From agentic-skills
A fundamental pattern where the output of one LLM call serves as the input for the next, enabling complex tasks to be broken down into manageable sequential steps. Use when user asks to "chain prompts together", "multi-step prompts", "prompt pipeline", or mentions sequential prompts, prompt workflows, or chain-of-thought.
npx claudepluginhub lauraflorentin/skills-marketplace --plugin agentic-skillsThis skill uses the workspace's default tool permissions.
Prompt Chaining is the practice of decomposing a complex task into a series of smaller, sequential sub-tasks. Each sub-task is handled by a specific LLM call, with the output of one step feeding into the next. This approach improves reliability, testability, and allows for intermediate processing (like validation or formatting) between steps.
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Migrates code, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to Opus 4.5, updating model strings on Anthropic, AWS, GCP, Azure platforms.
Analyzes BMad project state from catalog CSV, configs, artifacts, and query to recommend next skills or answer questions. Useful for help requests, 'what next', or starting BMad.
Prompt Chaining is the practice of decomposing a complex task into a series of smaller, sequential sub-tasks. Each sub-task is handled by a specific LLM call, with the output of one step feeding into the next. This approach improves reliability, testability, and allows for intermediate processing (like validation or formatting) between steps.
def prompt_chain_workflow(input_data):
# Step 1: Extraction
# Focuses solely on getting the right data out of the raw input.
extracted_data = llm_call(
prompt="Extract key entities from this text...",
input=input_data
)
# Optional: Deterministic Validation
# We can run code check here before proceeding.
if not validate(extracted_data):
raise ValueError("Extraction failed")
# Step 2: Transformation
# Focuses on converting the data into the desired format/style.
final_output = llm_call(
prompt="Transform this extraction into a marketing summary...",
input=extracted_data
)
return final_output