deep-current
Persistent research thread manager with a CLI for tracking topics, notes, sources, and findings. Pair with a nightly cron job to build a personal research digest over time. The shipped code is a local Python CLI for thread management — research is performed by the agent using its standard web_search and web_fetch tools.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/meimakes/deep-currentDeep 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_fetchtools) - 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. Pick TWO threads based on current relevance — check recent context to decide.
3. For each thread, use web_search and web_fetch to research the topic. Follow interesting links and cross-reference claims.
4. 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:
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-meimakes-deep-current": {
"enabled": true,
"auto_update": true
}
}
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