AI工作流 是 AI Skill Hub 本期精选Agent工作流之一。综合评分 8.0 分,整体质量较高。我们强烈推荐将其纳入你的 AI 工具库,帮助提升工作效率。
AI工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
AI工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 方式一:go install(推荐) go install github.com/knowns-dev/knowns@latest # 方式二:从源码编译 git clone https://github.com/knowns-dev/knowns cd knowns go build -o knowns . # 方式三:下载预编译二进制 # 访问 Releases 页面下载对应平台二进制文件 # https://github.com/knowns-dev/knowns/releases
# 查看帮助 knowns --help # 基本运行 knowns [options] <input> # 详细使用说明请查阅文档 # https://github.com/knowns-dev/knowns
# knowns 配置说明 # 查看配置选项 knowns --config-example > config.yml # 常见配置项 # output_dir: ./output # log_level: info # workers: 4 # 环境变量(覆盖配置文件) export KNOWNS_CONFIG="/path/to/config.yml"
<p align="center"> <img src="./images/logo.png" alt="Knowns" width="120"> </p>
<p align="center"> <a href="https://go.dev/"><img src="https://img.shields.io/badge/go-%3E%3D1.24.2-00ADD8?style=flat-square&logo=go" alt="Go"></a> <a href="https://www.npmjs.com/package/knowns"><img src="https://img.shields.io/npm/v/knowns.svg?style=flat-square" alt="npm"></a> <a href="https://github.com/knowns-dev/knowns/actions/workflows/ci.yml"><img src="https://github.com/knowns-dev/knowns/actions/workflows/ci.yml/badge.svg" alt="CI"></a> <a href="#installation"><img src="https://img.shields.io/badge/platform-win%20%7C%20mac%20%7C%20linux-lightgrey?style=flat-square" alt="Platform"></a> <a href="LICENSE"><img src="https://img.shields.io/github/license/knowns-dev/knowns?style=flat-square" alt="License"></a> </p>
<p align="center"> <a href="https://knowns.sh">Homepage</a> | <a href="./README.vi.md">Tiếng Việt</a> | <a href="./docs/README.md">Documentation</a> </p>
<p align="center"> <strong>Give your AI coding assistant structured access to tasks, docs, specs, and decisions - so it stops guessing and starts building.</strong> </p>
---
Every time you start a new AI coding session, you re-explain your architecture, paste docs, repeat conventions, and clarify past decisions. Your AI assistant is powerful - but it forgets everything between sessions.
Knowns fixes that. It gives AI assistants like Claude, Cursor, Copilot, and others structured, persistent access to your project's tasks, documentation, specs, acceptance criteria, and architectural decisions. Instead of prompting from scratch, your AI reads what it needs and picks up where you left off.
If you believe AI should truly understand software projects, consider giving Knowns a star.
<p align="center"> <a href="https://player.cloudinary.com/embed/?cloud_name=dkxhoyenc&public_id=knowns-full-pipeline_uwhyk1"> <img src="./images/knowns-full-pipeline-20s.gif" alt="Knowns full pipeline demo preview" width="100%"> </a> </p> <p align="center"> <em>Click the preview to watch the full pipeline demo video.</em> </p>
brew install knowns-dev/tap/knowns
knowns --version
npx knowns init
| Capability | What It Does |
|---|---|
| **Task Management** | Tasks with acceptance criteria, plans, status, and time tracking |
| **Documentation** | Nested markdown folders with cross-references and mermaid support |
| **Semantic Search** | Meaning-based search with local AI models (fully offline) |
| **Time Tracking** | Built-in timers and reports per task |
| **Context Linking** | @task-42 and @doc/patterns/auth references AI can resolve |
| **Validation** | Detect broken references and incomplete tasks with knowns validate |
| **Template System** | Code generation with Handlebars (.hbs) templates |
| **Import System** | Import docs and templates from git, npm, or local sources |
| **Memory System** | Project / session / global memory for persistent AI recall |
| **MCP Server** | Native AI assistant integration with full tool access |
| **AI Workspaces** | Multi-phase agent orchestration with worktree isolation |
| **Code Intelligence** | LSP-based symbols, definitions, references, diagnostics, and safe edits |
| **Web UI** | Kanban board, doc browser, knowledge graph, mermaid diagrams |
---
```bash curl -fsSL https://knowns.sh/script/install | sh
curl -fsSL https://knowns.sh/script/install | KNOWNS_VERSION=0.18.0 sh ```
```powershell irm https://knowns.sh/script/install.ps1 | iex
$env:KNOWNS_VERSION = "0.18.0"; irm https://knowns.sh/script/install.ps1 | iex ```
The shell installer on macOS/Linux and the PowerShell installer on Windows both auto-run knowns search --install-runtime after installing the binary. If that step fails, rerun it manually.
npm install -g knowns
npx knowns ```
git clone https://github.com/knowns-dev/knowns.git cd knowns make build # Output: bin/knowns make install # Install to GOPATH/bin ```
```bash
knowns setup agents # KNOWNS.md + AGENTS.md only knowns setup codex --global # user-level Codex MCP/skills/hooks knowns setup # full interactive project integration setup knowns sync ```
---
```bash
```
Now when your AI reads the project, it sees structured tasks with acceptance criteria, linked documentation, and clear definitions of done - instead of guessing.
---
You: /kn-init
Claude: [Reads README, ARCHITECTURE, checks task backlog]
"Project uses NestJS with CQRS pattern. 3 tasks in-progress..."
You: /kn-plan 42
Claude: [Takes task, reads refs, searches docs]
"Task #42: Add user authentication
## Implementation Plan
1. Review @doc/patterns/auth
2. Create AuthModule with JWT strategy
3. Add login/register endpoints
4. Write unit tests
Approve this plan?"
You: Yes
You: /kn-implement 42
Claude: [Follows plan, generates code, checks ACs]
"✓ AC1: User can login - DONE
✓ AC2: JWT token returned - DONE
✓ AC3: Tests pass - DONE
All acceptance criteria complete. Ready to commit?"
You: /kn-commit
Claude: [Creates conventional commit]
"feat(auth): add JWT authentication
- Add AuthModule with passport JWT strategy
- Add login/register endpoints
- Add unit tests (94% coverage)"
```bash
Create tasks with acceptance criteria, implementation plans, and status tracking. AI can read tasks, follow plans, check off ACs, and know exactly when work is complete.
knowns task create "Title" --ac "Criterion 1" --ac "Criterion 2"
knowns task edit <id> -s in-progress
knowns task edit <id> --check-ac 1
Full Model Context Protocol server. Claude, Cursor, and other MCP-compatible assistants get native access to tasks, docs, memory, search, and validation - no copy-pasting required.
When using Claude Code with Knowns, skills (slash commands) provide a guided workflow:
<p align="center"> <img src="./images/workflow.png" alt="Knowns AI Workflow" width="100%"> </p>
/kn-init -> Read project docs, understand context
/kn-plan -> Take task, gather context, create plan (wait for approval)
/kn-implement -> Follow plan, check ACs, track progress
/kn-commit -> Commit with conventional format
knowns是一个高质量的开源AI工作流项目
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
✅ MIT 协议 — 最宽松的开源协议之一,可自由商用、修改、分发,仅需保留版权声明。
经综合评估,AI工作流 在Agent工作流赛道中表现稳健,质量优秀。如果你已有明确的使用需求,可以直接上手体验;如果还在评估阶段,建议对比同类工具后再做决策。
| 原始名称 | knowns |
| Topics | aiai-agentsai-nativeai-workflowgo |
| GitHub | https://github.com/knowns-dev/knowns |
| License | MIT |
| 语言 | Go |
收录时间:2026-06-11 · 更新时间:2026-06-11 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。
选择 Agent 类型,复制安装指令后粘贴到对应客户端