What is artifact-based state handoff?

It is the practice of writing the output of one workflow step into a physical file (an artifact) which the next skill then reads. This ensures that critical data is passed between steps reliably without relying on volatile conversation history.

What is the purpose of the context.md file?

The context.md file acts as a living summary of the current task and decisions made. It is updated by each skill in a workflow to provide a persistent source of truth for the entire system.

When should I use 'fork' versus 'inherit' context?

Use 'fork' for clean-room analysis like triage, research, or reviews to prevent context pollution from previous chat history. Use 'inherit' for implementation tasks that require the full context of prior decisions and conversation.

How does the !command preprocessing syntax work?

The !command syntax executes shell commands immediately before a prompt reaches Claude. This allows the model to see fresh, deterministic output (like a git diff or database schema) without the overhead of a standard tool call.

Skills Workflows Orchestrator

Name: Skills Workflows Orchestrator
Author: outfitter-dev

byoutfitter-dev

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Productivity & Workflow

Designs and implements structured multi-skill pipelines using artifact-based state management for complex AI development tasks.

The Skills Workflows skill provides a robust framework for orchestrating complex AI development tasks by chaining specialized skills together. It introduces a standardized discipline for state handoff using physical artifacts and a shared context file, ensuring that critical project decisions and task progress remain persistent even when conversation history is compacted. By utilizing advanced patterns such as context forking for isolated analysis and !command preprocessing for deterministic data injection, it allows developers to build reliable, multi-stage workflows for triaging, planning, implementing, and reviewing code changes.

Key Features

012 GitHub stars

02Automated failure mode mitigations for complex AI pipelines

03Deterministic data injection using !command preprocessing syntax

04Standardized directory structure for project context and constraints

05Workflow isolation through 'fork' vs 'inherit' context patterns

06Artifact-based state handoff between sequential development steps

Use Cases

01Building a multi-stage 'Triage → Plan → Implement → Review' pipeline

02Creating isolated analysis skills that prevent conversation context pollution

03Managing persistent project state across long-running AI development sessions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add outfitter-dev/outfitter skills-workflows

For use in Claude.ai and ChatGPT

Download Skill