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10x-stack-assess

Assess a project's stack against four agent-friendliness quality gates.

Updated May 25, 2026
Search Tags:#quality gates#project assessment#agent-friendliness#stack assessment#compensation strategies
One-click install
npx skills add https://github.com/bvshi/fodswap --skill 10x-stack-assess-bvshi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: 10x-stack-assess
Source: https://github.com/bvshi/fodswap/tree/main/.agents/skills/10x-stack-assess
Command: npx skills add https://github.com/bvshi/fodswap --skill 10x-stack-assess-bvshi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill assesses the agent-friendliness of an existing project's stack against four quality gates, providing a detailed report with scores, gaps, and compensation strategies.

Core Features & Use Cases

  • Stack Assessment: Evaluate a project's stack against typed, convention-based, popular, and well-documented quality gates.
  • Compensation Strategies: Document concrete compensation strategies for each failed gate to improve agent-friendliness.
  • Automated Report: Generate a structured report with per-component scores, gaps, and ready-to-paste AGENTS.md entries.
  • Use Case: For a brownfield project, use this Skill to ensure your stack is optimized for AI agent workflows and identify areas for improvement.

Quick Start

Run the /10x-stack-assess skill to evaluate your project's stack and generate an assessment report.

Frequently Asked Questions about 10x-stack-assess

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I evaluate my project stack for AI agent readiness?▼

Agent-friendliness quality gates are typed, convention-based, popular, and well-documented standards that an existing codebase is evaluated against. Scoring these gates identifies structural gaps and generates compensation strategies to improve AI agent workflows.

How do I assess a brownfield codebase for agent-friendliness?▼

Assess a brownfield codebase by running an evaluation against established quality gates using recognizable project markers and optional PRD context. This produces a structured report with per-component scores and ready-to-paste AGENTS.md entries to bridge existing gaps.

Can I generate an AGENTS.md file from an existing project's stack gaps?▼

Yes, you can generate an AGENTS.md file from stack gaps by evaluating the project against quality gates. The resulting assessment provides ready-to-paste AGENTS.md entries that document concrete compensation strategies for each failed gate.

What are compensation strategies for failed agent-friendliness quality gates?▼

Compensation strategies for failed agent-friendliness quality gates are concrete, documented actions generated in an assessment report. They provide specific methods to overcome structural stack deficiencies and optimize the project for AI agent workflows.

Does stack assessment require a PRD or specific project markers?▼

Stack assessment requires an existing codebase with recognizable project markers, while PRD context is optional. Providing PRD context alongside the project markers allows the evaluation to generate a more targeted report with per-component scores and compensation strategies.

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