Reading
A pre-check routing skill set designed for OpenAI Codex. By evaluating task complexity, model tier, reasoning intensity, file scope, and execution strategy, it helps users make optimal decisions before Codex starts reading files or modifying code, significantly reducing token waste.
Section 01
A pre-check routing skill set designed for OpenAI Codex. By evaluating task complexity, model tier, reasoning intensity, file scope, and execution strategy, it helps users make optimal decisions before Codex starts reading files or modifying code, significantly reducing token waste.
Section 02
Section 03
With the popularity of AI programming assistants like OpenAI Codex, Claude Code, and GitHub Copilot, developers have enjoyed unprecedented improvements in coding efficiency. However, behind this efficiency lies a cost issue—each time an AI assistant reads files, analyzes code, or generates suggestions, it consumes a large number of tokens, which translate directly to real costs.
Common mistakes made by new users include: letting AI handle large projects directly leading to token explosion, using high-cost models for small tasks, scanning the entire codebase right away, and performing high-risk operations without first conducting read-only analysis. The root cause of these problems is the lack of a mechanism to pre-judge task complexity and cost risks.
Section 04
Codex Cost Router Skills is a pre-check routing skill system designed for OpenAI Codex. Its core idea is simple: before Codex starts reading files or modifying code, evaluate the task first to help users make optimal decisions on model selection, permission configuration, and execution strategy.
This system provides decision recommendations by analyzing multiple dimensions of the task:
Section 05
The project currently offers two main versions, targeting different user groups:
Section 06
This is the basic version, mainly for official GPT users and beginners. It features complete output and simple, stable logic, suitable for users who are just starting to use Codex to build cost awareness.
Section 07
This is the advanced version, targeting provider-aware users with multi-model and multi-interface needs. It supports L0-L4 model tier classification and uses compact output mode by default, making it more suitable for experienced developers to perform refined cost control.
Both versions follow the same design philosophy: provide clear cost optimization suggestions without increasing the user's cognitive burden.
Section 08
The workflow of Cost Router can be divided into three stages:
First Stage: Task Analysis When a user makes a request, the skill first analyzes the nature of the task. Is it fixing a simple bug? Or refactoring an entire module? Does it need to understand business logic? Or is it purely technical implementation? This classification directly affects subsequent decisions.
Second Stage: Cost Modeling Based on the task analysis results, the skill estimates the cost of different execution plans. This includes estimating the number of files to read, lines of code, expected output length, and token consumption corresponding to different model choices.
Third Stage: Strategy Recommendation Synthesizing task characteristics and cost models, the skill provides specific execution suggestions. For example: "It is recommended to use a lightweight model for preliminary analysis, expected to consume 500 tokens; after confirming the plan, use the flagship model to generate code, which is expected to save 60% of costs."
Keep going with more reads from the same topic.
Nornir MCP Server is an enterprise-level server based on the Model Context Protocol (MCP). It seamlessly integrates large language models (such as Claude) with the Nornir network automation framework, supporting natural language orchestration for multi-vendor network devices (Cisco, Arista, Juniper, etc.), and providing production-grade features like a dual-engine architecture (NAPALM + Netmiko), intelligent filtering, and a secure sandbox.
Bibliothèque Française LLM is a structured indexing and annotation project for French public domain literature designed specifically for large language models (LLMs). It integrates multiple authoritative sources such as DraCor, Common Corpus, and Wikisource, providing metadata indexing categorized by genre, author, and era, as well as in-depth annotations for dramatic texts (including characters, lines, stage directions, etc.). Its aim is to enable LLMs to efficiently read and understand classic French literary works.
Splinter is a minimalist, high-performance key-value (KV) and vector storage system enabling zero-latency inter-process communication via shared memory and atomic operations. With only 766 lines of core code, it supports millions of operations per second and 768-dimensional vector storage, offering a new architectural approach for local LLM inference and data-intensive applications.
A complete cloud-native AI application development guide for beginners, building a simple generative AI chatbot using Amazon EC2, Apache, Python CGI, and Amazon Bedrock, covering architecture design, IAM permission configuration, security best practices, and cost optimization suggestions.