` | 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 </article></body></html>

--- 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] [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 ` | Full thread details | | `add ` | 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 </article></body></html>

Deep Current

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