LongCat-Flash-Thinking-2601

🥈Silver

LongCat-Flash-Thinking-2601 automates complex decision-making for operations teams. It connects to Claude agents to process large datasets, identify patterns, and generate actionable insights. Ideal for supply chain optimization, demand forecasting, and process automation.

24690Updated Today

Intermediate30min to implementautomation

Saves ~300 min per use

Quick InstallView Source

git clone https://github.com/meituan-longcat/LongCat-Flash-Thinking-2601.git

Works with:

Claude

Overview

About This Skill

How to Use

1. **Prepare your dataset**: Export your operational data (CSV/Excel) with all relevant metrics (e.g., inventory levels, lead times, demand forecasts). Ensure columns are properly labeled with units of measure. 2. **Define your constraints**: Clearly specify any limitations (e.g., 'budget cannot exceed $50K', 'must maintain 95% service level'). These guide the prioritization logic. 3. **Select your key metric**: Choose whether to optimize for cost reduction, service level improvement, or risk mitigation. This shapes the recommendation algorithm. 4. **Review and refine**: LongCat-Flash-Thinking-2601 will generate 5-10 recommendations. Use the impact scores to shortlist candidates, then validate assumptions with your team before implementation. 5. **Iterate**: After implementing changes, feed new data back into the system to refine future recommendations. The model learns from actual outcomes to improve accuracy.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/meituan-longcat/LongCat-Flash-Thinking-2601

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Use LongCat-Flash-Thinking-2601 to analyze [DATASET_DESCRIPTION] and generate a prioritized list of [DECISION_TYPE] recommendations. Focus on [KEY_METRIC] while considering [CONSTRAINTS]. Provide the top 5 actions with estimated impact scores (1-100) and implementation timelines.

Example Output

Based on the analysis of 12,478 supply chain transactions from Q4 2023, LongCat-Flash-Thinking-2601 identified critical bottlenecks in our just-in-time inventory system. The top 5 recommendations are:

1. **Reallocate 15% of safety stock** from Warehouse B to Warehouse D (Impact: 92, Timeline: 2 weeks) - This would reduce stockouts in high-demand regions by 40% while cutting carrying costs by $180K annually.

2. **Implement dynamic reorder points** for SKU-456789 (Impact: 88, Timeline: 1 week) - Adjusting reorder triggers based on real-time demand patterns could save $120K in excess inventory.

3. **Negotiate expedited shipping** with Supplier X for critical components (Impact: 85, Timeline: 3 days) - Reducing lead time from 7 to 3 days would prevent $2.1M in potential lost sales during peak season.

4. **Automate PO generation** for low-value items (Impact: 82, Timeline: 4 weeks) - This would free up 12 hours/week of procurement staff time while maintaining 99.5% fill rates.

5. **Create a supplier risk scorecard** (Impact: 79, Timeline: 2 weeks) - Proactively identifying at-risk suppliers could prevent $850K in disruption costs.

The analysis revealed that 68% of our current inefficiencies stem from static inventory policies that don't account for seasonal demand spikes. The system also flagged that our current safety stock levels are 23% higher than necessary for 80% of our SKUs.