Declarative Prompt Chaining

via “multi-file prompt composition (skills system)”

Curated collection of 150+ ChatGPT prompt templates.

Unique: Treats prompt composition as a first-class database entity with versioning and metadata, rather than just concatenating prompts as strings. Enables Skills to be discovered, shared, and reused through the same community platform as individual prompts, creating a marketplace for complex reasoning patterns.

vs others: More discoverable and shareable than ad-hoc prompt chaining scripts because Skills are stored in the database with metadata, tags, and community ratings, making it easy to find and reuse complex workflows without reading source code.

via “prompt chain composition and orchestration”

LangGPT: Empowering everyone to become a prompt expert! 🚀 📌 结构化提示词（Structured Prompt）提出者 📌 元提示词（Meta-Prompt）发起者 📌 最流行的提示词落地范式 | Language of GPT The pioneering framework for structured & meta-prompt design 10,000+ ⭐ | Battle-tested by thousands of users worldwide Created by 云中江树

Unique: Enables composition of Role Templates into chains where output from one prompt feeds into the next, creating reusable multi-step reasoning pipelines, whereas most prompt frameworks treat individual prompts as isolated units

vs others: Allows prompt reuse across different chain compositions through structured template design, whereas traditional approaches require custom orchestration code for each chain variation

via “task decomposition and prompt chaining”

22 prompt engineering techniques with hands-on Jupyter Notebook tutorials, from fundamental concepts to advanced strategies for leveraging LLMs.

Unique: Provides Jupyter notebooks showing both task decomposition (breaking problems into sub-tasks) and prompt chaining (sequencing prompts with output passing). Includes LangChain integration patterns for orchestrating multi-step workflows, with examples of error handling and output validation between steps.

vs others: More comprehensive than generic workflow tutorials because it specifically addresses prompt-to-prompt chaining with concrete examples (research → outline → draft → edit) and shows how to structure outputs for downstream consumption.

via “prompt engineering and template management for rag synthesis”

Everything you need to know to build your own RAG application

Unique: Uses LangChain PromptTemplate for parameterized prompt construction with explicit variable injection, enabling prompt reuse and experimentation without string concatenation

vs others: More maintainable than string concatenation, and more flexible than hard-coded prompts because templates are reusable and variables are explicit

via “prompt chaining technique for decomposing complex tasks into sequential steps”

🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.

Unique: Explains prompt chaining as a foundational workflow pattern that complements other techniques (CoT, RAG, ReAct), showing how chaining enables more complex agent behaviors and task automation

vs others: More flexible than single-prompt approaches because it enables task decomposition and intermediate validation; simpler than full agent frameworks because it doesn't require tool integration or dynamic decision-making

via “workflow chains and connected prompts with execution orchestration”

f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.

Unique: Implements workflow chains as a declarative system where prompts are connected as nodes in a directed graph, with automatic state passing between steps. This enables complex reasoning patterns (like chain-of-thought) to be defined and reused without custom code.

vs others: More integrated than external workflow tools (like Zapier) because workflows are defined within the prompt library; more flexible than rigid prompt templates because workflows support branching and loops. Differs from general-purpose workflow engines by being specialized for prompt execution and reasoning chains.

via “nested prompt composition and multi-stage workflows”

Generative AI Scripting.

Unique: Treats prompts as first-class composable functions within a scripting language, allowing complex workflows to be expressed as JavaScript code with full control flow (loops, conditionals, error handling) rather than static workflow definitions.

vs others: More flexible than linear prompt chains because nested prompts can be conditionally executed, looped, or composed based on runtime data, enabling adaptive workflows that respond to intermediate results.

via “prompt chaining workflow pattern for sequential task execution”

Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents.

Unique: Implements prompt chaining as an explicit workflow pattern where each step is a distinct LLM invocation with independent prompts and validation, enabling fine-grained control over reasoning stages and intermediate result inspection rather than single-shot generation.

vs others: More transparent and auditable than single-shot generation by making each reasoning step explicit, and more flexible than fixed pipelines by allowing dynamic step selection based on intermediate results.

via “context-aware prompt chaining with output inheritance”

A structured prompt pipeline that turns vague ideas into implementable RFCs — works with any AI assistant.

Unique: Uses a file-based context inheritance pattern where outputs are explicitly passed as context to downstream prompts, creating a traceable chain of reasoning. This differs from typical prompt chaining where context is implicit or managed by the LLM — here, context is explicit and versioned as files.

vs others: More traceable than implicit context passing, more coherent than independent prompts, and enables users to inspect and understand the reasoning at each stage rather than treating the pipeline as a black box.

via “prompt-composition-and-chaining-patterns”

📏 Collection of prompts/rules for use within AI Agent settings

Unique: Provides templates for prompt chaining patterns that encode task decomposition and sequential reasoning in prompts themselves rather than requiring a dedicated workflow engine — enables prompt-native composition

vs others: Simpler to implement than frameworks like LangChain for basic chains, but lacks built-in error handling, caching, and observability of dedicated orchestration tools

via “prompt template composition with variable binding”

Core domain types for Model Context Protocol (MCP) tool generation

Unique: Provides MCP-native prompt definition system with parameterized templates and composition support, enabling Claude to discover and invoke prompt templates dynamically with runtime argument binding, rather than treating prompts as static strings

vs others: More composable than hardcoded prompts because templates are reusable and parameterized, and more discoverable than prompt libraries because they're exposed as MCP PromptDefinitions that Claude can query and invoke directly

via “chain-of-thought reasoning elicitation through prompt structuring”

Strategies and tactics for getting better results from large language models.

Unique: Synthesizes research on chain-of-thought prompting into practical templates and guidance on when to use it, including analysis of performance gains on specific task categories and interaction with other prompt techniques

vs others: More accessible than academic chain-of-thought papers, but less sophisticated than frameworks like LangChain's reasoning chains that programmatically decompose tasks and aggregate reasoning across multiple model calls

via “prompt chaining and multi-step workflow orchestration”

Guide and resources for prompt engineering.

via “prompt-composition-and-chaining”

Amplify your workflow with the best prompts.

Unique: Implements visual or declarative workflow composition for LLM chains with variable interpolation and conditional routing, abstracting away manual API orchestration code

vs others: Simpler than building chains with LangChain or LlamaIndex because it provides UI-driven composition without requiring Python/JavaScript coding

via “chain-of-thought prompting for complex reasoning”

A short course by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI).

via “prompt chaining and complex prompt composition instruction”

Anthropic's educational courses.

Unique: Treats prompt chaining as a distinct technique within the broader prompt engineering curriculum, with explicit patterns for context management and error handling across chain steps. Emphasizes the trade-offs between single-prompt complexity and multi-step chaining.

vs others: More systematic than scattered examples because it teaches prompt chaining as a deliberate technique with clear patterns, and more practical than academic papers because it focuses on production implementation patterns

via “prompt composition with conditional logic and branching”

Visual AI Prompt Editor

via “multi-turn prompt chaining with state passing”


Unique: Teaches prompt chaining as a pedagogical pattern with working code examples in Jupyter notebooks, emphasizing how to structure Python code that maintains semantic state across multiple API calls without requiring conversation history to be re-sent

vs others: More accessible than reading raw API documentation because it provides concrete, runnable examples of chaining patterns with instructor guidance on when and why to use sequential vs parallel execution

via “prompt chaining and multi-step task decomposition”

via “multi-step prompt chaining with conditional branching”

Unique: Implements conditional branching directly in the visual node editor, allowing non-technical users to define if/then logic for prompt chains without writing code, using visual connections and rule definitions instead of imperative programming

vs others: More accessible than LangChain or similar frameworks for non-developers, though likely less flexible for complex conditional logic that would require custom code in traditional orchestration tools