oysterworkflow — Codex Skill by ShuxinYang111

by ShuxinYang111 · Codex Skill · ★ 202

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About oysterworkflow

OysterWorkflow English | 简体中文 The work experience layer for autonomous agents on macOS and Windows. Website Commercial Licensing OysterWorkflow captures what humans and agents observe, how they react, and how they complete real work on computers. It turns screens, OCR text, clicks, keystrokes, retries, choices, and verification moves into reusable experience for AI agents. The current release focuses on reviewable AI skills: recording evidence, detecting candidate workflows, creating structured workflow skills, and installing them into your agent. Open Core The core workflow pipeline is open source in OysterWorkflow Core under the Apache-2.0 license. The open core includes the Screenpipe ingest client, OCR/UI/audio trace processing, event normalization, deduplication, segmentation, workflow discovery, OpenClaw skill extraction, and generated skill quality evaluation. The Idea Autonomous agents need more than instructions. They need work experience. Most real work is not just reasoning or a

agent-toolsagentic-aiai-agentai-agentsautomationautonomous-agentscomputer-usedesktop-automationdeveloper-toolsmacos

Quick Facts

Stars202
Forks10
CategoryCodex Skill
Quality Score62.2037461674911/100
Last Updated2026-06-05
Created2026-04-18
Est. Tokens~177k

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Frequently Asked Questions

What is oysterworkflow?

oysterworkflow is The work experience layer for autonomous agents. Open-source core: https://github.com/ShuxinYang111/oysterworkflow-core. It is categorized as a Codex Skill with 202 GitHub stars.

How do I install or use oysterworkflow?

You can find installation instructions and usage details in the oysterworkflow GitHub repository at github.com/ShuxinYang111/oysterworkflow. The project has 202 stars and 10 forks, indicating an active community.

What are the best alternatives to oysterworkflow?

The top alternatives to oysterworkflow on Agent Skills Hub include mcp, claude-forge, ai-maestro. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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