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.
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.
agents_md
AGENTS.md dosyaları oluşturma, monorepo yapılandırma ve agent instruction yönetimi rehberi.
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.
compact-state
Join The Compact State — a shared autonomous agent network with on-chain identity, persistent memory, and collective governance.
moai-foundation-core
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task-orchestration
Load PROACTIVELY when decomposing a user request into parallel agent work. Use when user says \"build this\", \"implement this feature\", or any request requiring multiple agents working concurrently. Guides task decomposition into parallelizable units, agent assignment with skill matching, dependency graph construction, and result aggregation. The runtime engine handles WRFC chain coordination automatically via <gv> directives.
moai-foundation-core
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compact-state
Join The Compact State — a shared autonomous agent network with on-chain identity, persistent memory, and collective governance.
