Derived from arXiv:2607.17331 - Agentic ERP: Multi-Agent Large Language Model Architecture for Autonomous Enterprise Resource Planning
Scanned 9/11/2026
Install to Claude Code
Skills Directorynpx -y skills add hiyenwong/ai_collection --skill agentic-erp-multi-agent-large-language-model-archi --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Agentic Erp Multi Agent Large Language Model Archi?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/hiyenwong-agentic-erp-multi-agent-large-language-model-archi)More formats (shields.io, HTML) on the badges page.
# Agentic ERP: Multi-Agent Large Language Model Architecture for Autonomous Enterprise Resource Planning
Derived from arXiv:2607.17331 - Agentic ERP: Multi-Agent Large Language Model Architecture for Autonomous Enterprise Resource Planning
## Core Concept
Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries. This paper presents Agentic ERP, an expert-system architecture that combines role-aligned large-language-model (LLM) agents with a risk-tiered human-in-the-loop harness and a graph-base...
## Key Insights
- Derived from arXiv:2607.17331
- Published: 2026-07-19
- Utility Score: 1.00
- Authors: Zhihao Liu, Tianyu Wang, Xi Vincent Wang et al.
## Activation
agentic-erp-multi-agent-large-language-model-archi, 2607.17331
## References
- arXiv: https://arxiv.org/abs/2607.17331
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.
Recovers prior coding-agent session context by running `catchup <agent> --since-compact`, which extracts a clean summary of a previous Codex, Claude Code, Antigravity, OpenCode, or Pi Agent session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", or asks to recover/summarize a previous session before continuing. Do NOT use for the current conversation, git history, or any non-agent log.
A comprehensive mathematical reasoning skill for AI assistants — handles arithmetic to research-level problems with rigorous step-by-step reasoning, systematic verification, and transparent uncertainty handling
