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Langchain

Checked
davila7·vlatest·ai-ml·MIT

Connects to external data sources & tools using LangChain's framework for enhanced AI capabilities and 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 davila7/claude-code-templates --skill langchain -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

davila7

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 passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • 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 undeclared network calls: passed

    Makes no network calls.

    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.

  • 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 davila7/claude-code-templates at 79182c51fb.

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

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/agents-langchain/SKILL.md
Scanned
4 files, 1,955 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

The langchain skill enables AI agents to interact with and utilize LangChain, a framework for building applications with LLMs. It allows for chaining together multiple language models, prompts, and data sources to create complex workflows.

When to use it

  • When you need to build modular chains of operations involving LLMs and external data sources.
  • To integrate multiple AI models into a single application or workflow.
  • For creating dynamic prompt templates that adapt based on user input or context.
  • When building applications that require memory, retrieval, or multi-step reasoning with LLMs.

Key capabilities

  • Integration with LangChain for advanced LLM workflows
  • Support for modular chaining of models and prompts
  • Ability to use external data sources in AI agent workflows

Example prompts

  • "Create a chain that uses an LLM to generate text and then stores it in a database."
  • "Build a prompt template that dynamically inserts user input into a query for an LLM."
  • "Set up a workflow where multiple models are chained together with memory support."

Tips & gotchas

  • Ensure LangChain is properly installed and configured before using this skill.
  • Be mindful of the complexity when chaining multiple models—keep workflows modular for easier debugging.