能力标签
ReqForge
💬
Prompt模板

ReqForge

基于 Shell · 专业级提示词模板,解锁 AI 的真实潜力
⭐ 7 Stars 🍴 1 Forks 💻 Shell 📄 MIT 🏷 AI 7.5分
7.5AI 综合评分
promptagent-harnessai-agentsai-coding
✦ AI Skill Hub 推荐

经 AI Skill Hub 精选评估,ReqForge 获评「推荐使用」。这款Prompt模板在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 7.5 分,适合有一定技术背景的用户使用。

📚 深度解析

ReqForge 是经过精心设计和实践验证的专业 Prompt 模板。Prompt 工程(Prompt Engineering)是充分发挥 Claude、ChatGPT 等大型语言模型潜力的关键技能,而一套经过优化的 Prompt 模板可以将 AI 输出质量提升数倍。

优质 Prompt 模板的核心价值在于其结构化设计:明确的角色设定、精确的任务描述、具体的输出格式要求和必要的边界条件,这些要素共同构成了一个能够持续产出高质量结果的 Prompt 框架。ReqForge 提供的模板经过反复迭代和用户验证,能够有效减少 AI 的"幻觉"(Hallucination)和输出不稳定问题。

无论你使用 Claude 3.5 Sonnet、GPT-4、Gemini 还是国内的文心一言、智谱 AI,优质的 Prompt 设计都能跨模型复用。AI Skill Hub 建议将本模板保存为个人 Prompt 库的标准组件,根据具体场景调整参数后反复使用,形成自己的 AI 提效工作流。

📋 工具概览

ReqForge 是经过精心设计和反复验证的专业 Prompt 模板集合。这些 Prompt 框架能够有效激活 Claude、ChatGPT 等大型语言模型的深层能力,让 AI 生成更准确、更有价值的输出结果。无需任何安装,直接复制模板内容到 AI 对话框即可使用。

GitHub Stars
⭐ 7
开发语言
Shell
支持平台
macOS / Linux
维护状态
轻量级项目,按需更新
开源协议
MIT
AI 综合评分
7.5 分
工具类型
Prompt模板
Forks
1

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

ReqForge 是经过精心设计和反复验证的专业 Prompt 模板集合。这些 Prompt 框架能够有效激活 Claude、ChatGPT 等大型语言模型的深层能力,让 AI 生成更准确、更有价值的输出结果。无需任何安装,直接复制模板内容到 AI 对话框即可使用。

📌 核心特色
  • 精心设计的 Prompt 框架,快速激活 AI 的深层能力
  • 支持参数化替换,灵活适配多种业务场景
  • 经过反复验证的指令结构,显著提升 AI 输出质量和一致性
  • 适用于 Claude、ChatGPT 等主流大语言模型
  • 可作为团队标准 Prompt 模板复用和二次开发
🎯 主要使用场景
  • 快速生成高质量的专业文案、分析报告或结构化内容
  • 利用 Prompt 框架引导 AI 解决特定领域的复杂问题
  • 在不同 AI 工具间复用经过验证的提示词模板
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# Prompt 无需安装,直接复制使用
# 支持:Claude / ChatGPT / Gemini / 通义千问 等主流模型

# 使用步骤
# 1. 复制 Prompt 模板内容
# 2. 粘贴到 AI 对话框
# 3. 替换 [占位符] 为实际内容
# 4. 发送后获取结构化输出

# 获取原始文件
git clone https://github.com/zxpmail/ReqForge
📋 安装步骤说明
  1. 复制本工具的 Prompt 模板内容
  2. 打开 Claude、ChatGPT 或其他 AI 对话工具
  3. 将 Prompt 粘贴到对话框开头
  4. 根据实际需求替换 [占位符] 中的内容
  5. 发送后 AI 将按照模板格式执行,获得结构化输出
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 粘贴到 Claude/ChatGPT 使用
# 示例 Prompt 结构:

你是一位 [角色],擅长 [领域]。
请根据以下要求完成任务:

任务背景:[描述背景]
具体要求:[详细说明]
输出格式:[期望格式]

# 将 [] 内内容替换为实际需求
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# reqforge 配置说明
# 查看配置选项
reqforge --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 REQFORGE_CONFIG="/path/to/config.yml"
📑 README 深度解析 真实文档 完整度 75/100 含工作流图 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

ReqForge

version license English 中文

From requirements to shippable products — a full AI-guided path for founders, PMs, and indie developers (Spec → Plan → Build → Review → Release).

Open-source Agent Harness for Claude Code, Cursor, and OpenCode — skills, hooks, memory, and evolution constrain the model so work stays verifiable, not just conversational.

Harness in one line: the model is the CPU; the harness is the OS — orchestration, memory, guardrails, and validation so work ships, not just chats. ReqForge targets requirements → shippable product (spec, code, release), not consumer “run after you close the chat” life automation. Maturity checklist → · Seven-layer map → · Loadout scenarios → · Platform compliance →

vs OpenSpec? One change at a time. ReqForge = requirements → product + Harness. OpenSpec → · vs Superpowers? TDD/subagents vs full pipeline. Superpowers → · vs Open Design? OD = mockups/preview; ReqForge = Spec→code (absorbs discovery checklist). Open Design → · vs Context7? Library docs injection; use with ReqForge. Context7 → · vs RTK? Shell output compression; optional with ReqForge. RTK → · vs nanochat? LLM training harness; Forge borrows golden-path / fast-loop discipline. nanochat → · vs autoresearch? Scoped edit + fixed budget + single metric; Forge maps to Spec/Plan lock + Primary metric. autoresearch → · vs llm-council? Multi-LLM peer review; Forge uses role-based council in code-review + spec Step 7. llm-council → · vs jobs? BLS data + LLM rubric batch scoring (occupations, not job queues); Forge maps to risk_rank + PROJECT-HEALTH. jobs → · vs LLM Wiki gist? raw/wiki/schema + ingest/query/lint; Forge maps to memory/ + ADR filing. llm-wiki → · Skill self-evolution papers? EmbodiSkill + SkillEvolver vs Forge feedback/evolution. Skill evolution →

No npm install required to use the framework — copy adapter files into your project and open your AI client. Node.js + pnpm are only needed if you contribute to this repo or run scripts/.

Overview

If you've done Vibe Coding, you know the hard part isn't getting AI to write code — it's managing the entire product development process. You tell AI "build me a writing tool," and it starts coding. Halfway through, you realize the direction is wrong and start over. Features finally work, but the UI looks terrible — no design specs, so AI pieced together default styles from training data. Fix the UI, introduce bugs. Fix bugs, introduce more bugs. Context gets long, AI forgets earlier requirements, code starts drifting.

The root cause isn't that models aren't smart enough. It's that there's no system around the model.

Forge is an Agent Harness — not about optimizing how you talk to AI, but building a complete system of constraints, guidance, and feedback. The AI knows what to do before it starts, automatically verifies results afterward, self-corrects when things go wrong, and never makes the same mistake twice.

Harness = Guides (feedforward) + Sensors (feedback) + Steering Loop (evolution)

  • Guides — Each Skill defines methodology, workflow, and acceptance criteria. Before the agent acts, it knows exactly "how to do it" and "what counts as done."
  • Sensors — Hook scripts + Code Review check every critical node after the agent acts. No reliance on the model's self-awareness.
  • Steering Loop — Every correction you give is recorded. When the same issue surfaces 3+ times, it's automatically promoted to a formal rule in the Skill.

---

What's New

Prerequisites

RequiredNotes
**AI client** (one of)[Claude Code](https://docs.anthropic.com/en/docs/claude-code), [Cursor](https://cursor.com), or [OpenCode](https://opencode.ai)
**Git**Clone this repo; optional for your own project
**Empty or existing project folder**Forge files live at the project root alongside your code
Optional (contributors only)Notes
Node.js 22.x LTS + pnpm 10.xRun pnpm test, pnpm sync, pnpm dep-graph — see [Framework Development](#framework-development)

Requirements depth: PM frameworks & Chain-of-Thought

Beyond the interview flow, product-spec-builder ships optional references (no extra Skills to install):

LayerWhatWhere
**PM frameworks**OST, JTBD value prop, assumption table, competitive brief — adapted from [pm-skills](https://github.com/phuryn/pm-skills) (MIT)core/skills/product-spec-builder/references/pm-frameworks-*.md → optional sections in Product-Spec.md
**Chain-of-Thought (CoT)**Think step-by-step before conclusions (tech choice, edge cases, self-critique); analysis vs implementation splitconversation-strategy.md; also implementer pre-code step, bug-fixer checklist, forge-bootstrap Iron Law 9

You do not need to type “think first” in every message — the Skill and session bootstrap apply the structure. See What's New → v1.25.0.

Installation & Usage

Forge is copy-to-use: no package publish, no npm install in your app project. You only need a supported AI coding assistant.

Step 2 — Install into your project

Option A — One-command install (recommended)

From your Forge clone (requires Node.js for ts-node):

```bash

Install into another project

pnpm forge-install claude-code --target /path/to/my-app

Install into current directory

pnpm forge-install cursor .

After installation — what appears in your project

my-app/
├── .claude/                    # or .cursor/ or .opencode/  ← adapter bundle
│   ├── CLAUDE.md               # control file (OpenCode: AGENTS.md)
│   ├── settings.json           # 10 hooks (Unix .sh); copy settings.windows.json on Windows
│   ├── skills/                 # 12 Skill definitions + commands/
│   ├── agents/                 # 10 Sub-agent definitions
│   ├── hooks/                  # .sh + .bat hook scripts
│   ├── loadouts/               # full | web-app | cli-tool | minimal
│   ├── feedback/               # evolution fuel (lessons learned)
│   ├── EVOLUTION.md            # evolution engine levels
│   └── rules/                  # Claude Code: .claude/rules/*.md; Cursor: .cursor/rules/*.mdc
├── Product-Spec.md             # after /product-spec-builder
├── DEV-PLAN.md                 # after /dev-planner
├── Design-Brief.md             # optional
├── changes/                    # optional — brownfield iterations (/change-manager)
│   └── archive/
├── memory/                     # auto-created on first /dev-builder
│   ├── project-memory.md
│   ├── decisions-log.md
│   └── task-history.md
└── <project-name>/ ...         # your application code (not flat in root)

Forge does not modify your package.json unless you ask the agent to add dependencies during development.

Quick Start Mode

Don't want the full interview? Just describe your project in one sentence:

You: "A habit tracker app with AI coaching"
Forge: ⚡ Quick Spec generated! Items marked [待确认] are my best guesses.

AI infers everything — product type, target users, core features, tech stack, layout. Uncertain items default to the simpler option and are marked for your review. Switch to deep-dive mode anytime with /product-spec-builder.

Merge upgrade (keeps your feedback/ and settings.local.json)

pnpm forge-install claude-code --target ../my-app --force

powershell

Step 3b — Loadouts (optional)

Adapters ship 4 loadout bundles under loadouts/ (full, web-app, cli-tool, minimal). Each JSON lists recommended skills, agents, and hooks for a project type.

Not sure which one? See loadout-scenarios.md — scenario → loadout → first Skill command.

You want to…Loadout
New web app (spec → design → ship)web-app
One feature on existing codefull or web-app + /change-manager
CLI / librarycli-tool
Quick spike / scriptminimal
  • Default installfull loadout (all hooks in settings.json).
  • Trim hooks (contributors, from Forge clone): pnpm apply-loadout minimal claude-code merges a lighter hook set into adapter settings.json. Add --dry-run to preview.
  • Loadouts are reference manifests — skills/agents are already copied; use loadouts to understand what each bundle includes.
  • Brownfield (/change-manager): included in full and web-app only; cli-tool and minimal omit it — copy the skill from core/skills/change-manager/ or switch loadout if you need changes/ on a CLI project.

Workflow

  1. Describe your idea/product-spec-builder interviews you (or Quick Mode for one sentence). For fuzzy ideas, optional PM discovery (OST, assumptions) and CoT templates improve Spec quality before any code.
  2. Generate spec — Outputs Product-Spec.md (may include optional JTBD, metrics, competitors, assumptions sections) → user confirms → .forge/spec-confirmed.json
  3. Design brief (optional) — Invoke /design-brief-builder
  4. Design mockups (optional) — Invoke /design-maker
  5. Development plan — Invoke /dev-planner, outputs DEV-PLAN.md
  6. Build — Invoke /dev-builder, works through each Task in each Phase
  7. Memory auto-update — After each Task, project memory is updated automatically
  8. Auto-review — code-reviewer parallel agent review + confidence aggregation
  9. Auto-fix — Failed review triggers bug-fixer automatically
  10. Commit & push — Review passes → auto commit + push
  11. Phase verification — Cross-Task integration check + compile + functional test
  12. Iterate — Request changes in conversation; auto-update Spec → Plan → code → review
  13. Brownfield feature (optional, when Spec already exists) — /change-manager propose <name> → fill changes/<name>/ → apply (dev-planner/dev-builder scoped) → verify → archive
  14. Release — Invoke /release-builder

Research & comparisons

External harnesses reviewed for positioning (not dependencies):

ProjectFocusForge doc
[OpenSpec](https://github.com/Fission-AI/OpenSpec)Spec-driven changes/ + CLI[openspec-comparison.md](core/docs/openspec-comparison.md) — absorbed via /change-manager
[Superpowers](https://github.com/obra/superpowers)Skills + TDD + subagent-driven development[superpowers-comparison.md](core/docs/superpowers-comparison.md) — skill/TDD discipline absorbed in dev-builder
[Open Design](https://github.com/nexu-io/open-design)Design artifacts, preview, design systems[open-design-comparison.md](core/docs/open-design-comparison.md) — discovery/presets/anti-slop in design skills
[OpenHuman](https://github.com/tinyhumansai/openhuman)Personal AI runtime, Memory Tree, integrations[openhuman-comparison.md](core/docs/openhuman-comparison.md) — optional memory backends, context rules
[RTK](https://github.com/rtk-ai/rtk)Shell output compression (PreToolUse bash proxy)[rtk-comparison.md](core/docs/rtk-comparison.md) — optional layer 5 partner; tee-style verify evidence
[nanochat](https://github.com/karpathy/nanochat)End-to-end LLM training harness (speedrun, leaderboard)[nanochat-comparison.md](core/docs/nanochat-comparison.md) — golden path / fast-loop discipline (methodology only)
[autoresearch](https://github.com/karpathy/autoresearch)Autonomous LLM training experiments (scoped edit, val_bpb)[autoresearch-comparison.md](core/docs/autoresearch-comparison.md) — Primary metric + Spec/Plan lock + Task micro-cycle
[llm-council](https://github.com/karpathy/llm-council)Multi-LLM peer review + Chairman synthesis[llm-council-comparison.md](core/docs/llm-council-comparison.md) — code-review council + spec Step 7
[jobs](https://github.com/karpathy/jobs)BLS occupation data + LLM rubric scoring (not task queues)[jobs-comparison.md](core/docs/jobs-comparison.md) — risk_rank + PROJECT-HEALTH + Spec LLM-judge
[LLM Wiki gist](https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f)Persistent wiki: raw/schema + ingest/query/lint[llm-wiki-comparison.md](core/docs/llm-wiki-comparison.md) — memory/ + ADR filing discipline
[andrej-karpathy-skills](https://github.com/multica-ai/andrej-karpathy-skills)**4 principles**: Think Before Coding, Simplicity First, Surgical Changes, Goal-Driven (154k ★)[karpathy-skills-comparison.md](core/docs/karpathy-skills-comparison.md) — behavior-rules.md + Karpathy Discipline in every Skill
Founder's Playbook (PDF)Idea → MVP → Launch → Scale; validation-before-build[founders-playbook-comparison.md](core/docs/founders-playbook-comparison.md) — Idea Validation Gate + DEV-PLAN MVP Scope

ReqForge maintainer docs (not third-party comparisons):

TopicDoc
Which loadout when[loadout-scenarios.md](core/docs/loadout-scenarios.md)
GitHub Actions & fork policy[platform-compliance.md](core/docs/platform-compliance.md)
Release gate (contributors)pnpm forge-smoke · [scripts/forge-smoke/README.md](scripts/forge-smoke/README.md) · [forge-smoke.yml](.github/workflows/forge-smoke.yml)
Golden path demo[test-demo/README.md](test-demo/README.md) · pnpm test-demo-golden-path (todo-cli/ = Spec+Plan artifact, not framework CLI)
Agent execution discipline (8 rules)[session-execution-discipline.md](core/docs/session-execution-discipline.md) · agents-template.md § Agent 执行纪律
Founder's Playbook ↔ Forge gates[founders-playbook-comparison.md](core/docs/founders-playbook-comparison.md)

---

🇨🇳 中文文档镜像 AI 翻译 2026-06-09
英文原文章节由系统翻译为中文摘要,便于快速理解。完整原文见上方 "📑 README 深度解析"。
📌 简介

如果您已经完成过 Vibe Coding,那么您知道产品开发过程的困难之处不在于让 AI 编写代码,而在于管理整个产品开发过程。您告诉 AI "构建一个写作工具",它开始编码。然而,在途中,您意识到方向是错误的,开始重头开始。功能最终工作,但 UI 看起来很糟糕——没有设计规范,所以 AI 从训练数据中拼凑了默认样式。修复这些问题需要大量的时间和精力。

⚡ 功能介绍

ReqForge 的新功能

📋 环境依赖

环境依赖与系统要求:需要 AI 客户端(Claude Code、Cursor 或 OpenCode)和 Git

🛠 安装步骤(Docker/pip/源码)

安装步骤:从源码克隆本项目,使用支持的 AI 编码助手(如 Claude Code)

🚀 使用教程

使用教程:快速启动模式(只需一句话描述项目),深入模式(详细的产品开发流程)

⚙️ 配置说明(含 MCP / env)

配置说明:Merge 升级(保留反馈和设置)

🔄 工作流/模块

工作流:描述您的想法,生成产品规范,输出 Product-Spec.md 文件

🎯 aiskill88 AI 点评 A 级 2026-05-26

高质量的开源Prompt模板,值得使用

📚 实用指南(长尾问题)
适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 想快速复用高质量提示词模板的 AI 用户
最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效
部署方案
  • CLI:直接 npm install -g / pip install,命令行调用
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
ReqForge 中文教程ReqForge 安装报错怎么办ReqForge MCP 配置ReqForge Agent 工作流ReqForge 与同类工具对比ReqForge 最佳实践ReqForge 适合谁用

⚡ 核心功能

👥 适合谁
  • 需要让 Claude / Cursor 操作本地工具的 AI 工程师
  • 构建多智能体协作系统的 Agent 开发者
  • 想快速复用高质量提示词模板的 AI 用户
⭐ 最佳实践
  • 配置 MCP 服务器时建议使用 stdio 传输 + JSON-RPC,避免暴露公网
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • MCP 配置路径拼错或权限不足,重启 Claude Desktop 才生效

👥 适合人群

内容创作者和自媒体人职场人士和学生ChatGPT / Claude 重度用户希望提升 AI 使用效率的普通用户

🎯 使用场景

  • 快速生成高质量的专业文案、分析报告或结构化内容
  • 利用 Prompt 框架引导 AI 解决特定领域的复杂问题
  • 在不同 AI 工具间复用经过验证的提示词模板

⚖️ 优点与不足

✅ 优点
  • +MIT 协议,可免费商用
  • +无需安装,立即可用
  • +适配所有主流 AI 工具
  • +经社区验证的最佳实践
⚠️ 不足
  • 效果依赖使用者对 Prompt 工程的熟悉程度
  • 不同模型和版本的响应效果可能存在差异
  • 复杂场景需结合实际需求二次调整
⚠️ 使用须知

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。

📄 License 说明

✅ 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。

🔗 相关工具推荐

📚 相关教程推荐
📰 相关 AI 新闻
🍿 AI 圈相关吃瓜
🗺️ 相关解决方案
🧩 你可能还需要
基于当前 Skill 的能力图谱,自动补全的工具组合

❓ 常见问题 FAQ

ReqForge 是一款Shell开发的AI辅助工具。开源Prompt模板:ReqForge — From requirements to shippable products. Open-source Agent Harness fo。⭐7 · Shell 主要应用场景包括:快速生成可交付产品。
💡 AI Skill Hub 点评

AI Skill Hub 点评:ReqForge 的核心功能完整,质量良好。对于内容创作者和自媒体人来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。

⬇️ 获取与下载
⬇ 下载源码 ZIP

✅ MIT 协议 · 可免费商用 · 直接从 aiskill88 服务器下载,无需跳转 GitHub

📚 深入学习 ReqForge
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 ReqForge
原始描述 开源Prompt模板:ReqForge — From requirements to shippable products. Open-source Agent Harness fo。⭐7 · Shell
Topics promptagent-harnessai-agentsai-coding
GitHub https://github.com/zxpmail/ReqForge
License MIT
语言 Shell
🔗 原始来源
🐙 GitHub 仓库  https://github.com/zxpmail/ReqForge 🌐 官方网站  https://github.com/zxpmail/ReqForge#readme

收录时间:2026-05-26 · 更新时间:2026-05-30 · License:MIT · AI Skill Hub 不对第三方内容的准确性作法律背书。