Do NOT write code, create files, run commands, or take any implementation action. Your ONLY output is an analysis plus an optimized prompt.

If the user says "just do it", "直接做", or "don't optimize, just execute", do not switch into implementation mode inside this skill. Tell the user this skill only produces optimized prompts, and instruct them to make a normal task request if they want execution instead.

Run this 6-phase pipeline sequentially. Present results using the Output Format below.

Analysis Pipeline

Phase 0: Project Detection

Before analyzing the prompt, detect the current project context:

1. Check if a CLAUDE.md exists in the working directory — read it for project conventions
2. Detect tech stack from project files:
   • package.json → Node.js / TypeScript / React / Next.js
   • go.mod → Go
   • pyproject.toml / requirements.txt → Python
   • Cargo.toml → Rust
   • build.gradle / pom.xml → Java / Kotlin (then check for quarkus in build file → Quarkus, or spring-boot → Spring Boot)
   • Package.swift → Swift
   • Gemfile → Ruby
   • composer.json → PHP
   • *.csproj / *.sln → .NET
   • Makefile / CMakeLists.txt → C / C++
   • cpanfile / Makefile.PL → Perl
3. Note detected tech stack for use in Phase 3 and Phase 4

If no project files are found (e.g., the prompt is abstract or for a new project), skip detection and flag "tech stack unknown" in Phase 4.

Phase 1: Intent Detection

Classify the user's task into one or more categories:

Phase 2: Scope Assessment

If Phase 0 detected a project, use codebase size as a signal. Otherwise, estimate from the prompt description alone and mark the estimate as uncertain.

…

santa-method

通过双评审智能体对结果进行对抗式校验，提升输出发布前的可靠性

adapt-skill

将其他 AI 编码助手的技能规范转换为 Amplifier 原生 SKILL.md 文件