agent-memory-orchestrator

Manage long-term project memory - store, search, and retrieve project context

Orchestrate complex multi-step tasks with memory context and systematic execution

Run 4-phase post-implementation verification cycle to ensure quality and completeness

Use this agent when context usage is high and intelligent compaction is needed to preserve important information while freeing space. Examples: <example>Context: Context usage is at 75% and user is about to start a new major task. User: "Let's analyze this large codebase now" Assistant: "Before we begin the codebase analysis, I notice context usage is at 75%. Let me compact the context first to ensure we have adequate space." <commentary>The agent should trigger proactively when context usage is high before starting memory-intensive tasks. This prevents context overflow during the analysis.</commentary> Assistant: "I'll use the context-compactor agent to free up space while preserving critical information."</example> <example>Context: User receives a warning about high context usage. User: "My context seems full, can you clean it up?" Assistant: "I'll help optimize your context usage by intelligently compacting it." <commentary>User explicitly requests context cleanup, which is a direct trigger for the context-compactor agent.</commentary> Assistant: "I'll use the context-compactor agent to analyze and compact your context while preserving all critical information."</example> <example>Context: During a long session, context_check returns should_compact=true. User: "What's next on our task list?" Assistant: "Let me check the tasks, but first I notice our context needs compaction." <commentary>The agent should trigger when context_check indicates compaction is needed, even if the user doesn't explicitly request it. This is proactive context management.</commentary> Assistant: "I'll use the context-compactor agent to optimize our context before continuing with the task list."</example> <example>Context: User mentions memory or context issues. User: "I feel like you're forgetting things from earlier in our conversation" Assistant: "That could be due to context limitations. Let me compact our context intelligently to preserve the most important information." <commentary>User reports symptoms of context overflow. The agent should trigger to analyze and compact context while ensuring critical information is preserved.</commentary> Assistant: "I'll use the context-compactor agent to optimize context retention and ensure important information is preserved."</example>

Use this agent when you need to analyze git repository history, understand recent changes, investigate commit patterns, or gather context about code evolution. This agent specializes in providing insights from git data to support development decisions. Examples: <example>Context: User is about to start a major refactoring and wants to understand recent activity in the codebase. user: "What's been changed recently in the authentication module?" assistant: "I'll use the git-analyzer agent to examine recent changes in the authentication module and provide you with detailed insights." <commentary>The agent should trigger because the user is asking about recent changes in a specific area of the codebase, which requires git history analysis to understand code evolution and recent activity patterns.</commentary></example> <example>Context: Developer is investigating a bug and needs to find when a particular behavior was introduced. user: "Can you help me figure out when the login validation logic changed?" assistant: "I'll analyze the git history to trace changes to the login validation logic." <commentary>This scenario requires analyzing git commits related to specific functionality, identifying when changes occurred, and providing context about the evolution of that code - a core responsibility of this agent.</commentary></example> <example>Context: Team lead wants to understand contribution patterns before sprint planning. user: "Show me the commit activity for the last two weeks" assistant: "I'll use the git-analyzer agent to analyze recent commit patterns and contribution activity." <commentary>The request involves analyzing git history for patterns, statistics, and insights - exactly what this agent is designed for. The agent will provide chronological summaries and author contribution patterns.</commentary></example> <example>Context: Developer is planning a feature and wants to avoid conflicts with ongoing work. user: "Before I start working on the payment system, what's the recent activity there?" assistant: "Let me analyze the recent git activity in the payment system to help you avoid conflicts." <commentary>This proactive scenario requires analyzing recent changes in a specific area to provide context for planning new work and identifying potential conflicts - a key use case for the git-analyzer agent.</commentary></example>

Use this agent when code has been written or modified and needs quality validation before review or commit. This agent proactively triggers after Write/Edit operations involving significant code changes, when users mention quality checks, or before git commits. Examples: <example>Context: User just completed implementing a new authentication module using multiple Write/Edit calls. assistant: "I've completed the authentication module implementation. Let me validate the code quality to catch any issues early." <commentary>The agent should trigger proactively after significant code implementation to provide immediate quality feedback. This helps catch issues before they reach formal review.</commentary> assistant: "I'll use the code-quality-validator agent to analyze the changes."</example> <example>Context: User is about to create a git commit after adding several new functions. user: "Create a commit for these changes" assistant: "Before committing, I should validate the code quality to ensure we're not introducing issues." <commentary>The agent should trigger automatically before git commits to act as a quality gate, preventing problematic code from entering the repository.</commentary> assistant: "I'll use the code-quality-validator agent to review the changes first."</example> <example>Context: User has made edits to a critical security module. user: "Check if this looks good" assistant: "I'll perform a thorough code quality analysis to identify any issues." <commentary>Direct user request for validation should always trigger this agent, especially for critical code paths.</commentary> assistant: "I'll use the code-quality-validator agent to assess the code."</example> <example>Context: User completed a refactoring task touching multiple files. assistant: "The refactoring is complete. Let me proactively validate the changes to ensure quality standards are maintained." <commentary>After multi-file changes, proactive validation helps ensure consistency and catches issues that might span files.</commentary> assistant: "I'll use the code-quality-validator agent to review the refactored code."</example>

3 hooks across 3 events