Can I use specific modules like the task classifier independently?

Yes, the skill is structured with internal libraries (lib/) that allow for modular use of selection, spawning, or auditing functions.

Does this skill support tracking agent performance?

Yes, it uses an audit module with SQLite to track agent spawn events, confidence updates, and completion status across sessions.

What is included in the Agent Lifecycle Management mega-skill?

This mega-skill consolidates selection, spawning, execution, output processing, and auditing into one unified package for Claude Code.

How does it handle agent selection errors?

It utilizes a fallback-enabled selection logic that guarantees non-empty agent arrays even when specific mappings are not found.

What are the system requirements for this skill?

It requires Bash 4.0+, Node.js (LTS), jq for JSON processing, and SQLite3 for audit logging.

Agent Lifecycle Management

Name: Agent Lifecycle Management
Author: masharratt

bymasharratt

•

개발자 도구

Orchestrates the complete AI agent pipeline from task classification and selection to spawning, execution, and audit logging.

소개

The Agent Lifecycle Management mega-skill is a comprehensive orchestration framework designed for the Claude Flow ecosystem. It automates the complex process of managing AI agents by classifying user tasks into specific domains, selecting the optimal agents for implementation and validation, and handling technical deployment with robust dependency checking. By integrating structured output parsing and persistent SQLite audit trails, it provides a reliable and traceable environment for multi-agent workflows, ensuring that every step from agent birth to task completion is monitored and validated.

주요 기능

Intelligent task classification into nine specialized domains with automated agent selection.
Robust SQLite-based audit trails for tracking agent confidence and lifecycle events.
Production-ready TypeScript implementation for type-safe orchestration and 95%+ accuracy.
High-performance agent spawning with sub-200ms deployment and dependency validation.
Multi-pattern output parsing to extract structured data from diverse agent responses.
12 GitHub stars

사용 사례

Maintaining compliance and accountability in AI workflows through persistent event logging and confidence tracking.
Managing complex multi-agent development cycles involving implementers (Loop 3) and validators (Loop 2).
Automating infrastructure and deployment tasks with integrated system dependency checks.

소개

주요 기능

Intelligent task classification into nine specialized domains with automated agent selection.
Robust SQLite-based audit trails for tracking agent confidence and lifecycle events.
Production-ready TypeScript implementation for type-safe orchestration and 95%+ accuracy.
High-performance agent spawning with sub-200ms deployment and dependency validation.
Multi-pattern output parsing to extract structured data from diverse agent responses.
12 GitHub stars

사용 사례

Maintaining compliance and accountability in AI workflows through persistent event logging and confidence tracking.
Managing complex multi-agent development cycles involving implementers (Loop 3) and validators (Loop 2).
Automating infrastructure and deployment tasks with integrated system dependency checks.