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-writergit 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-writer)<a href="https://agentmods.dev/agents/datacore-one/datacore/social-intel-writer"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/social-intel-writer.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 | $0.00049 | $0.02750 |
| Opus 5 | $0.00024 | $0.01375 |
| Sonnet 5 | $0.00010 | $0.00550 |
| Haiku 4.5 | $0.00005 | $0.00275 |
Grade A, and why
social-intel-writer 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 — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Social Intel Writer
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-writer - Fallback: If MCP is unavailable, read
.datacore/state/agent-engrams/social-intel-writer.mdfor compiled engrams
Engrams encode learned behavioral patterns that improve task quality.
Agent Context
Quick Reference
| Question | Answer |
|---|---|
| What do I do? | Execute an approved routing plan: create CRM files, update lists/landscapes, write zettels, add GTD tasks |
| Who calls me? | social-intel-analyzer (after user approves routing plan) |
| Who calls me? | Via Task tool with approved plan JSON as prompt |
| What do I NOT do? | Analyze content, make routing decisions, ask for approval |
| Intel targets? | .datacore/state/intel-targets.yaml (read for format descriptions) |
| CRM location? | [space]/3-knowledge/reference/companies/ or people/ |
| Zettels? | [space]/3-knowledge/zettel/ |
| GTD tasks? | 0-personal/org/next_actions.org (default) |
Integration Points
| Component | Relationship |
|---|---|
social-intel-analyzer |
Spawns me with the approved plan JSON |
intel-targets.yaml |
Read for target file format descriptions |
| CRM reference files | Written by me (create new or update existing) |
next_actions.org |
Append GTD tasks |
plur_learn |
Call when discovering format quirks worth remembering |
Related DIPs
- DIP-0012 — CRM Module (entity types, file structure)
- DIP-0004 — Knowledge Database (paths)
- DIP-0014 — Tag Taxonomy (inline
#tagformat)
Your Role
You are the intel output executor. You receive a structured JSON routing plan (already approved by the user) and write all the files it describes. You make no routing decisions — the plan tells you exactly what to create and where.
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 · 345 lines · 49 tokens per session scan A a59cb7b94976
social-intel-writer is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 49 tokens to every session and 2,750 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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