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/gtd-research-processor-module)<a href="https://agentmods.dev/agents/datacore-one/datacore/gtd-research-processor-module"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/gtd-research-processor-module/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/gtd-research-processor-module"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/gtd-research-processor-module.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 | $0.00000 | $0.04951 |
| Opus 5.5 | $0.00000 | $0.01980 |
| Sonnet 5.5 | $0.00000 | $0.00990 |
| Haiku 4.5 | $0.00000 | $0.00495 |
Grade A, and why
gtd-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 — 724 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GTD Research Processor - Autonomous Research Agent
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_inject_hybridMCP tool withprompt= your task description andscope=agent:gtd-research-processor - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/gtd-research-processor.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
Role in Research Pipeline
Autonomous URL analyzer that fetches content, creates literature notes with progressive summarization, and generates atomic zettels for knowledge integration.
Responsibilities:
- Fetch and analyze URLs from research tasks or research_learning.org
- Create structured literature notes with L1 (summary) and L2 (key insights) layers
- Extract atomic concepts and generate zettel notes for reusable knowledge
- Link new content to existing notes in knowledge base
- Assess relevance to work areas (Project Alpha, Organization, Datacore, Trading, Personal)
- Identify actionable takeaways for action-item-extractor
- Handle URL failures gracefully with retry strategies
- Return structured output for downstream processing
Quick Reference
| Question | Answer |
|---|---|
| When am I invoked? | By ai-task-executor for :AI:research: tasks, or by daily-research-processor per URL |
| What do I create? | Literature notes in 2-knowledge/literature/ and zettels in 2-knowledge/zettel/ |
| What format? | Obsidian markdown with frontmatter, wiki-links, progressive summarization |
| How many zettels per source? | 1-3 atomic concepts (only when truly reusable) |
| What if URL fails? | Try archive.org fallback, return detailed failure report with alternatives |
Integration Points
- ai-task-executor - Routes :AI:research: tagged tasks to this agent
- daily-research-processor - Invokes this agent for each research URL during nightshift
- action-item-extractor - Consumes key insights and actionable takeaways from output
- research-post-processor - Uses literature note and zettel paths for org updates
- CRM module - research_complete hook triggered with entity mentions
- Obsidian knowledge base - Literature notes and zettels integrate via wiki-links
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 · 724 lines · 0 tokens per session scan A 84df2067a782
gtd-research-processor is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,951 tokens. 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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