langchain-core-workflow-a

Build LangChain chains and prompts for structured LLM workflows. Use when creating prompt templates, building LCEL chains, or implementing sequential processing pipelines. Trigger with phrases like "langchain chains", "langchain prompts", "LCEL workflow", "langchain pipeline", "prompt template".

Install with Tessl CLI

npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill langchain-core-workflow-a

What are skills?

Review — 87%

Does it follow best practices?

If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:

npx tessl skill review --optimize ./path/to/skill

Learn more

Validation — 12 / 16 Passed

Validation for skill structure

SKILL.md

Review

Evals

LangChain Core Workflow A: Chains & Prompts

Overview

Build production-ready chains using LangChain Expression Language (LCEL) with prompt templates, output parsers, and composition patterns.

Prerequisites

Completed langchain-install-auth setup
Understanding of prompt engineering basics
Familiarity with Python type hints

Instructions

Step 1: Create Prompt Templates

from langchain_core.prompts import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
    MessagesPlaceholder
)

# Simple template
simple_prompt = ChatPromptTemplate.from_template(
    "Translate '{text}' to {language}"
)

# Chat-style template
chat_prompt = ChatPromptTemplate.from_messages([
    SystemMessagePromptTemplate.from_template(
        "You are a {role}. Respond in {style} style."
    ),
    MessagesPlaceholder(variable_name="history", optional=True),
    HumanMessagePromptTemplate.from_template("{input}")
])

Step 2: Build LCEL Chains

from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser, JsonOutputParser

llm = ChatOpenAI(model="gpt-4o-mini")

# Basic chain: prompt -> llm -> parser
basic_chain = simple_prompt | llm | StrOutputParser()

# Invoke the chain
result = basic_chain.invoke({
    "text": "Hello, world!",
    "language": "Spanish"
})
print(result)  # "Hola, mundo!"

Step 3: Chain Composition

from langchain_core.runnables import RunnablePassthrough, RunnableParallel

# Sequential chain
chain1 = prompt1 | llm | StrOutputParser()
chain2 = prompt2 | llm | StrOutputParser()

sequential = chain1 | (lambda x: {"summary": x}) | chain2

# Parallel execution
parallel = RunnableParallel(
    summary=prompt1 | llm | StrOutputParser(),
    keywords=prompt2 | llm | StrOutputParser(),
    sentiment=prompt3 | llm | StrOutputParser()
)

results = parallel.invoke({"text": "Your input text"})
# Returns: {"summary": "...", "keywords": "...", "sentiment": "..."}

Step 4: Branching Logic

from langchain_core.runnables import RunnableBranch

# Conditional branching
branch = RunnableBranch(
    (lambda x: x["type"] == "question", question_chain),
    (lambda x: x["type"] == "command", command_chain),
    default_chain  # Fallback
)

result = branch.invoke({"type": "question", "input": "What is AI?"})

Output

Reusable prompt templates with variable substitution
Type-safe LCEL chains with clear data flow
Composable chain patterns (sequential, parallel, branching)
Consistent output parsing

Examples

Multi-Step Processing Chain

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(model="gpt-4o-mini")

# Step 1: Extract key points
extract_prompt = ChatPromptTemplate.from_template(
    "Extract 3 key points from: {text}"
)

# Step 2: Summarize
summarize_prompt = ChatPromptTemplate.from_template(
    "Create a one-sentence summary from these points: {points}"
)

# Compose the chain
chain = (
    {"points": extract_prompt | llm | StrOutputParser()}
    | summarize_prompt
    | llm
    | StrOutputParser()
)

summary = chain.invoke({"text": "Long article text here..."})

With Context Injection

from langchain_core.runnables import RunnablePassthrough

def get_context(input_dict):
    """Fetch relevant context from database."""
    return f"Context for: {input_dict['query']}"

chain = (
    RunnablePassthrough.assign(context=get_context)
    | prompt
    | llm
    | StrOutputParser()
)

result = chain.invoke({"query": "user question"})

Error Handling

Error	Cause	Solution
Missing Variable	Template variable not provided	Check input dict keys match template
Type Error	Wrong input type	Ensure inputs match expected schema
Parse Error	Output doesn't match parser	Use more specific prompts or fallback

Resources

Next Steps

Proceed to langchain-core-workflow-b for agents and tools workflow.

Repository: jeremylongshore/claude-code-plugins-plus-skills
Commit: 22fc789
Last updated: about 1 month ago
Created: about 1 month ago

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.