Master Structured Prompts and Reusable AI Skills

Transition from ad-hoc chat prompts to durable, modular SKILL.md packages that equip your AI agents with repeatable capabilities.

Mission Explanation Voice:English (Global Tech)

Mission 05Intermediate to Advanced~3 Hours

100% Free Local Skills

The Expected Outcome

A modular SKILL.md package enforcing web accessibility, mobile responsiveness, and clean code direction rules across coding agent sessions.

Tools Used:

ClaudeClaude CodeAntigravity SkillsMarkdown

Required Accounts (Prerequisites):

Anthropic Account or Code Editor

The Project Brief

Problem Statement:

Chat histories vanish across sessions and agents forget instructions. Encapsulating domain knowledge into SKILL.md files builds durable institutional memory.

Target User:

Engineers wanting to standardize audits, refactors, and test generation across distributed agent sessions.

Acceptance Criteria:

Strict YAML frontmatter specifying clear trigger conditions and scope
Actionable step sequences with clear stop conditions
Autonomous execution by Claude Code or Antigravity without repetitive prompt guidance

Master Prompt

Copy and paste this structured prompt into your AI model (Claude or Google AI Studio):

--- name: web-quality-auditor description: Audit web pages for mobile responsiveness, Urdu typography integrity, contrast, and form validation. --- # Web Quality Auditor Skill When invoked, perform a comprehensive 4-stage audit: 1. Inspect viewport scaling and RTL directional consistency. 2. Check that English technical terms are safely isolated in LTR containers. 3. Validate button contrast and accessibility focus indicators. 4. Output a prioritized markdown report with code diffs for immediate fixing.

Step-by-Step Build Instructions

Follow each step carefully and use the refinement prompts if needed.

Structure Skill Folder and Author YAML Frontmatter

Step 1

Create a .agents/skills/web-quality-auditor/SKILL.md file with precise frontmatter describing trigger conditions.

ProTip: ProTip: Keep edits small and iterative.

Specify Negative/Positive Examples and Critical Invariants

Step 2

Add explicit invariants and negative examples preventing common failure modes like unwanted refactor cascades.

ProTip: ProTip: Keep edits small and iterative.

Invoke Skill via Agent and Verify Output Quality

Step 3

Trigger the skill inside your agent terminal and verify that output strictly adheres to the requested checklist format.

ProTip: ProTip: Keep edits small and iterative.

Iteration & Refinement Tips

Structure your prompts with clear XML delimiter tags (<context>, <task>, <rules>, <output_format>) for deterministic model reasoning.
Include few-shot positive and negative examples to anchor the output distribution.

Testing & Verification Checklist

Check each item before publishing your project to ensure everything works smoothly:

Launch Guidance

Publish & Share Your Project

You have built a calibrated production prompt and reusable agent skill. Export and deploy across your team or toolchain.

Next Steps:

Package your prompt template into an MCP tool server for local agent integration.
Build automated regression evals to track prompt accuracy over time.

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