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Llm Application Dev Langchain Agent

Listed
sickn33·vlatest·ai-ml·MIT

Helps with LLMs, LangChain as part of building AI and machine learning applications 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 sickn33/agentic-awesome-skills --skill llm-application-dev-langchain-agent -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

sickn33

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.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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 sickn33/antigravity-awesome-skills at 5a4cdd9df8.

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

Commit
5a4cdd9df8
Manifest
skills/llm-application-dev-langchain-agent/SKILL.md
Scanned
0 files
Copies in repo
3 — all of them scanned
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.
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About this skill

What it does

This skill enables AI agents to develop and deploy LLM applications using LangChain, a powerful framework for chaining language model calls. It automates the creation of complex agent workflows by managing prompts, memory, and tool integrations directly within the application code.

When to use it

  • Building autonomous agents that can perform multi-step reasoning tasks independently.
  • Creating chatbots or assistants that interact with external APIs and databases via custom tools.
  • Prototyping rapid LLM applications where you need dynamic prompt management and state retention.
  • Integrating LangChain-specific features like retrieval-augmented generation (RAG) pipelines into your agent logic.

Key capabilities

  • Orchestrates sequences of language model calls to handle complex tasks.
  • Manages conversation history and context through built-in memory modules.
  • Connects agents to external tools and APIs for extended functionality.
  • Supports custom prompt templates for fine-tuned agent behavior.

Example prompts

  • "Create a LangChain agent that monitors stock prices and sends alerts when thresholds are breached."
  • "Build an RAG-powered assistant that answers questions based on my uploaded PDF documents."
  • "Develop a multi-step agent that researches a topic, summarizes findings, and drafts a report."

Tips & gotchas

Ensure you have LangChain installed in your environment before deploying agents that rely on its specific modules. Complex chains may require careful tuning of temperature and max tokens to avoid hallucinations or excessive latency.