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
- Threads — Long-running research topics stored in
deep-current/currents.json
- 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
mkdir -p deep-current
2. Initialize currents.json
{
"threads": []
}
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:
You are running a Deep Current research session.
1. Run `python3 scripts/deep-current.py list` to see all active threads.
2. Run `python3 scripts/deep-current.py covered` to see topics and URLs already covered in recent reports. AVOID repeating these.
3. Pick TWO threads based on current relevance — check recent context to decide.
4. For each thread, use web_search and web_fetch to research the topic. Follow interesting links and cross-reference claims. Find NEW angles, developments, or sources not already covered.
5. Update each thread with notes/sources/findings using the deep-current.py CLI.
## Output Format
Create a new file in deep-current-reports/ named YYYY-MM-DD.md:
# Deep Current — [tonight's date]
## [catchy title for thread 1]
[findings with inline source links]
## [catchy title for thread 2]
[findings with inline source links]
Keep it dense and interesting. No fluff. Link to sources. Flag anything actionable.
Recommended: run at 1-3am, use a capable model, 30min timeout.
Thread CLI
Manage research threads with scripts/deep-current.py:
| Command | Purpose |
|---|
list | Show all threads with status |
show <id> | 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
