prompt-chaining

Decompose tasks into sequential LLM steps with validation between stages.

Updated Apr 29, 2026
One-click install
npx skills add https://github.com/dev-khoi/AURA-conHack-2026 --skill prompt-chaining-dev-khoi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-chaining
Source: https://github.com/dev-khoi/AURA-conHack-2026/tree/main/.opencode/skills/prompt-chaining
Command: npx skills add https://github.com/dev-khoi/AURA-conHack-2026 --skill prompt-chaining-dev-khoi

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Complex tasks handled by a single prompt often produce unreliable results because errors compound silently and intermediate reasoning is invisible. This Skill breaks work into fixed sequential LLM steps with validation gates between stages, so bad output is caught early and each stage stays focused on one job. ## Core Features & Use Cases - Sequential Step Decomposition: Split one task into ordered stages where each step has a single responsibility and a locked input/output schema. - Inter-Step Validation: Check output after every step with retry and stop rules, preventing bad early output from poisoning later stages. - Observability: Log step input, output, cost, and latency for each handoff to keep intermediate state traceable. - Use Case: When processing a long document, chain extract-facts → summarize-facts → draft-answer → check-answer-against-facts → format-output, validating each handoff instead of relying on one monolithic prompt. ## Quick Start Ask the AI to break your task into fixed sequential steps with a defined schema and validation check between each step using the prompt chaining pattern.

Frequently Asked Questions about prompt-chaining

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I break a complex LLM task into sequential steps?▼

Define the end output first, then split the task into small steps where each has one responsibility. Lock an input/output schema for every handoff, validate after each step, and apply retry or stop rules when output fails validation.

When should I use prompt chaining instead of a single prompt?▼

Use prompt chaining when the task has a clear sequence, later steps need cleaned output from earlier steps, or you need step-specific prompts, tools, or models. If one prompt solves the task well, chaining adds unnecessary handoff cost.

What are good step boundaries in a prompt chain?▼

Good boundaries include extracting facts, summarizing facts, drafting an answer, checking the answer against the facts, and formatting for the target system. Each step should have exactly one job and a well-defined handoff format.

Why does prompt chaining fail or produce worse results?▼

Common failure modes include too many steps making the flow slow and fragile, vague handoff formats causing drift, later steps redoing earlier work, and missing validation letting errors compound. Copying full context into every step also spikes cost.

When should I avoid prompt chaining?▼

Avoid it when order does not matter, work can run in parallel, steps depend on dynamic branching rather than fixed flow, or the handoff cost exceeds the quality gain. In those cases use a single prompt or a different orchestration pattern.