research-daily

research-daily is a command for coding agents from datacore-one/datacore. It costs 8 tokens per session (1,381 once invoked), scanned A, original, MIT.

A command that manually runs the daily research-processing workflow. It processes items from research_learning.org, a file or system that stores research tasks and links.

In plain words
What is it for?
Use it to process all items or a selected section or batch, choose whether to create podcasts, and handle failed URLs.
Why use it?
It lets you process queued research immediately when you do not want to wait for the overnight run.

Command

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/datacore-one/datacore/research-daily
Clone the repo
git clone --depth 1 https://github.com/datacore-one/datacore

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for research-daily

README.md
[![agentmods](https://agentmods.dev/badge/commands/datacore-one/datacore/research-daily.svg)](https://agentmods.dev/commands/datacore-one/datacore/research-daily)
Your own site
<a href="https://agentmods.dev/commands/datacore-one/datacore/research-daily"><img src="https://agentmods.dev/badge/commands/datacore-one/datacore/research-daily.svg" alt="Measured on agentmods" height="20"></a>
Per session 8 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,381 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00008 $0.01381
Opus 5 $0.00004 $0.00691
Sonnet 5 $0.00002 $0.00276
Haiku 4.5 $0.00001 $0.00138

Measured yesterday against content hash 57212d3e2580, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

research-daily scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.datacore/modules/research/commands/research-daily.md · 238 lines

How it starts

The opening of the file, as written. The whole thing — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/research-daily

Command Context

When to Reference Research Module

Always reference when:

  • User wants immediate processing instead of waiting for nightshift
  • Testing the research pipeline during development
  • Processing urgent research that can't wait until overnight
  • Need to clear research queue before end of day
  • Troubleshooting research processing issues

Key decisions this command informs:

  • Whether to process all items or specific section/batch
  • Whether to generate podcasts immediately or skip for nightshift
  • Whether to extract action items and update CRM during processing
  • How to handle URL failures (retry, skip, mark cancelled)

Quick Reference

Question Answer
What does it process? TODO items from research_learning.org (all, by section, or limited batch)
Does it generate podcasts? Configurable - can skip if nlm unavailable
What's the processing limit? max_sources_per_night setting (default 20)
What outputs are created? Literature notes, zettels, action items, journal updates, landscape entries

Agents This Command Invokes

Agent Purpose
research-orchestrator Orchestrates entire processing pipeline with user-specified scope and options

Integration Points

  • research-orchestrator agent - Invoked with processing scope (all/section/limited)
  • research_learning.org - Input source scanned for TODO items
  • User confirmation - Interactive prompts for scope and settings
  • Processing summary - Results presented with follow-up action suggestions
  • nlm availability - Checks if podcast generation is possible
  • Module settings - Respects action_extraction, post_processing, max_links settings

Manually trigger daily research processing outside of nightshift.

When to Use

  • You want to process research links immediately (not wait for nightshift)
  • Testing the research pipeline
  • Processing a specific batch of links

Workflow

Read the full file on GitHub · 238 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 238 lines · 8 tokens per session scan A 57212d3e2580

Subscribe to this mod's changes

research-daily is a command published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 8 tokens to every session and 1,381 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.