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/commands/datacore-one/datacore/intel)<a href="https://agentmods.dev/commands/datacore-one/datacore/intel"><img src="https://agentmods.dev/badge/commands/datacore-one/datacore/intel/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/commands/datacore-one/datacore/intel"><img src="https://agentmods.dev/badge/commands/datacore-one/datacore/intel.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 | $0.00029 | $0.00991 |
| Opus 5.5 | $0.00012 | $0.00396 |
| Sonnet 5.5 | $0.00006 | $0.00198 |
| Haiku 4.5 | $0.00003 | $0.00099 |
Grade A, and why
intel 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/intel Command
Command Context
Agents This Command Invokes
| Agent | Purpose |
|---|---|
knowledge-extractor |
Fetches URL and creates a literature note |
social-intel-analyzer |
Analyzes literature note, proposes routing plan, gets user approval |
social-intel-writer |
Creates outputs (spawned by analyzer after approval) |
Integration Points
- knowledge-extractor — content acquisition layer
- social-intel-analyzer — entity extraction, insight routing
- CRM / 3-knowledge/ — output destinations
Social media intelligence: extract content from X posts or YouTube videos, analyze entities and insights, route to knowledge base and CRM.
Usage
/intel <url>
/intel <url> --deep
Arguments:
| Argument | Description |
|---|---|
<url> |
X post URL (x.com/*/status/* or twitter.com/*/status/*) or YouTube URL (youtube.com/watch?v=* or youtu.be/*) |
--deep |
Enable deep analysis mode (multi-hop entity expansion) |
Workflow
Step 1: Parse Input
Extract the URL from $ARGUMENTS. Check for the --deep flag and set depth accordingly:
--deeppresent → depth =deep- No flag → depth =
1-hop
Step 2: Validate URL
Accept only:
- X / Twitter:
x.com/*/status/*ortwitter.com/*/status/* - YouTube:
youtube.com/watch?v=*oryoutu.be/*
If the URL does not match either pattern, show this error and stop:
This command handles X posts and YouTube videos.
For other URLs, use the knowledge-extractor directly.
If no URL is present in the arguments, ask the user to provide one.
Step 3: Check for Existing Literature Note
Call datacore.search with the URL as the query to check if content was already extracted.
- If a matching literature note is found: use it directly (skip Step 4).
- If not found: proceed to Step 4.
Step 4: Acquire Content
Spawn the knowledge-extractor agent with the URL. Wait for it to create the literature note and return its path.
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 · 138 lines · 29 tokens per session scan A feb6fe035298
intel is a command published in the GitHub repository datacore-one/datacore (4 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 991 once invoked, about $0.0001 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 2026-09-03.
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