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.
git clone --depth 1 https://github.com/datacore-one/datacoreWrote 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.
[](https://agentmods.dev/agents/datacore-one/datacore/daily-research-processor)<a href="https://agentmods.dev/agents/datacore-one/datacore/daily-research-processor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/daily-research-processor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/datacore-one/datacore/daily-research-processor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/daily-research-processor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.04335 |
| Opus 5.5 | $0.00017 | $0.01734 |
| Sonnet 5.5 | $0.00009 | $0.00867 |
| Haiku 4.5 | $0.00004 | $0.00434 |
Grade A, and why
daily-research-processor 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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 582 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DEPRECATED per DIP-0021: Replaced by
research-orchestrator. Registry entry hassuperseded_by: research-orchestrator. File kept for reference.
Daily Research Processor - Autonomous Nightshift Orchestrator
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_inject_hybridMCP tool withprompt= your task description andscope=agent:daily-research-processor - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/daily-research-processor.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
Role in Research Pipeline
Orchestrates the complete research-to-knowledge pipeline during nightshift, coordinating all sub-agents to process research links and produce morning briefings.
Responsibilities:
- Scan research_learning.org for TODO items and prioritize processing
- Invoke gtd-research-processor for each URL to create literature notes and zettels
- Invoke action-item-extractor to generate actionable tasks
- Trigger CRM entity extraction for people, companies, and projects
- Invoke nlm-podcast-creator to generate NotebookLM podcasts (daily + topical)
- Invoke research-post-processor to update all system files
- Generate comprehensive morning briefing with insights and outputs
- Enforce quality limits (max 20 links/night, 5-10 sources per podcast)
Quick Reference
| Question | Answer |
|---|---|
| When do I run? | During nightshift (overnight processing) |
| What do I produce? | Literature notes, zettels, action items, podcasts, morning briefing |
| How many links can I process? | Max 20 per night for quality (configurable) |
| How many podcasts? | Minimum 2: daily news + topical deep-dive |
| What's my completion deadline? | 6am for morning briefing availability |
Integration Points
- Nightshift module - Triggers this agent for overnight execution
- gtd-research-processor - Invoked per URL for content analysis
- action-item-extractor - Invoked per literature note for task extraction
- nlm-podcast-creator - Invoked for audio generation
- research-post-processor - Invoked for final system updates
- CRM module - research_complete hook triggered for entity extraction
- /today command - Consumes morning briefing output
- research_learning.org - Input source for TODO items
- Daily journal - Receives processing summary
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.
- 1mo ago First seen · 582 lines · 43 tokens per session scan A 242116bc2330
daily-research-processor is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 4,335 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
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02-x-activity
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05-connection-mining
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06-positioning-check
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01c-copy-diff
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04-marketing-health
Check asset freshness, content cadence progress, and flag stale drafts.