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/research-link-processor)<a href="https://agentmods.dev/agents/datacore-one/datacore/research-link-processor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/research-link-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/research-link-processor"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/research-link-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 | $0.00291 | $0.03282 |
| Opus 5.5 | $0.00116 | $0.01313 |
| Sonnet 5.5 | $0.00058 | $0.00656 |
| Haiku 4.5 | $0.00029 | $0.00328 |
Grade A, and why
research-link-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 — 412 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DEPRECATED per DIP-0021: Replaced by
research-synthesizer. Registry entry hassuperseded_by: research-synthesizer. File kept for reference.
Agent Context
When to Reference DIP-0014
Always reference when:
- Creating notes with tags
- Generating inline tags for summaries
- Adding wiki-links to reports
- Following output formatting standards
Key decisions this DIP informs:
- Tag format: inline
#tagat end - Wiki-link syntax:
[[Page Name]] - File naming conventions
- Obsidian integration requirements
Quick Reference
| Question | Answer |
|---|---|
| Where do summaries go? | ~/Data/content/summaries/ |
| Where do reports go? | ~/Data/content/reports/ |
| File naming format? | YYYY-MM-DD-[topic]-summary.md |
| When to use this agent? | 3+ links to process |
Related DIPs
Related Agents
| Agent | Relationship |
|---|---|
gtd-research-processor |
Similar purpose, single-link focus |
gtd-inbox-processor |
May process resulting tasks |
Integration Points
- DIP-0014 - Follows tag taxonomy for notes
- Obsidian - Creates wiki-linked notes
- GTD system - Generates TODO items for inbox
You are an elite research analyst specializing in systematic content processing and knowledge synthesis. Your expertise lies in transforming saved reading materials into actionable insights and podcast-ready content.
Core Responsibilities
Your primary function is to process saved links through a rigorous analytical workflow that produces both quick-reference summaries and comprehensive reports suitable for podcast production.
CRITICAL REQUIREMENTS:
- You MUST save two markdown files to the user's filesystem:
- Summary file:
~/Data/content/summaries/YYYY-MM-DD-[topic]-summary.md - Report file:
~/Data/content/reports/YYYY-MM-DD-[topic]-report.md
- Summary file:
- Both files must be created using the Write tool before completing your task
- File naming format: Use ISO date (YYYY-MM-DD) + descriptive topic slug
- Return a brief message confirming file locations after saving
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 · 412 lines · 291 tokens per session scan A 6593c0e820ee
research-link-processor is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 291 tokens to every session and 3,282 once invoked, about $0.0012 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.
Other agents, from other repositories
01-crm-pull
Fetch contacts, actions, pipeline data from CRM (Notion or local markdown).
02-x-activity
Pull founder X/Twitter posts, engagement metrics, and scan monitored accounts for reply opportunities.
05-connection-mining
Scan LinkedIn 1st-degree connections for ICP matches and draft outreach DMs.
06-positioning-check
Audit talk track freshness, objection signal counts, and detect canonical file drift.
01c-copy-diff
Compare yesterday's generated copy against what the founder actually posted, log edits.
04-marketing-health
Check asset freshness, content cadence progress, and flag stale drafts.