
7. 更新标签索引
检查 news/tags.md 是否存在:
- 不存在则创建
- 存在则更新
标签索引格式:
# 标签索引
## #AI
- [[20251223-20251229#标题1]]
- [[20251223-20251229#标题2]]
## #投资
- [[20251223-20251229#标题3]]
8. 确认反馈
完成后告知用户:
- 资讯标题
- 归档文件
- 应用的标签
- 简短概述(如从网页获取了内容)
- 内容获取方式(WebFetch / Playwright / 用户描述)
注意事项
- 周文件不存在时,先创建并添加头部
- 新条目追加到文件末尾
- 标签索引按字母/拼音排序
- 尊重用户指定的标签,智能标签为补充
- Playwright 访问后记得关闭浏览器,避免资源占用
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.
- 2mo ago First seen · 165 lines · 11 tokens per session scan A 407072d166fc
collect is a command published in the GitHub repository funiq-lab/your-mind (11 stars, last pushed 10mo ago), licensed MIT. It adds 11 tokens to every session and 1,173 once invoked, about $0.0000 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.
collect compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| collectfuniq-lab/your-mind | Command | 11 | 10mo ago | MIT | A | 11 | Claude Code |
| minutes-ideassilverstein/minutes | Command | 1.6k | yesterday | MIT | A | 48 | Claude Code, OpenCode |
| learnReflexioAI/claude-smart | Command | 791 | 3d ago | Apache-2.0 | A | 14 | Claude Code |
| memory-storeverygoodplugins/mcp-automem | Command | 66 | yesterday | MIT | A | 10 | Claude Code |
| analyze-contextRohiRIK/OpenLtm | Command | 29 | 8d ago | MIT | A | 39 | Claude Code |
| cc-memoryTheLobbi/claude | Command | 21 | 7d ago | MIT | A | 0 | Claude Code |
| lians-recallLians-ai/Lians | Command | 17 | 5d ago | Apache-2.0 | A | 19 | Claude Code |