What makes Deep Current notable?

1.4K downloads. Trust evidence available

How should Deep Current be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

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 di... Skill: Deep Current Owner: madebydia Summary: 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 di... Tags: latest:2.0.1 Version history: v2.0.1 | 2026-06-07T03:31:41.596Z | user Refresh publisher identity and metadata for madebydia v2.0.0 | 2026-03-24T17:18:08.340Z | user Add metadata, fix ClawHub listing v1.1.0

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.4k

Updated

Oct 10, 2026

Version

2.0.1

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.4K downloads reported by the source. Last updated 10/10/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 10, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 10, 2026
Adoption signal: 1.4K downloadsadoption · observed Oct 10, 2026
Latest release: 2.0.1release · observed Jun 7, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s176b3312npmthbt2chf2mccw183grfh:deep-current

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-madebydia-deep-current/snapshot"

Documentation

CLAWHUB

38,871 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: deep-current
description: 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.
metadata: {"openclaw":{"requires":{"bins":["python3"]},"writablePaths":["deep-current/","deep-current-reports/"],"homepage":"https://github.com/madebydia/deep-current","author":"Diana Park (@madebydia)"}}
---

# 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 `deep-current/currents.json`
2. **Nightly job** — A cron job tells the agent which threads to research (agent uses its own `web_search`/`web_fetch` tools)
3. **Reports** — Each night's findings are written to `deep-current-reports/YYYY-MM-DD.md` (one file per run)
4. **Thread CLI** — Manage threads between sessions (add, note, source, finding, status)

## Setup

### 1. Create data directory

```bash
mkdir -p deep-current
```

### 2. Initialize currents.json

```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]
[find

README.md

# Deep Current

A research thread manager for AI agents. Track topics over time, accumulate notes and sources, and pair with scheduled jobs to produce regular research digests.

Your agent picks the threads. Your agent does the searching. This tool keeps the state.

## What It Does

**Ships:** A zero-dependency Python CLI (`scripts/deep-current.py`) that manages research threads as local JSON.

**Doesn't ship:** Web search, link following, or report generation. Those come from your agent's own tools.

The CLI handles *what* to research. Your agent handles *how*.

## Quick Start

```bash
# Create a thread
python3 scripts/deep-current.py add "Carnivore Diet Research"

# Add notes, sources, findings as you go
python3 scripts/deep-current.py note carnivore "New study on protein satiety in women"
python3 scripts/deep-current.py source carnivore "https://example.com/study" "2024 protein satiety meta-analysis"
python3 scripts/deep-current.py finding carnivore "High-protein diets show 25% better satiety scores"

# See what you're tracking
python3 scripts/deep-current.py list
python3 scripts/deep-current.py show carnivore
python3 scripts/deep-current.py digest
```

## CLI Reference

| 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) |

Thread IDs are auto-generated slugs. Prefix matching works (`carn` matches `carnivore-diet-research`).

## Agent Integration

### OpenClaw

Install from [ClawHub](https://clawhub.ai/madebydia/deep-current):

```bash
openclaw skills install deep-current
```

Schedule a nightly cron job that tells your agent to pick threads, research them with `web_search`/`web_fetch`, and write findings to `deep-current-reports/YYYY-MM-DD.md`. See [SKILL.md](SKILL.md) for the full cron prompt template.

### Other Agent Frameworks

The CLI is framework-agnostic. Any agent that can:

1. Run shell commands (to call the CLI)
2. Search the web (to do the actual research)
3. Write files (to output reports)

...can use Deep Current. Point your agent at the CLI, give it a prompt like "pick a thread, research it, write findings," and you're set.

### Manual / Script Use

It's just Python with no dependencies. Use it as a personal research tracker without any agent at all:

```bash
python3 scripts/deep-current.py add "Topic I'm curious about"
python3 scripts/deep-current.py note topic "Found an interesting paper on..."
python3 scripts/deep-current.py digest
```

## Data

Everything lives in `deep-current/currents.json` — a single JSON file. Back it up, version it, move it between machines. Reports go to `deep-current-reports/` as ind

_meta.json

{
  "ownerId": "kn711gxkbyw34qye3faaqw24yn8163fm",
  "slug": "deep-current",
  "version": "2.0.1",
  "publishedAt": 1780803101596
}

skill-card.md

## Description:

Deep Current helps agents maintain long-running research threads with a local Python CLI for topics, notes, sources, findings, and dated markdown reports.

This skill is ready for commercial/non-commercial use.

## Publisher:

[madebydia](https://clawhub.ai/user/madebydia)

### License/Terms of Use:

MIT

## Use Case:

External users and developers use this skill to track ongoing research topics, keep local research state, and guide an agent through scheduled or manual research digest workflows.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill maintains local research state and report files.

Mitigation: Use a workspace where deep-current/currents.json and deep-current-reports/ are acceptable write targets, and review generated reports before relying on them.

Risk: The optional nightly workflow can cause the agent to search the web and write reports on a schedule.

Mitigation: Review the cron prompt, selected model, timeout, and scheduling policy before enabling unattended runs.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/madebydia/skills/deep-current)
- [Project homepage](https://github.com/madebydia/deep-current)

## Skill Output:

**Output Type(s):** [text, markdown, shell commands, configuration, guidance]

**Output Format:** [CLI text output, JSON state files, and markdown research reports]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Writes local research state under deep-current/ and dated reports under deep-current-reports/ when configured by the user.]

## Skill Version(s):

2.0.1 (source: server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

LICENSE

MIT License

Copyright (c) 2026 Diana Park

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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