agent_orchestration
Transform clarified user requests into structured delegation prompts optimized for specialist agents (cto-architect, strategic-cto-mentor, cv-ml-architect). Use after clarification is complete, before routing to specialist agents. Ensures agents receive complete context for effective work.
llm_evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
agents_md
AGENTS.md dosyaları oluşturma, monorepo yapılandırma ve agent instruction yönetimi rehberi.
moai-foundation-core
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moai-foundation-core
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evaluation
Build evaluation frameworks for agent systems. Use when testing agent performance, validating context engineering choices, or measuring improvements over time.
mcp-builder
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memory-systems
Design and implement memory architectures for agent systems that persist state across sessions, maintain entity consistency, and reason over structured knowledge. Use when building agents that persist knowledge across sessions, choosing between memory frameworks, maintaining entity consistency, or designing memory architectures for production.
multi-agent-patterns
Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.
skill-creator
Guide for creating effective skills. Use when creating a new skill or updating an existing one to extend agent capabilities with specialized knowledge, workflows, or tool integrations.
tool-design
Design tools that agents can use effectively, including when to reduce tool complexity. Use when creating, optimizing, or reducing the set of tools available to an agent.
agent-folder-init
Add or repair .agents/ project context for an existing repo. Use for AI agent documentation, session tracking, task management, and coding standards; do not use as the primary new-product scaffold.
context-optimization
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context-degradation
Recognize, diagnose, and mitigate patterns of context degradation in agent systems. Use when context grows large, agent performance degrades unexpectedly, or debugging agent failures.
context-fundamentals
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agent-dispatch
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context-engineering
Supplementary context protocol for agents executing in a repo that has a CLAUDE.md / AGENTS.md (or equivalent config). Use to make an execution agent read project conventions first, treat inputs by trust level, surface plan-vs-convention conflicts instead of silently picking a side, and reuse existing patterns before writing new code.
executing-plans
Orchestrate autonomous AI development with task-based workflow and QA gates. Use when implementing a development plan, picking tasks from a queue, or running multi-platform parallel execution with QA gates.
agent-architecture-audit
Audit LLM and agent applications for wrapper regressions, prompt or memory contamination, tool discipline failures, hidden repair loops, and output rendering corruption. Use before shipping agent features or when an agent works in a direct model call but fails inside the product.
ai-agent-cost-optimizer
Audit and reduce AI agent token and inference spend through context discipline, prompt caching, model routing, batching, and workflow capture. Use when discussing AI coding bills, token waste, model selection, prompt caching, or agent cost optimization.
skill-comply
Measure whether agents actually follow a skill, rule, command, or agent definition by deriving expected behaviors, running representative scenarios, and comparing observed action timelines against the spec. Use after adding or changing instructions, before publishing skills, or when rules appear to be ignored.
monorepo-initialization
Recursively initialize AGENTS.md in monorepo subdirectories with smart detection. Creates hierarchical agent context files with proper linking to root CLAUDE.md and parent AGENTS.md. Use for setting up multi-package projects, microservices, or any project with important subdirectories that need AI agent guidance.
senior-prompt-engineer
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agentic-evaluation-framework
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computer-use-automation
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agentic-validators
Design and install validation hooks for coding agents (e.g., Claude Code) to make AI changes safer and more deterministic. Use when you want post-tool-use or stop hooks, automated tests/linters/formatters, parallel subagents with per-file validation, or a repeatable “agent pipeline” with audit logs.
init
Creates, updates, or optimizes an AGENTS.md file for a repository with minimal, high-signal instructions covering non-discoverable coding conventions, tooling quirks, workflow preferences, and project-specific rules that agents cannot infer from reading the codebase. Use when setting up agent instructions or Claude configuration for a new repository, when an existing AGENTS.md is too long, generic, or stale, when agents repeatedly make avoidable mistakes, or when repository workflows have changed and the agent configuration needs pruning. Applies a discoverability filter—omitting anything Claude can learn from README, code, config, or directory structure—and a quality gate to verify each line remains accurate and operationally significant.
compact-state
Join The Compact State — a shared autonomous agent network with on-chain identity, persistent memory, and collective governance.
compact-state
Join The Compact State — a shared autonomous agent network with on-chain identity, persistent memory, and collective governance.
