aiwg Prompt模板 是 AI Skill Hub 本期精选AI工具之一。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。
aiwg Prompt模板 是一款基于 TypeScript 开发的开源工具,专注于 多智能体、Prompt模板、自主编程 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
aiwg Prompt模板 是一款基于 TypeScript 开发的开源工具,专注于 多智能体、Prompt模板、自主编程 等核心功能。作为 GitHub 开源项目,它拥有活跃的社区支持和持续的版本迭代,代码完全透明可审计,支持本地部署以保护数据隐私。无论是个人使用还是集成到企业工作流,都能提供稳定可靠的解决方案。
# 方式一:npm 全局安装 npm install -g aiwg # 方式二:npx 直接运行(无需安装) npx aiwg --help # 方式三:项目依赖安装 npm install aiwg # 方式四:从源码运行 git clone https://github.com/jmagly/aiwg cd aiwg npm install npm start
# 命令行使用
aiwg --help
# 基本用法
aiwg [options] <input>
# Node.js 代码中使用
const aiwg = require('aiwg');
const result = await aiwg.run(options);
console.log(result);
# aiwg 配置说明 # 查看配置选项 aiwg --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 AIWG_CONFIG="/path/to/config.yml"
<a href="https://aiwg.io"><img src="https://aiwg.io/assets/badges/aiwg-hero-dark.png" alt="AIWG — multi-agent AI framework · one source of truth · 10 platforms" width="680"></a>
aiwg sdlc-accelerate "AI-powered code review tool with GitHub integration"
aiwg-training for fine-tuning dataset curation (corpus-to-dataset pipeline with DPO/KTO/ORPO/SimPO export)---
aiwg mcp info ```
The MCP server exposes AIWG's artifact management, workflow execution, and project health capabilities as tools that any MCP-compatible AI platform can invoke programmatically.
---
AIWG ships five primitive artifact types. All are plain text:
/flow-security-review-cycle)Each is a single .md file with YAML frontmatter. Nothing executes until an AI platform reads it.
```bash
npm install -g aiwg
cd your-project aiwg use sdlc # Full SDLC framework (98 agents, 38 rules, 200+ templates) aiwg use forensics # Digital forensics & incident response (13 agents, 10 skills) aiwg use marketing # Marketing operations (37 agents, 87+ templates) aiwg use media-curator # Media archive management (6 agents, 9 commands) aiwg use research # Research workflow automation (8 agents, 8-stage pipeline) aiwg use rlm # RLM addon (recursive context decomposition) aiwg use all # Everything
aiwg doctor ```
aiwg use sdlc # Claude Code (default)
aiwg use sdlc --provider copilot # GitHub Copilot
aiwg use sdlc --provider cursor # Cursor
aiwg use sdlc --provider warp # Warp Terminal
aiwg use sdlc --provider factory # Factory AI
aiwg use sdlc --provider opencode # OpenCode
aiwg use sdlc --provider openai # OpenAI/Codex
aiwg use sdlc --provider windsurf # Windsurf
aiwg mcp install claude
Prerequisites: Node.js >=20.0.0 and an AI platform (Claude Code, GitHub Copilot, Cursor, Warp Terminal, or others). New installs should prefer Node 24. See Prerequisites Guide for details.
Verifying releases (v2026.5.3+): Every AIWG release ships with Sigstore-anchored npm provenance, a signed git tag, a cosign keyless tarball signature, and a signed CycloneDX SBOM. Verification is optional but recommended:Full walkthrough at> npm view aiwg@2026.5.3 --json | jq .dist.attestations >docs/releases/verifying.md. Adopt the same pattern for your own packages:docs/security/supply-chain-hardening.md.
/voice-analyze docs/existing-content.md
aiwg sdlc-accelerate --resume
Generates intake form, vision document, use cases, architecture baseline, risk register, test strategy, and deployment plan — all with human approval gates between phases.
**Dual-Track Iteration Model:**
┌─────────────────────────────────────────────────┐ │ ITERATION N │ │ │ │ Discovery Track Delivery Track │ │ (Next iteration) (Current iteration) │ │ │ │ ┌─────────────┐ ┌──────────────┐ │ │ │ Requirements│ │ Implement │ │ │ │ Research │ │ Test │ │ │ │ Design │ │ Review │ │ │ │ Validate │ │ Deploy │ │ │ └─────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ └────────────┬────────────┘ │ │ │ │ │ ┌──────▼──────┐ │ │ │ Iteration │ │ │ │ Assessment │ │ │ └─────────────┘ │ └─────────────────────────────────────────────────┘ ```
Metrics & Quality Tracking:
| Metric Category | Metrics Tracked |
|---|---|
| **DORA** (4) | Deployment Frequency, Lead Time, Change Failure Rate, MTTR |
| **Velocity** (3) | Story Points, Cycle Time, Throughput |
| **Flow** (3) | WIP Limits, Flow Efficiency, Blocked Items |
| **Quality** (13) | Test Coverage (4), Defect Metrics (4), Code Quality (3), Technical Debt (2) |
| **Operational** (16) | SLO/SLI (5), Infrastructure (4), Incidents (4), Cost (3) |
/curate "Pink Floyd"
┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
│ DISCOVER │───▶│ ACQUIRE │───▶│ DOCUMENT │───▶│ ARCHIVE │
│ │ │ │ │ │ │ │
│ Search │ │ Download │ │ RAG │ │ OAIS │
│ databases│ │ PDF │ │ summaries│ │ lifecycle│
│ Rank │ │ Metadata │ │ Citations│ │ FAIR │
│ results │ │ extract │ │ GRADE │ │ W3C PROV │
└──────────┘ └──────────┘ └──────────┘ └──────────┘
8-stage pipeline: Discovery → Acquisition → Documentation → Citation → Quality Assessment → Synthesis → Gap Analysis → Archival. Persistent REF-XXX identifiers. GRADE scoring (HIGH/MODERATE/LOW/VERY LOW). Unpaywall integration for open access papers.
---
AIWG includes a built-in MCP server for tool-based AI workflow integration:
```bash
aiwg use writes artifacts at one of two scopes. Both are first-class supported (see ADR-NUA-001 in .aiwg/studies/novice-user-adoption/):
aiwg use sdlc from a project root and the artifacts land in ./.claude/agents/, ./.claude/skills/, etc. One project's agent set never bleeds into another's session. This is the recommended default for most use cases.aiwg use sdlc --scope user writes to ~/.claude/agents/, ~/.claude/skills/, etc. Same artifact set loads into every session, regardless of project. Fits "AIWG in every conversation" workflows and is the canonical mode for OpenClaw and Hermes (whose primary discovery is user-scope).The trade-off is real: when the same agent set loads into every session, context from one project can bleed into reasoning about another. Research (REF-720, Lost in Multi-Turn Conversation, MSR/Salesforce 2025) measured a 39% capability drop when this happens. The non-blocking project-isolation warning surfaces the trade-off at deploy time so the scope choice is informed. Neither scope is wrong; pick the one that fits the workflow.
See docs/cli-reference.md (under aiwg use → "Scope models") for the per-provider details and the global-install rough-edge inventory.
/plugin marketplace add jmagly/ai-writing-guide
/plugin install sdlc@aiwg
/ai-pattern-detection docs/generated-content.md ```
---
aiwg 是一个基于 AI 驱动的软件开发生命周期(SDLC)加速工具。通过集成 GitHub,它能够根据描述自动生成并执行复杂的开发任务,旨在通过 AI 能力提升代码审查、架构设计及自动化流程的效率,让开发者从繁琐的重复劳动中解脱出来。
aiwg 拥有强大的 AI 能力矩阵,包含 188 个涵盖测试、安全、架构、DevOps、云原生、前后端及数据工程等领域的专业 Agents;提供 50 个 CLI 命令用于框架部署、项目脚手架搭建及指标验证;并内置 128 种 Workflow Skills,支持通过自然语言触发回归测试、取证分析及质量保障等自动化工作流。
aiwg 由五种基础原语构建:Agents(具备特定工具集的专业角色)、Skills(通过自然语言触发的自动化工作流)、Commands(显式斜杠命令)、Rules(会话强制执行指令)以及 Behaviors。用户可以通过 npm 进行全局安装:执行 `npm install -g aiwg` 即可快速完成部署。
在使用前,请确保环境已安装 Node.js >=20.0.0(推荐使用 Node 24),并配置好支持 MCP 协议的 AI 平台,如 Claude Code、GitHub Copilot、Cursor 或 Warp Terminal。对于 v2026.5.3+ 版本,所有发布版本均经过 Sigstore 签名与验证,确保了供应链的安全可靠。
aiwg 支持通过 MCP(Model Context Protocol)协议进行扩展。其 MCP server 可以将 AIWG 的产物管理、工作流执行及项目健康检查能力转化为工具,供任何兼容 MCP 的 AI 平台通过程序化方式调用,实现深度定制化配置。
aiwg 核心由六大组件构成,支持“双轨迭代模型”。例如使用 `aiwg sdlc-accelerate` 命令可以自动生成需求文档、架构基线、风险登记册及测试策略,并在每个阶段设置人工审批环节(Human Approval Gates),确保 AI 生成的内容符合预期并安全落地。
如果在执行 `npm i -g aiwg` 后提示找不到 `aiwg` 命令,通常是因为 npm 的全局 bin 目录未添加到系统的 PATH 环境变量中。你可以通过 `npm config get prefix` 获取全局路径,并使用 `export PATH="$(npm config get prefix)/bin:$PATH"` 将其添加到你的 shell 配置文件(如 .zshrc)中。
设计完整的多智能体认知架构,提供可复用Prompt模板,适合企业级自动化开发。代码质量优秀,社区关注度适中,值得推荐。
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,aiwg Prompt模板 在AI工具赛道中表现稳健,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 | aiwg |
| 原始描述 | 开源Prompt模板:Cognitive architecture for AI-augmented software development. Specialized agents。⭐133 · TypeScript |
| Topics | 多智能体Prompt模板自主编程Claude工作流自动化 |
| GitHub | https://github.com/jmagly/aiwg |
| License | MIT |
| 语言 | TypeScript |
收录时间:2026-05-18 · 更新时间:2026-05-19 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。