How the PUA Plugin Doubles AI Coding Efficiency by Eliminating Five Common Pitfalls

Project Background

The PUA (Pressure‑Uplift‑Automation) plugin is an MIT‑licensed open‑source project, currently at version v3. It integrates with nine mainstream AI coding platforms (Claude Code, OpenAI Codex CLI, Cursor, Kiro, CodeBuddy, VSCode (GitHub Copilot) and others) and targets five typical “lazy” AI behaviors to improve coding efficiency.

Core Pain Points

Violent retry : Repeating the same command three times before giving up.

Blaming the user : Shifting responsibility to environment/permissions without verification.

Tool idle : Possessing search/read/terminal capabilities but never invoking them.

Ineffective iteration : Re‑tuning the same line of code in a loop.

Passive waiting : Stopping after fixing surface issues without further validation.

Architecture

The plugin follows a three‑layer architecture: Pressure Layer + Methodology Layer + Execution Layer . Version v3 adds intelligent methodology routing and code‑level behavior detection, forming a closed‑loop system.

Red‑Line Constraints

Closed‑loop verification : Claims of “completion” must be backed by evidence; otherwise the task is considered incomplete.

Fact‑driven : Statements like “environment issue” must be verified before blaming.

Exhaustive solution : Declaring “cannot solve” triggers five methodology steps before the plugin gives up.

Pressure Escalation (L0‑L4)

Pressure increases with consecutive AI failures. Each level is paired with specific dialogue and actions, driving the AI from passive response to proactive investigation.

Methodologies

Thirteen problem‑solving frameworks are embedded, sourced from major tech companies such as Alibaba, ByteDance, Huawei, Tencent, Amazon, as well as approaches inspired by Elon Musk and Steve Jobs. Each methodology includes dedicated prompts and execution logic.

Smart Methodology Routing (v3)

After task ingestion, the plugin analyzes the task type (debug, build, research, architecture, etc.).

It automatically selects the optimal methodology (e.g., Huawei for debugging, Musk for building).

If consecutive failures occur, it switches to the next methodology in a predefined chain, never repeating a failed approach.

Code‑level hooks detect AI behavior and trigger mandatory execution without omission.

Performance

In nine real‑bug scenarios with eighteen comparative experiments (Claude Opus 4.6), the plugin achieved:

100% full‑coverage inspection in configuration‑review cases, catching issues such as Redis misconfigurations and CORS wildcard risks.

Significant reduction of repeated failed attempts, effectively doubling coding efficiency.

Installation

Universal (Vercel Skills CLI)

npx skills add tanweai/pua --skill pua-en

Claude Code

claude plugin marketplace add tanweai/pua
claude plugin install pua@pua-skills
# update
claude plugin marketplace update
claude plugin update pua@pua-skills
# manual trigger
/pua
/pua:p9   # technical lead mode
/pua:yes  # encouragement mode

VSCode (GitHub Copilot)

mkdir -p .github
cp vscode/copilot-instructions-zh-CN.md .github/copilot-instructions.md
cp vscode/prompts/pua-zh-CN.prompt.md .github/prompts/
# Enable the setting github.copilot.chat.codeGeneration.useInstructionFiles in VSCode.

Cursor (project‑level)

mkdir -p .cursor/rules
curl -o .cursor/rules/pua.mdc https://raw.githubusercontent.com/tanweai/pua/main/cursor/rules/pua.mdc

Usage Considerations

The plugin does not trigger on the first attempt failure or when a clear fix is already in progress.

Automatic activation occurs after two consecutive AI failures, when the AI says “cannot solve”, or when it blames the user/environment.

Multi‑language support: Chinese (default), English (pua‑en), Japanese (pua‑ja).

Claude Code offers the most comprehensive sub‑modes; other platforms support the core features.

Application Scenarios

Debugging & troubleshooting : Activates a 7‑item checklist to locate root causes such as server registration failures, API connection errors, SQLite locks.

Configuration audit & security review : Forces AI to uncover hidden issues like Redis misconfigurations, CORS wildcard risks, deployment script vulnerabilities.

Build & deployment : Applies Musk’s “simplify + automate” methodology to prune ineffective steps and optimize execution.

Multi‑agent team collaboration : Works as a “PUA executor” within Claude Code’s Agent Team, standardizing methodology across agents.

Data processing & API integration : Uses a search‑first methodology (inspired by Baidu) to gather documentation before proposing solutions.

Repository

GitHub:

https://github.com/tanweai/pua