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Construa fluxos de trabalho corporativos de RAG e agentes com B…

Use o Bisheng para montar, avaliar e publicar fluxos de trabalho corporativos de RAG e agentes em dados internos, modelos e usuários de negócios.

O que é Construa fluxos de trabalho corporativos de RAG e agentes com B…?

Esta skill permite que desenvolvedores e engenheiros de IA montem, avaliem e publiquem fluxos de trabalho corporativos de RAG (Retrieval-Augmented Generation) e agentes utilizando a plataforma open-source Bisheng. Ela é especialmente útil para equipes que precisam integrar dados internos, modelos de linguagem (LLMs) e usuários de negócios em pipelines de IA complexos, mas de forma visual e intuitiva — substituindo componentes especializados por uma interface de "fluxograma" que simplifica operações como loops, paralelismo e processamento em lote. A skill suporta ambientes Docker e requer ao menos 4 núcleos de CPU e 16 GB de RAM. Ao seguir as instruções de instalação e configuração, o usuário pode rapidamente implantar o Bisheng e começar a construir fluxos de trabalho que conectam dados empresariais a modelos generativos, facilitando a criação de assistentes inteligentes e sistemas de busca contextualizada para aplicações de nível enterprise.

Funciona com~Claude Code~Codex CLI~Cursor

npx skills add https://github.com/agentskillexchange/skills/tree/main/skills/build-enterprise-rag-and-agent-workflows-with-bisheng

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Documentação

Construa fluxos de trabalho corporativos de RAG e agentes com Bisheng

Use o Bisheng para montar, avaliar e publicar fluxos de trabalho corporativos de RAG e agentes em dados internos, modelos e usuários de negócios.

Pré-requisitos

Bisheng, credenciais de provedor de LLM, fontes de dados corporativos

Instalação

Use o caminho de instalação upstream ou de configuração que corresponde ao seu ambiente:

git clone https://github.com/dataelement/bisheng.git
docker compose -f docker-compose.yml -p bisheng up -d

Requisitos e observações do upstream:

🖐️ Intuitivo e fácil de usar: Operações como loops, paralelismo e processamento em lote, que exigem componentes especializados em produtos similares, podem ser facilmente visualizadas no BISHENG como um "fluxograma" (desenhando uma l...
Docker 19.03.9+

Notas de uso básico ou início rápido:

Certifique-se de que as seguintes condições sejam atendidas antes de instalar o BISHENG:
CPU >= 4 Núcleos Virtuais
RAM >= 16 GB
Fonte: https://github.com/dataelement/bisheng
Extraído da documentação upstream: https://raw.githubusercontent.com/dataelement/bisheng/HEAD/README.md

Documentação

https://github.com/dataelement/bisheng

Fonte

Agent Skill Exchange

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

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ACF Extended for Advanced Custom Fields Workflows

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Activepieces Open Source Workflow Automation

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ActivityWatch Cross-Platform Automated Time Tracker and Producti

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act Local GitHub Actions Runner

act is an open-source CLI tool that runs GitHub Actions workflows locally using Docker, enabling fast feedback on workflow changes without pushing to GitHub. It is a standard tool for local Actions development and testing.

Add a browser review checkpoint before risky agent actions with

Use AgentClick when an agent should pause before risky commands, plans, drafts, or code changes so a human can inspect, edit, approve, or reject them in a purpose-built browser UI.

Add Claude design-system and accessibility workflows with UX/UI

Use UX/UI Agent Skills when Claude should generate tokens, component specs, accessibility audits, and framework-specific UI code from a repeatable design workflow.

Add executable smoke tests for shell scripts and CLIs before ref

Use Bats-core when an agent needs to turn fragile shell scripts or command-line workflows into something it can verify repeatedly after edits. The agent writes focused Bash tests for success paths, failure paths, and output contracts, then runs them locally or in CI before a refactor, release, or incident fix goes out.

Add graph-backed memory and context retrieval to agent workflows

Use Cognee to ingest project knowledge into graph and vector memory so agents can retrieve durable context across sessions and workflows.

Add IDE-style semantic code navigation and symbol-safe refactors

Give a coding agent symbol-aware lookup, cross-file rename, and structural edit tools before it starts making brittle text-only changes.

Add managed long-term memory to LangGraph agents with LangMem

Give LangGraph agents memory management and search tools so they can store, retrieve, and update durable facts across sessions.

Add multimodal lifelong memory to MCP and Python agents with Sim

Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.

Add persistent project memory and hook-driven synthesis with Cla

Keep Claude Code sessions grounded in prior decisions, project context, and daily handoff notes instead of starting from zero every time.

Add Postgres-native vector retrieval to agent and RAG workflows

Store embeddings beside application data in Postgres, create vector indexes, and query nearest neighbors for semantic search, RAG, recommendations, or agent memory retrieval.

Address GitHub PR review comments from the current branch with g

Find the open PR for the current branch, gather unresolved review comments, and drive a focused comment-resolution workflow with gh-authenticated context.

Add runtime guardrails to TypeScript agents with VoltAgent

Use VoltAgent to intercept, validate, and enforce input/output policies in TypeScript agent workflows.

Add temporal context memory to agent workflows with Zep

Use Zep as an external context layer for agents that need to store events, assemble temporal graph context, and retrieve relevant memory before model calls.

Adobe Firefly API Batch Image Generator

Calls Adobe Firefly

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