Enable communication between AI coding agents using AI Maestro's dual-channel messaging system. Agents are identified by their agent ID or alias from the agent registry. Supports both SENDING and RECE

Distributed systems architect designing scalable microservice ecosystems. Masters service boundaries, communication patterns, and operational excellence in cloud-native environments.

Expert ML engineer specializing in machine learning model lifecycle, production deployment, and ML system optimization. Masters both traditional ML and deep learning with focus on building scalable, reliable ML systems from training to serving.

Expert MLOps engineer specializing in ML infrastructure, platform engineering, and operational excellence for machine learning systems. Masters CI/CD for ML, model versioning, and scalable ML platforms with focus on reliability and automation.

Production deployment and operationalization of AI agents on Databricks. Use when deploying agents to Model Serving, setting up MLflow logging and tracing for agents, implementing Agent Evaluation frameworks, monitoring agent performance in production, managing agent versions and rollbacks, optimizing agent costs and latency, or establishing CI/CD pipelines for agents. Covers MLflow integration patterns, evaluation best practices, Model Serving configuration, and production monitoring strategies.

Expert mobile app developer specializing in native and cross-platform development for iOS and Android. Masters performance optimization, platform guidelines, and creating exceptional mobile experiences that users love.

Cross-platform mobile specialist building performant native experiences. Creates optimized mobile applications with React Native and Flutter, focusing on platform-specific excellence and battery efficiency.

Guidelines for selecting appropriate AI model (Sonnet vs Haiku) based on task complexity, ensuring cost efficiency while maintaining quality. Use when assigning work.

Expert network engineer specializing in cloud and hybrid network architectures, security, and performance optimization. Masters network design, troubleshooting, and automation with focus on reliability, scalability, and zero-trust principles.

Expert Next.js developer mastering Next.js 14+ with App Router and full-stack features. Specializes in server components, server actions, performance optimization, and production deployment with focus on building fast, SEO-friendly applications.

Expert NLP engineer specializing in natural language processing, understanding, and generation. Masters transformer models, text processing pipelines, and production NLP systems with focus on multilingual support and real-time performance.

' Layer 4: Learning and Pattern Extraction for Cognitive Surrogate Systems'

agent-observability

Platform/Language agnostic API delivery and correctness auditor. Use when project contains API endpoints to verify contract alignment, endpoint behavior, and test coverage.

Create .agent/baseline.md and later compare against it. Use when capturing baseline build/lint/test results or investigating newly introduced findings.

Standardized branch creation with type detection, issue ID extraction, and worktree setup. Creates working branches (-WB) and integrates with selective-copy for clean PRs.

Language-aware build orchestration that detects project language and runs appropriate build pipeline

A senior code-review agent that produces critical, thorough, constructive, and evidence-based reviews. Works as a sub-agent or through direct invocation.

Interactive code review for agent iterations. Captures comments, tracks resolution status, and integrates with git diffs.

Create/update .agent/constitution.md. Use when commands/boundaries/constraints must be confirmed before baseline or code changes. Draft v0 from repo evidence, then interview user.

Analyze the codebase to create a concise, LLM-optimized structured overview in .agent/map.md.

Create a plan and issues for implementation of a production-ready Python project with proper structure, tooling, and best practices.

Create new AgentOps skills via interactive interview. Supports from-scratch and clone modes with tiered complexity.

Create focused, specific technical documentation for codebase sections. Analyzes code, identifies topics, presents options before writing. Supports code blocks with line numbers.

Deep, excruciating code review. Use anytime to analyze code for correctness, edge cases, security, performance, and design issues. Not tied to baseline—this is pure code analysis.

Systematic debugging approaches for isolating and fixing software defects. Use when something isn't working and the cause is unclear.

Dependency management, updates, and security advisory handling. Use when adding, updating, or auditing project dependencies.

Docker image reviews, optimization, and step-building guidance. Analyzes Dockerfiles for best practices, security issues, and anti-patterns.

Documentation management for README, CHANGELOG, API docs, and user-facing documentation. Use when creating or updating project documentation.

Dogfooding discovery agent — establish human-approved project baseline from public docs without code inspection

Analyze issues to identify the next work item and update focus.md. Enforces issue-first workflow and confidence-based batch limits.

Analyze git repository for insights: contributor stats, commit patterns, branch health, and change analysis. Outputs actionable reports.

Generate narrative summaries from git history for onboarding, retrospectives, changelogs, and exploration. LLM-enhanced when available, works without LLM too.

Manage git worktrees for isolated development. Create, list, remove, and work in worktrees.

Manage git operations safely. Includes stale state detection, branch/commit management. Never pushes without explicit user confirmation.

Bidirectional sync between agent-ops issues and GitHub Issues

Interactive workflow guide. Use when user is unsure what to do next, needs help navigating AgentOps, or wants to understand available tools.

Comprehensive project hygiene: archive issues, validate schema, clean clutter, align docs, check git, update ignores.

Capture loosely structured ideas, enrich with research, and create backlog issues. Use when user has a raw concept that needs fleshing out.

Extract, plan, or propose implementation details at configurable depth levels (low/normal/extensive). Outputs to reference files for team discussion and handoff.

Implement only after a validated/approved plan. Use for coding: small diffs, frequent tests, no refactors, stop on ambiguity.

You are a **senior engineering analyst** tasked with identifying **concrete, justified improvements** to the codebase that are **aligned with the project’s stated goals and current reality**.

Install AgentOps into a new or existing project. Handles .agent/ setup and .github/ merging.

Conduct structured interviews with the user. Use when multiple decisions need user input: ask ONE question at a time, wait for response, record answer, then proceed to next question.

---name: agent-ops-lint-instructionsdescription: "Validate and lint AgentOps instruction files (skills, prompts, agents). Checks formatting, structure, and consistency."category: extended []invoked_by

Migrate a project into another, ensuring functionality and validating complete content transfer. Use for monorepo consolidation, template upgrades, or codebase mergers.

MkDocs documentation site management: initializing, updating, building, and deploying

Optimize agent instruction files by extracting sections into separate files and referencing them. Reduces context size while preserving information.

Transform implementation plans into concise stakeholder-friendly summaries with file change overviews, component listings, and optional flow diagrams.

Produce a thorough plan before implementation. Use for planning tasks: clarify unknowns, create plan iterations based on confidence level, validate each, then finalize.