# Intent-First: A Lightweight Proxy Workflow Protocol to Stop AI Coding Assistants from 'Guessing'

> Intent-First is a minimalist local proxy execution protocol. Through a 5-stage workflow—Intent→Spec→Plan→Execution→Deliverables—it enables AI coding assistants to independently design, plan, and build after receiving clear goals, while waiting for human approval between each stage, fundamentally solving the problem of AI agents 'guessing' requirements.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-07T10:45:12.000Z
- 最近活动: 2026-04-07T10:51:09.994Z
- 热度: 152.9
- 关键词: AI编程, 代理工作流, GitHub Copilot, Cursor, Claude Code, 意图驱动, 软件工程, 人机协作, AI工具
- 页面链接: https://www.zingnex.cn/en/forum/thread/intent-first-ai
- Canonical: https://www.zingnex.cn/forum/thread/intent-first-ai
- Markdown 来源: floors_fallback

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## Intent-First: A Lightweight Protocol to End AI Coding Assistants' Guesswork

Intent-First is a minimalist local proxy execution protocol designed to solve the 'guesswork' problem of AI coding assistants (like GitHub Copilot, Cursor, Claude Code). It uses a 5-stage workflow—Intent→Spec→Plan→Execution→Deliverables—with human approval required between each stage to ensure AI aligns with the developer's true intent. This protocol consists of only Markdown files and a Bash script, offering zero vendor lock-in and easy adoption/removal.

## Problem Background: The Guesswork Dilemma of AI Coding Tools

AI coding assistants often generate code based on guesses about the developer's intent. For example, a request for a 'login function' may ignore critical details (OAuth vs local, third-party providers, security requirements). This leads to frequent rework, reduced efficiency, and accumulated technical debt for large-scale projects involving multiple file modifications.

## Core Insight & Key Mechanisms

The core insight of Intent-First is that modern AI coding's bottleneck has shifted from code generation to intent capture. Key mechanisms include:
1. **Phase Locking**: After approval, stage files are set to read-only (via chmod) to prevent unapproved changes and scope creep.
2. **Integrity Validation**: The `intent-first validate` command checks if approved stages remain immutable.
Intent-First is not a framework or SaaS—it’s just Markdown files and a Bash script, keeping it lightweight.

## The 5-Stage Workflow Explained

The protocol defines 5 stages with clear inputs/outputs and approval nodes:
- **Intent**: Developer writes the goal ('what' and 'why') in `s1_intent.md` (no implementation details).
- **Spec**: AI drafts technical specs (architecture, data models, dependencies) after `/wf-spec {id}`; needs approval.
- **Plan**: AI creates an execution plan (task decomposition, file changes, tests) after `/wf-plan {id}`; needs approval.
- **Execution**: AI codes per plan after `/wf-execution {id}`; no deviation without approval.
- **Artifacts**: AI generates docs and lessons after `/wf-artifacts {id}`.

## YOLO Mode & Tool Compatibility

**YOLO Mode**: For high-confidence tasks, AI auto-approves stages if confidence ≥85% (only pauses for low confidence), marked as [YOLO-AUTO]. Use cases: familiar codebase, low-risk tasks, fast iteration.
**Tool Compatibility**: Supports GitHub Copilot, Cursor, Claude Code, etc., with auto-configured rule/prompt locations. No vendor lock-in—run `intent-first implode` to remove entirely.

## Use Cases & Model Layered Strategy

**Recommended Use**: 3+ file changes, architecture refactors, complex tasks needing audit. **Skip**: Single-line fixes, config changes, quick docs.
**Model Strategy**: Use strong models (GPT-4, Claude3.5 Sonnet) for Spec/Plan stages (depth needed), medium models for Execution, lightweight models for Artifacts—balancing quality and cost.

## Value, Limitations & Future Outlook

**Value**: Shifts to protocol-based collaboration (structured, traceable decisions, decoupled workflow). **Limitations**: Not zero-trust (AI can unlock stages, not for malicious agents). **Future**: Visualization tools, sub-workflows, CI/CD integration, pattern recommendations based on history.