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。
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。
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。
# 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
# 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
# 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"
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/.
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)
---
| Required | Notes |
|---|---|
| **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.x | Run pnpm test, pnpm sync, pnpm dep-graph — see [Framework Development](#framework-development) |
Beyond the interview flow, product-spec-builder ships optional references (no extra Skills to install):
| Layer | What | Where |
|---|---|---|
| **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 split | conversation-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.
Forge is copy-to-use: no package publish, no npm install in your app project. You only need a supported AI coding assistant.
Option A — One-command install (recommended)
From your Forge clone (requires Node.js for ts-node):
```bash
pnpm forge-install claude-code --target /path/to/my-app
pnpm forge-install cursor .
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.
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.
pnpm forge-install claude-code --target ../my-app --force
powershell
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 code | full or web-app + /change-manager |
| CLI / library | cli-tool |
| Quick spike / script | minimal |
full loadout (all hooks in settings.json).pnpm apply-loadout minimal claude-code merges a lighter hook set into adapter settings.json. Add --dry-run to preview./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./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.Product-Spec.md (may include optional JTBD, metrics, competitors, assumptions sections) → user confirms → .forge/spec-confirmed.json/change-manager propose <name> → fill changes/<name>/ → apply (dev-planner/dev-builder scoped) → verify → archiveExternal harnesses reviewed for positioning (not dependencies):
| Project | Focus | Forge 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):
| Topic | Doc |
|---|---|
| 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) |
---
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。
ReqForge 的新功能
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: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: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:Merge 升级(保留反馈和设置)
工作流:描述您的想法,生成产品规范,输出 Product-Spec.md 文件
高质量的开源Prompt模板,值得使用
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。
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。
| 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 |
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。
选择 Agent 类型,复制安装指令后粘贴到对应客户端