Results for “agent-workflow”

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

CI/CD pipeline engineering agent for GitHub Actions workflows with matrix builds, caching, artifacts, and security best practices

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

Use acpx as a headless ACP CLI for agent-to-agent communication, always inside an isolated SubAgent. Use when running coding agents through acpx, managing persistent ACP sessions, queueing prompts, consuming structured agent output from scripts, comparing the same prompt across multiple agents, or composing multi-agent workflows with defineFlow/decision/decisionEdge. Never invoke the claude adapter (nested-instance blacklist).

Provides a structured process for creating or updating skills that extend an AI agent's capabilities with specialized knowledge, workflows, and tool integrations.

Agent profile for evaluate and implement no-code/low-code workflows, forms, automations, Airtable/Sheets/Zapier-like patterns. Use when Codex needs a specialist agent perspective for planning, implementation, review, debugging, validation, or handoff in this domain.

Optimizes agent context setup by structuring rules, specs, source files, error output, and conversation history to improve output quality.

Manages Netlify sites through the official CLI, covering installation, local development, and deployment workflows.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent.

Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Invokes the researcher agent.

Machine learning development agent for end-to-end ML workflows: data preprocessing, model training, evaluation, hyperparameter tuning, and deployment

Use `task-agent` to update source-backed documentation or `review-agent` to assess documentation impact and accuracy when public behavior or operator workflows change. Skip work with no audience-facing behavior change.

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

Coordinate multi-agent development pipelines using shared JSON task files. Use when dispatching work across dev team agents (code-agent, test-agent, docs-agent, monitor-bot), tracking pipeline progress, or running sequential/parallel workflows. Covers project init, task assignment, status tracking, agent dispatch via sessions_send, and result collection. Supports two modes: linear (sequential pipeline) and dag (dependency graph with parallel execution).

Guides a research-before-coding workflow, searching existing tools, libraries, and patterns before writing custom code, with decision matrices and integration with agent workflows.

Move issues and external pull requests through a state machine of triage roles — categorise, verify, grill if needed, and write agent-ready briefs.

Create, synthesize, and iteratively improve agent skills following the Agent Skills specification. Handles source capture, precision passes, authoring, registration, and validation.

Inspect, create, close, and rearrange cmux workspaces, panes, surfaces, and agent sessions, and send input or monitor agents running inside cmux from macOS terminal workflows.

Analyzes Android apps to identify key user workflows for AppFunctions such as creating a note, playing media, or sending an automated or AI agent triggered message, voice commands, or system shortcuts, without needing to open the app UI. Generates Kotlin code to expose these workflows to the Android system, allowing agents to discover and execute them on-device. Also refines KDoc documentation to ensure AI agents correctly understand and use the provided functionality.

Especialista em CrewAI - o principal framework multi-agente baseado em papéis utilizado por 60% das empresas da Fortune 500. Abrange design de agentes com papéis e objetivos, definição de tarefas, orquestração de crew, tipos de processo (sequencial, hierárquico, paralelo), sistemas de memória e flows para workflows complexos. Essencial para construir equipes colaborativas de agentes IA. Use quando: crewai, equipe multi-agente, papéis de agentes, crew de agentes, agentes baseados em papéis.

Generates a complete GitHub Copilot configuration for a new project, including instructions, reusable skills, and agent definitions tailored to the technology stack.

Build automated AI workflows combining multiple models and services for batch processing, scheduled tasks, event-driven pipelines, and agent loops using the inference.sh CLI.

Use when data operation or statistical analysis tasks do not need full program implementation, and the agent should operate Insyra through CLI/REPL, .isr scripts, or DSL workflows, including environment workflows, reproducible command pipelines, and command selection guidance.

This skill should be used when building the async job workflow, implementing polling loops, handling job status transitions, processing rich data, managing conversation threads, or understanding the full submit-poll-complete lifecycle of the Bankr Agent API.

Analyzes Android apps to identify user workflows for AppFunctions, generates Kotlin code to expose them to the Android system, and refines KDoc documentation for AI agent compatibility.

Quick reference for Salesforce Agent Script CLI commands, covering retrieve, validate, publish, deploy, and test workflows, plus authoring bundle structure and CI/CD integration.

Expert skill for building LLM applications with the LlamaIndex framework — RAG pipelines, multi-agent orchestration, event-driven workflows, knowledge graph construction, production deployment, and evaluation. Use when working with LlamaIndex or comparing RAG and agent orchestration frameworks.

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.

Establishes a two-party working posture where the user directs and the agent executes, surfacing every genuine fork as a batched single-select question with a recommended default.

Build multi-agent AI systems with LangGraph — the low-level orchestration framework for stateful, graph-based agent workflows. Covers supervisor, swarm, and hierarchical multi-agent patterns; subgraph composition; state management (checkpointers/stores); persistence; evals; and production debugging. Reach for this when designing agent architectures that need cycles, conditional branching, parallel execution, or human-in-the-loop patterns.