What is considered 'good evidence' in this workflow?

Good evidence includes concrete command output, such as specific test pass counts and terminal exit codes, rather than vague statements like 'it works'.

How does this skill prevent two AI workers from doing the same task?

It implements an atomic claiming phase (Phase 2) where the worker must successfully run a claim script with their unique session ID before starting any work.

What happens if a task fails during implementation?

The workflow directs the worker to log the failure evidence and release the task so that another worker or a human can retry it, rather than marking it as resolved.

Why does the skill forbid using 'git add . '?

Selective staging is required because in a multi-worker environment, other agents might have uncommitted changes; only the files modified for the specific task should be staged.

Teamwork Worker Workflow

Name: Teamwork Worker Workflow
Author: mnthe

bymnthe

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协作工具

Orchestrates the complete lifecycle of task execution for AI agents within a collaborative development environment.

关于

The worker-workflow skill provides a rigorous, multi-phase framework that enables Claude to function as a reliable autonomous agent within the Teamwork marketplace ecosystem. It guides the model through a structured process of task discovery, atomic claiming to prevent race conditions, implementation, and mandatory evidence collection. By enforcing selective git staging and standardized status updates, it ensures that multiple AI workers can collaborate on a single repository without conflicting changes or lost progress.

主要功能

Atomic task claiming mechanism to prevent multi-worker conflicts
Structured 6-phase task lifecycle from discovery to final commit
2 GitHub stars
Mandatory evidence collection with required command exit codes
Standardized status reporting for successful and failed task attempts
Selective git staging to prevent accidental commits of unrelated files

使用场景

Maintaining a clean, audit-ready git history in agent-driven repositories
Coordinating multiple AI agents working simultaneously on the same software project
Standardizing the task reporting and verification process for autonomous workers

关于

主要功能

Atomic task claiming mechanism to prevent multi-worker conflicts
Structured 6-phase task lifecycle from discovery to final commit
2 GitHub stars
Mandatory evidence collection with required command exit codes
Standardized status reporting for successful and failed task attempts
Selective git staging to prevent accidental commits of unrelated files

使用场景

Maintaining a clean, audit-ready git history in agent-driven repositories
Coordinating multiple AI agents working simultaneously on the same software project
Standardizing the task reporting and verification process for autonomous workers