Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add hajekim/agentic-design-patterns-extension --skill prompt-chaininggit clone --depth 1 https://github.com/hajekim/agentic-design-patterns-extensionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/prompt-chaining)<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/prompt-chaining"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/prompt-chaining/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hajekim/agentic-design-patterns-extension/prompt-chaining"><img src="https://agentmods.dev/badge/skills/hajekim/agentic-design-patterns-extension/prompt-chaining.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00401 | $0.03310 |
| Opus 5 | $0.00200 | $0.01655 |
| Sonnet 5 | $0.00080 | $0.00662 |
| Haiku 4.5 | $0.00040 | $0.00331 |
Grade A, and why
prompt-chaining scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to prompt-chaining — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 312 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Chaining Pattern
Overview
Prompt Chaining (also known as the Pipeline Pattern) is a foundational agentic design pattern that breaks complex tasks into a sequence of smaller, focused sub-tasks. Rather than overwhelming a single LLM call with a multifaceted problem, each sub-task is addressed by a specifically crafted prompt, and the output of one step feeds as input to the next.
Core Principle: Divide-and-conquer — decompose the complex into a logical chain of manageable steps.
When This Skill Applies
Activate this pattern when:
- A task is too complex or multifaceted for a single prompt
- Multiple distinct processing stages are required
- Intermediate results need validation or transformation before the next step
- External tools or APIs must be called between reasoning steps
- You need to build agents capable of multi-step reasoning, planning, and decision-making
- The cognitive load on the model is causing instruction neglect, contextual drift, or hallucination
Rule of thumb: If a monolithic prompt struggles with multiple constraints and sequential reasoning steps, switch to prompt chaining.
Context Engineering
Context Engineering is the overarching discipline that governs how AI agents are designed — it is the practice of constructing and delivering the right informational environment to the model at every step of a pipeline. It is not just prompt writing; it is the systematic engineering of everything the model sees.
┌─────────────────────────────────────────────────────────────┐
│ CONTEXT WINDOW │
│ │
│ ┌──────────────┐ ┌───────────────┐ ┌────────────────┐ │
│ │ System Prompt│ │ RAG / Docs │ │ Tool Outputs │ │
│ │ (Role + │ │ (Retrieved │ │ (API results, │ │
│ │ Behavior) │ │ Knowledge) │ │ computations) │ │
│ └──────────────┘ └───────────────┘ └────────────────┘ │
│ │
│ ┌──────────────┐ ┌───────────────┐ │
│ │ State/History│ │ Structured │ │
│ │ (Prior chain │ │ Outputs │ │
│ │ outputs) │ │ (JSON/schema)│ │
│ └──────────────┘ └───────────────┘ │
└─────────────────────────────────────────────────────────────┘
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 312 lines · 401 tokens per session scan A 57f4251b866e
prompt-chaining is a skill published in the GitHub repository hajekim/agentic-design-patterns-extension (1 stars, last pushed 5mo ago), licensed MIT. It adds 401 tokens to every session and 3,310 once invoked, about $0.0020 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to prompt-chaining, differing in 11 lines, and is treated as a copy.
Other skills, from other repositories
prompt engineering
Use this skill when asked to create, refine, analyze, or optimize prompts for Large Language Models (LLMs). This skill ensures adherence to prompt engineering best practices and enforces a rigorous design workflow.
prompt-optimizer
Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite…
gemini-api
Google Gemini API patterns for Python and TypeScript. Covers content generation, streaming, tool use (function calling), vision, system instructions, context caching, batch requests, and agent workflows. Use when building applications with the Gemini API or Google Generative AI SDKs.
appendix-prompt-engineering
This skill should be used when the user wants to learn "prompt engineering", "few-shot prompting", "zero-shot prompting", "chain of thought prompting", "structured output prompting", "role prompting", "system prompt design", "prompt best practices", "CoT prompting", "Pydantic structured output", "prompt iteration"…
prompt-chaining
This skill should be used when the user wants to "chain prompts", "build a pipeline", "break down complex tasks into steps", "sequential LLM calls", "multi-step reasoning", "pipeline pattern", "sequential agent pipeline", "multi-step prompt pipeline", "LLM chain", "step-by-step agent", "prompt pipeline", "decompose…
reasoning
This skill should be used when the user wants to "chain-of-thought prompting", "ReAct agent", "tree of thought", "step-by-step reasoning", "structured reasoning agents", "agent thinking", "scratchpad reasoning", "self-consistency", "reasoning traces", "deliberate thinking", "agent metacognition", "think before…