glm-coding

Integrate GLM-4.7 Coding Plan API with reasoning_content extraction and endpoint handling.

2|Updated Dec 28, 2025
One-click install
npx skills add https://github.com/leon30083/wenjing-aigc --skill glm-coding
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: glm-coding
Source: https://github.com/leon30083/wenjing-aigc/tree/main/.claude/skills/glm-coding
Command: npx skills add https://github.com/leon30083/wenjing-aigc --skill glm-coding

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill provides specialized knowledge for integrating and using GLM-4.7 Coding Plan API (智谱编程套餐). Use this skill when working with GLM-4.7 for prompt optimization, code generation, or technical documentation tasks. GLM-4.7 uses a unique response format with reasoning_content field and requires special endpoint handling.

Core Features & Use Cases

  • API integration guidance for GLM-4.7 Coding Plan
  • Handling non-standard response fields (reasoning_content)
  • End-to-end workflow examples including prompts, tests, and docs

Quick Start

Install dependencies and run the provided scripts to test GLM-4.7 Coding Plan API integration and perform a prompt optimization task.

Frequently Asked Questions about glm-coding

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I integrate GLM-4.7 Coding Plan API for code generation?

GLM-4.7 Coding Plan API integration requires constructing endpoints without the /v1 prefix and extracting code generation outputs from the reasoning_content field. End-to-end workflow examples including prompts, tests, and docs are provided to streamline reliable coding assistance.

Why does GLM-4.7 API integration return a non-standard response format?

GLM-4.7 API integration returns a non-standard response format because outputs are contained within a special reasoning_content field. Handling this specific field extraction is necessary to retrieve code generation results, prompt optimizations, and technical documentation data successfully.

How do I optimize prompts using the GLM-4.7 Coding Plan?

Prompt optimization with the GLM-4.7 Coding Plan involves sending targeted coding requests to the API and parsing the reasoning_content field. Provided scripts manage API key handling and endpoint construction to execute and test prompt optimization tasks.

Do I need the requests library to handle GLM-4.7 API endpoints?

Yes, the requests library is required as a dependency to handle GLM-4.7 API endpoints. It facilitates sending HTTP requests to the non-standard endpoint structure, managing API keys, and retrieving the reasoning_content payload for coding assistance.

What are the limitations of handling GLM-4.7 coding assistance responses?

Limitations of handling GLM-4.7 coding assistance responses include managing special endpoint construction without /v1 and extracting data specifically from the reasoning_content field. Error handling and performance considerations must be addressed to maintain reliable automation tools and code generators.

Can I use GLM-4.7 API integration for technical documentation tasks?

Yes, GLM-4.7 API integration applies to technical documentation tasks by leveraging code-related documentation capabilities. It handles endpoint construction and reasoning_content extraction to generate and manage documentation for developers building automation tools.