crewai

CrewAI expert for role-based multi-agent systems: agents with roles/goals, task definitions, sequential/hierarchical/parallel crews, memory, and flows. USE WHEN orchestrating a multi-agent crew with the CrewAI framework.

Ir para a instalação

Repositório: Sheshiyer/skill-clusters
Última atividade na origem: 5 de junho de 2026 às 17:21
Idioma detectado do SKILL.md: inglês
Estrelas: 0
Forks: 1

Opções de instalação

Por padrão, está selecionado o prompt que primeiro revisa a origem. Você pode mudar para um comando direto ou baixar uma cópia local.

Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.

Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.

npx skills add https://github.com/Sheshiyer/skill-clusters --skill crewai

O comando permanece em uma só linha. Role horizontalmente para revisá-lo antes de copiar.

Prefere uma cópia local? Baixe os arquivos disponíveis atualmente no SkillsMP.

Revise os arquivos de origem

Leia o SKILL.md e os arquivos complementares exibidos pelo SkillsMP antes de decidir se vai instalar.

SKILL.md

Instruções da origem · Visualização somente leitura

Voltar à pesquisa Voltar à instalação

arcplume

Arcplume runs Grok through the Grok Build CLI's own OAuth-authenticated session (grok login) for image generation, with strict preflight validation, secret-safe handling, and headless CLI-driven execution -- no separate XAI_API_KEY billing. Video falls back to the billed xAI API. USE WHEN a user wants to generate an image via a locally logged-in Grok Build CLI session, e.g. 'generate an image with grok', 'use grok build', or 'use my logged-in grok session'.

2026-07-07

selemene-core

Shared reference for the Selemene cluster: the two report surfaces (deterministic Rust reports vs. narrative witness-pipeline readings), the @selemene/bridge CLI contract, the output manifest format, and non-prescriptive witnessing tone. USE WHEN deciding which Selemene surface to invoke or when routing between birth/compatibility/transit reports and solo/dyadic readings.

2026-07-07

selemene-orchestrator

Route Selemene Engine tasks to the right surface: deterministic reports (birth/compatibility/transit) via selemene-core and the @selemene/bridge CLI, or narrative witness readings via selemene-report. USE WHEN the user wants anything under the Selemene/Noesis umbrella but has not named the exact surface.

2026-07-07

Ver todas deste repositório

Focus on:
1. Key facts and statistics
2. Recent developments
3. Expert opinions
4. Contrarian viewpoints

Be thorough and cite sources.

Requirements:
- 800-1000 words
- Engaging introduction
- Clear structure with headers
- Actionable conclusion

@agent
def researcher(self) -> Agent:
    return Agent(config=self.agents_config['researcher'])

@agent
def writer(self) -> Agent:
    return Agent(config=self.agents_config['writer'])

@task
def research_task(self) -> Task:
    return Task(config=self.tasks_config['research_task'])

@task
def writing_task(self) -> Task:
    return Task(config=self.tasks_config['writing_task'])

@crew
def crew(self) -> Crew:
    return Crew(
        agents=self.agents,
        tasks=self.tasks,
        process=Process.sequential,
        verbose=True
    )

@start()
def gather_requirements(self):
    """First step - gather inputs."""
    self.topic = self.inputs.get("topic", "AI")
    self.style = self.inputs.get("style", "professional")
    return {"topic": self.topic}

@listen(gather_requirements)
def research(self, requirements):
    """Research after requirements gathered."""
    research_crew = ResearchCrew()
    result = research_crew.crew().kickoff(
        inputs={"topic": requirements["topic"]}
    )
    self.research = result.raw
    return result

@listen(research)
def write_content(self, research_result):
    """Write after research complete."""
    writing_crew = WritingCrew()
    result = writing_crew.crew().kickoff(
        inputs={
            "research": self.research,
            "style": self.style
        }
    )
    return result

@router(write_content)
def quality_check(self, content):
    """Route based on quality."""
    if self.needs_revision(content):
        return "revise"
    return "publish"

@listen("revise")
def revise_content(self):
    """Revision flow."""
    # Re-run writing with feedback
    pass

@listen("publish")
def publish_content(self):
    """Final publishing."""
    return {"status": "published", "content": self.content}

def _run(self, query: str) -> str:
    # Implementation
    results = search_api.search(query)
    return format_results(results)

1. Define researcher and writer agents
2. Create research → analysis → writing pipeline
3. Use structured output for research format
4. Chain tasks with context

1. Build crew with agents and tasks
2. Add Langfuse callback handler
3. Monitor agent interactions
4. Evaluate output quality

1. Design workflow with CrewAI Flows
2. Use LangGraph patterns for state
3. Combine crews in flow steps
4. Handle branching and routing

name	crewai
description	CrewAI expert for role-based multi-agent systems: agents with roles/goals, task definitions, sequential/hierarchical/parallel crews, memory, and flows. USE WHEN orchestrating a multi-agent crew with the CrewAI framework.
cluster	ai-agents-meta
version	1.0.0
origin	antigravity-awesome-skills (MIT)
risk	unknown
source	vibeship-spawner-skills (Apache 2.0)
date_added	"2026-02-27T00:00:00.000Z"

name	crewai
description	CrewAI expert for role-based multi-agent systems: agents with roles/goals, task definitions, sequential/hierarchical/parallel crews, memory, and flows. USE WHEN orchestrating a multi-agent crew with the CrewAI framework.
cluster	ai-agents-meta
version	1.0.0
origin	antigravity-awesome-skills (MIT)
risk	unknown
source	vibeship-spawner-skills (Apache 2.0)
date_added	"2026-02-27T00:00:00.000Z"

crewai

Informações da origem

Opções de instalação

Revise os arquivos de origem

Mais deste repositório

Mais deste repositório

CrewAI

CrewAI

Expertise

Capabilities

Prerequisites

Scope

Ecosystem

Primary

Common_integrations

Platforms

Patterns

Basic Crew with YAML Config

config/agents.yaml

config/tasks.yaml

crew.py

main.py

Hierarchical Process

Define specialized agents

Hierarchical crew - manager coordinates

Manager decides:

- Which agent handles which task

- When to delegate

- How to combine results

Planning Feature

Enable planning

With planning enabled:

1. CrewAI generates step-by-step plan

2. Plan is injected into each task

3. Agents see overall structure

4. More consistent results

Access the plan

Memory Configuration

Memory types:

- Short-term: Within task execution

- Long-term: Across executions

- Entity: About specific entities

Custom memory config

Memory helps agents:

- Remember previous interactions

- Build on past work

- Maintain consistency

Flows for Complex Workflows

Run flow

Custom Tools

Method 1: Class-based tool

Method 2: Function decorator

Assign tools to agents

Collaboration

Delegation Triggers

Research and Writing Crew

Observable Agent Team

Complex Workflow with Flows

Related Skills

When to Use

Limitations