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Langchain Langgraph Coding Assistant

Listed
5zjk5·vlatest·agents

Generates, debugs, and optimizes Python code using LangChain and LangGraph for complex AI workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add 5zjk5/prompt-engineering --skill langchain-langgraph-coding-assistant -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

5zjk5

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 6 hosts that are neither well-known infrastructure nor declared in the skill's frontmatter: api-inference.modelscope.cn, dashscope.aliyuncs.com, docs.langchain.com, llm-east-us2-test.openai.azure.com, open.bigmodel.cn….

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    open.bigmodel.cnundeclareddocs.langchain.comundeclaredgithub.comsource hostpython.langchain.comundeclaredapi.openai.commodel providerllm-east-us2-test.openai.azure.comundeclaredplatform.openai.comdocumentationdashscope.aliyuncs.comundeclaredapi-inference.modelscope.cnundeclared

    What it found (3 of 28)

    • Skill/lang_chain&graphy_skill/resources/MCP/调用本地工具/use_tool_1.py:12contacts open.bigmodel.cn
      openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
    • Skill/lang_chain&graphy_skill/resources/MCP/调用远程mcp服务/use_mcp_tool_1.py:12contacts open.bigmodel.cn
      openai_api_base="https://open.bigmodel.cn/api/paas/v4/"
    • Skill/lang_chain&graphy_skill/resources/MCP/调用远程mcp服务/use_mcp_tool_2.py:14contacts open.bigmodel.cn
      openai_api_base="https://open.bigmodel.cn/api/paas/v4/"

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned 5zjk5/prompt-engineering at e563dd1c97.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
e563dd1c97
Manifest
Skill/lang_chain&graphy_skill/SKILL.md
Scanned
61 files, 4,966 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill leverages LangGraph and LangChain to assist with coding tasks. It can generate code snippets, debug existing code, and explain complex code blocks in plain language. The assistant is designed to understand the context of a project and provide relevant solutions based on that understanding.

When to use it

  • Debugging: You're stuck on an error and need help identifying the root cause within a larger codebase.
  • Code Generation: You want to quickly generate boilerplate code for a specific function or class in Python, Javascript, or other supported languages.
  • Code Explanation: You’ve inherited a complex piece of code and need it explained line-by-line to understand its functionality.
  • Refactoring Assistance: You're looking to improve the efficiency or readability of existing code and want suggestions for refactoring.

Key capabilities

  • Code generation in multiple languages (Python, Javascript)
  • Debugging assistance with error identification
  • Code explanation using natural language
  • Contextual understanding of project codebase
  • Refactoring suggestions

Example prompts

  • "Debug this Python code: [paste code snippet]"
  • "Generate a JavaScript function to sort an array of objects by date."
  • "Explain what this code does line-by-line: [paste code block]"
  • “Suggest ways to refactor this python class for better readability”

Tips & gotchas

  • The assistant performs best with well-formatted and commented code. Providing clear context about the project is also beneficial.