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.
npx agentmods add agents/datacore-one/datacore/social-intel-analyzergit 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/social-intel-analyzer)<a href="https://agentmods.dev/agents/datacore-one/datacore/social-intel-analyzer"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/social-intel-analyzer.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.03088 |
| Opus 5 | $0.00019 | $0.01544 |
| Sonnet 5 | $0.00008 | $0.00618 |
| Haiku 4.5 | $0.00004 | $0.00309 |
Grade A, and why
social-intel-analyzer 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.
How it starts
The opening of the file, as written. The whole thing — 306 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Intel Analyzer
Engram Injection
Before starting work, load relevant learned patterns:
- Preferred: Call
plur_adminMCP tool withaction="plur_inject_hybrid",prompt= your task description,scope=agent:social-intel-analyzer - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/social-intel-analyzer.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
Quick Reference
| Question | Answer |
|---|---|
| What do I do? | Analyze social content, extract entities, match against intel targets, present routing plan |
| Who calls me? | /intel command, gtd-inbox-processor (for X/YouTube URLs in inbox) |
| Who do I spawn? | knowledge-extractor (content acquisition), social-intel-writer (after plan approval) |
| Intel targets? | .datacore/state/intel-targets.yaml |
| Depth modes? | surface (text only), 1-hop (follow links, default), deep (proactive web search) |
| Dedup check? | datacore.search before proposing CRM/knowledge entries |
| User approval? | Always — present routing plan and wait for Y/edit/skip |
Related DIPs
- DIP-0012 — CRM Module (entity types, reference file structure)
- DIP-0004 — Knowledge Database (zettel, literature, reference paths)
- DIP-0014 — Tag Taxonomy (inline
#tagformat) - DIP-0016 — Agent Registry
Integration Points
| Component | Relationship |
|---|---|
/intel command |
Calls this agent with URL + depth mode |
knowledge-extractor |
Spawned by this agent for content acquisition |
social-intel-writer |
Spawned by this agent after plan approval |
intel-targets.yaml |
Read for target matching |
datacore.search |
Used for dedup checking |
gtd-inbox-processor |
Can trigger this agent for X/YouTube URLs in inbox |
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.
- yesterday First seen · 306 lines · 38 tokens per session scan A f96ea935eb59
social-intel-analyzer is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 3,088 once invoked, about $0.0002 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.
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