Deep Current
A research thread manager for agents. Track topics you care about, accumulate notes and sources over time, and pair with a scheduled cron job to produce regular research digests.
Architecture
This skill ships one component: a Python CLI (scripts/deep-current.py) that manages research threads as local JSON data. It handles:
- - Creating, listing, and updating research threads
- Storing notes, sources, and findings per thread
- Thread lifecycle (active/paused/resolved) and decay
What this skill does NOT ship: web search, link following, or report generation. Those capabilities come from the agent's built-in tools (web_search, web_fetch). The cron job prompt instructs the agent to use those tools to research threads, then write findings to a report file.
In short: the CLI manages what to research. The agent's existing tools do the how.
How It Works
- 1. Threads — Long-running research topics stored in INLINECODE3
- Nightly job — A cron job tells the agent which threads to research (agent uses its own
web_search/web_fetch tools) - Reports — Each night's findings are written to
deep-current-reports/YYYY-MM-DD.md (one file per run) - Thread CLI — Manage threads between sessions (add, note, source, finding, status)
Setup
1. Create data directory
CODEBLOCK0
2. Initialize currents.json
CODEBLOCK1
3. Schedule the cron job
Create an isolated cron job that runs nightly. The agent will use its own web_search and web_fetch tools to research each thread, then use the CLI to record findings. Example prompt:
CODEBLOCK2
Recommended: run at 1-3am, use a capable model, 30min timeout.
Thread CLI
Manage research threads with scripts/deep-current.py:
| Command | Purpose |
|---|
| INLINECODE10 | Show all threads with status |
| INLINECODE11 |
Full thread details |
|
add <title> | Create new thread |
|
note <id> <text> | Add dated research note |
|
source <id> <url> [desc] | Add source/reference |
|
finding <id> <text> | Record key finding |
|
status <id> <active\|paused\|resolved> | Change thread status |
|
digest | Summary of all active threads |
|
decay | Prune stale threads (>90 days inactive + no recent notes) |
|
covered [days] | Show topics & URLs from recent reports (default 14 days) to avoid duplication |
Thread IDs are auto-generated slugs from the title. Prefix matching works for short IDs.
Report Format
Each run creates a standalone file in deep-current-reports/YYYY-MM-DD.md. Each report contains:
- - Date header
- 2+ research threads with catchy titles
- Dense findings with inline source links
- Actionable flags for anything the user should act on
One file per run — easy to browse, search, or archive.
Research Quality Guidelines
When running a research session (nightly or manual), the agent should:
- - Use
web_search to find sources, web_fetch to read them - Cross-reference claims across multiple sources
- Cite sources inline with markdown links
- Flag actionable items explicitly
- Write for a smart reader — dense, no filler
- Use catchy thread titles (this is morning reading, make it engaging)
- Distinguish speculation from sourced facts
Deep Current
面向智能体的研究线索管理器。追踪你关注的课题,随时间积累笔记与来源,并通过定时任务生成定期研究摘要。
架构
本技能提供一个组件:用于管理研究线索的Python CLI工具(scripts/deep-current.py),数据以本地JSON格式存储。其功能包括:
- - 创建、列出和更新研究线索
- 为每条线索存储笔记、来源和发现
- 线索生命周期管理(活跃/暂停/已解决)及衰减机制
本技能不包含: 网络搜索、链接追踪或报告生成功能。这些能力来自智能体内置工具(websearch、webfetch)。定时任务提示会指示智能体使用这些工具研究线索,并将发现写入报告文件。
简而言之:CLI管理研究什么,智能体现有工具负责如何研究。
工作原理
- 1. 线索 — 存储在deep-current/currents.json中的长期研究课题
- 夜间任务 — 定时任务告知智能体需要研究的线索(智能体使用自身的websearch/webfetch工具)
- 报告 — 每晚的研究发现写入deep-current-reports/YYYY-MM-DD.md(每次运行生成一个文件)
- 线索CLI — 在会话间管理线索(添加、笔记、来源、发现、状态)
设置
1. 创建数据目录
bash
mkdir -p deep-current
2. 初始化currents.json
json
{
threads: []
}
3. 安排定时任务
创建每晚运行的独立定时任务。智能体将使用自身的websearch和webfetch工具研究每条线索,然后使用CLI记录发现。示例提示:
你正在运行一个Deep Current研究会话。
- 1. 运行python3 scripts/deep-current.py list查看所有活跃线索。
- 运行python3 scripts/deep-current.py covered查看近期报告中已涵盖的课题和URL。避免重复这些内容。
- 根据当前相关性选择两条线索——检查近期上下文以做决定。
- 对每条线索,使用websearch和webfetch研究该课题。追踪有趣的链接并交叉验证声明。寻找尚未涵盖的新角度、新进展或新来源。
- 使用deep-current.py CLI用笔记/来源/发现更新每条线索。
输出格式
在deep-current-reports/目录下创建名为YYYY-MM-DD.md的新文件:
Deep Current — [今晚日期]
[线索1的吸引人标题]
[带有内联来源链接的发现]
[线索2的吸引人标题]
[带有内联来源链接的发现]
保持内容紧凑有趣。不要废话。链接到来源。标记任何可操作事项。
建议:在凌晨1-3点运行,使用能力较强的模型,超时时间设为30分钟。
线索CLI
使用scripts/deep-current.py管理研究线索:
| 命令 | 用途 |
|---|
| list | 显示所有线索及状态 |
| show <id> |
完整线索详情 |
| add
| 创建新线索 |
| note | 添加带日期的研究笔记 |
| source [desc] | 添加来源/参考 |
| finding | 记录关键发现 |
| status | 更改线索状态 |
| digest | 所有活跃线索摘要 |
| decay | 清理过时线索(超过90天无活动且无近期笔记) |
| covered [days] | 显示近期报告中的课题和URL(默认14天),避免重复 |
线索ID由标题自动生成短横线格式。支持前缀匹配短ID。
报告格式
每次运行在deep-current-reports/YYYY-MM-DD.md中创建独立文件。每份报告包含:
- - 日期标题
- 2条以上研究线索及吸引人标题
- 带有内联来源链接的紧凑发现
- 用户应关注的可操作标记
每次运行一个文件——便于浏览、搜索或归档。
研究质量指南
运行研究会话(夜间或手动)时,智能体应:
- - 使用websearch查找来源,webfetch阅读内容
- 跨多个来源交叉验证声明
- 使用Markdown链接内联引用来源
- 明确标记可操作事项
- 为聪明读者撰写——紧凑,无填充内容
- 使用吸引人的线索标题(这是晨读材料,要引人入胜)
- 区分推测与有来源的事实