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agentdb-state-manager

AgentDB Vector Search Optimization operates on 3 fundamental principles:

Use this skill in the scenario of intelligent agent application development.

Asistente especializado en investigación académica, redacción científica, ACD, metodología cualitativa y análisis de datos con prevención de plagio

AI assistant for creating clear, actionable task descriptions for GitHub Copilot agents

Use when building AI agent systems. Covers agent loops, tool calling, planning patterns, memory systems, multi-agent coordination, and safety guardrails. Apply when creating autonomous AI workflows, coding assistants, or task automation systems.

Write clear, plain-spoken code comments and documentation that lives alongside the code. Use when writing or reviewing code that needs inline documentation like file headers, function docs, architectural decisions, or explanatory comments. Works well for both human readers and AI coding assistants who see one file at a time.

This workflow enables you to transition from manual implementation to high-level system architecture by managing autonomous AI agents (like Devin) as "junior buddies." By shifting implementation to ag

Features in LivestockAI must now be designed for **dual consumption**: Humans (UI) and Agents (MCP/API).

Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination

Architecture guidelines for Jarvy CLI - codebase structure, tool implementation patterns, registry system, platform-specific code organization, and module conventions.

Code quality guidelines for Jarvy CLI - Rust formatting, Clippy linting, error handling patterns, documentation standards, and Conventional Commits.

Dependency management guidelines for Jarvy - crate selection criteria, feature flag best practices, version management, security auditing with cargo-audit and cargo-deny.

Performance optimization guidelines for Rust CLI tools. Covers efficient command execution, parallel processing, lazy initialization, allocation minimization, config parsing, and build optimizations for cross-platform CLI applications.

Security best practices and guidelines for the Jarvy CLI codebase - a cross-platform development environment provisioning tool that executes system commands with elevated privileges

Track and measure agentic coding KPIs for ZTE progression. Use when measuring workflow effectiveness, tracking Size/Attempts/Streak/Presence metrics, or assessing readiness for autonomous operation.

Patterns for multi-agent coordination, task decomposition, handoffs, and workflow orchestration. Best practices for building and managing agent systems.

Design and operate multi-agent orchestration patterns (ReAct loops, evaluator-optimizer, orchestrator-workers, tool routing) for LLM systems. Use when building or debugging agent workflows, tool-use loops, or multi-step task delegation; triggers: agentic, multi-agent, orchestration, ReAct, evaluator-optimizer, tool-use, handoff.

This skill allows product managers and founders to bypass the traditional "design-to-engineering" bottleneck by acting as a "generative lead" who directs AI agents to build, deploy, and maintain softw

Collaborative programming framework for production-ready development. Use when starting features, writing code, handling security/errors, adding comments, discussing requirements, or encountering knowledge gaps. Applies to all development tasks for clear, safe, maintainable code.

agentic-vision

Transform AI from a chat interface into a proactive teammate with "arms and legs." By using the Model Context Protocol (MCP) and agentic frameworks, you can move beyond "vibe coding" to autonomous exe

Design, review, and improve agent workflows & agent using SSOT, SRP, Fail Fast principles. Supports Prompt Chaining, Parallelization, Orchestrator-Workers patterns.

Design and implement agentic AI workflows and patterns. Covers ReAct, planning agents, tool use, memory systems, and multi-agent orchestration. Use when building autonomous AI agents, implementing complex task automation, or designing intelligent workflow systems.

Coinbase AgentKit - Toolkit for enabling AI agents with crypto wallets and onchain capabilities. Use for building autonomous agents that can execute transfers, swaps, DeFi operations, NFT minting, smart contract deployment, and gasless transactions via Smart Wallets.

Agent Lightning를 사용하여 AI 에이전트를 자동으로 최적화하는 방법을 제공합니다.

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Autonomous AI agent platform for building and deploying continuous agents. Use when creating visual workflow agents, deploying persistent autonomous agents, or building complex multi-step AI automation systems.

Generate a project-specific AGENTS.md from a user goal, then confirm before overwriting.

Patterns and architectures for building AI agents and workflows with LLMs. Use when designing systems that involve tool use, multi-step reasoning, autonomous decision-making, or orchestration of LLM-driven tasks.

Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

Branch skill for building and improving agents. Use when creating new agents, adapting marketplace agents, validating agent structure, writing system prompts, or improving existing agents. Triggers: 'create agent', 'improve agent', 'validate agent', 'fix agent', 'agent frontmatter', 'system prompt', 'adapt agent', 'customize agent', 'agent examples', 'agent tools'.

Write effective AGENTS.md files for AI coding agents.

Generate hierarchical AGENTS.md structures optimized for AI coding agents with minimal token usage.

Guide for using and supporting the AGENTS.md standard in VS Code. Use this when asked about AGENTS.md, custom instructions, or repo-level AI agent configuration.

Keeps repo-local agent instructions consistent by proposing updates to AGENTS.md when a user corrects the coding agent or asks to change AGENTS.md, CLAUDE.md, .claude/CLAUDE.md, or GEMINI.md.

Create or update root and nested AGENTS.md files that document scoped conventions, monorepo module maps, cross-domain workflows, and (optionally) per-module feature maps (feature -> paths, entrypoints, tests, docs). Use when the user asks for AGENTS.md, nested agent instructions, or a module/feature map.

AGENTS.md is the canonical agent-facing documentation. Keep it minimal—agents are capable and don't need hand-holding.

AgentsKB - Knowledge Base API for AI Agents with 32K+ technical Q&As

Set a key and value in the keyvalue storage. Requires authentication. Use for Agentuity cloud platform operations

Delete one or more vectors by key. Requires authentication. Use for Agentuity cloud platform operations

Get a specific vector entry by key. Requires authentication. Use for Agentuity cloud platform operations

Search for vectors using semantic similarity. Requires authentication. Use for Agentuity cloud platform operations

Add or update vectors in the vector storage. Requires authentication. Use for Agentuity cloud platform operations

Create and maintain AgentV YAML evaluation files for testing AI agent performance. Use this skill when creating new eval files, adding eval cases, or configuring custom evaluators (code validators or LLM judges) for agent testing workflows.

Regla 05: Aggregates y Aggregate Roots. Use when implementing DDD patterns.

Aggregate and summarize event datasets (logs) using OPAL statsby. Use when you need to count, sum, or calculate statistics across log events. Covers make_col for derived columns, statsby for aggregation, group_by for grouping, aggregation functions (count, sum, avg, percentile), and topk for top N results. Returns single summary row per group across entire time range. For time-series trends, see time-series-analysis skill.