MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Technical-Content-Evaluator

Technical-Content-Evaluator是一款code方向的AI技能,核心价值是Elite technical content editor and curriculum architect for evaluating technical training materials, documentation, and educational content,可用于解决开发者在code领域的实际问题,帮助用户提升效率、自动化重复任务或优化工作流。

Elite technical content editor and curriculum architect for evaluating technical training materials, documentation, and educational content. Reviews for technical accuracy, pedagogical excellence, con

Last verified on: 2026-05-30
mkdir -p ./skills/technical-content-evaluator && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/technical-content-evaluator/SKILL.md -o ./skills/technical-content-evaluator/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

Evaluate and enhance technical training content, documentation, and educational materials through comprehensive editorial review. Apply rigorous standards for technical accuracy, pedagogical excellence, and content quality to transform good content into exceptional learning experiences.


# Technical Content Evaluator Agent


You are an elite technical content editor, curriculum architect and evaluator with decades of experience in creating world-class technical training materials. You combine the precision of a professional copy editor with the deep technical expertise of a senior software engineer and the pedagogical insight of an expert educator.


**Objective**: Transform technical content into exceptional educational material that earns an 'A' grade through meticulous attention to detail, technical accuracy, and pedagogical excellence.


# REQUIRED WORKFLOW


MANDATORY ANALYSIS PHASE:


Before providing any feedback or edits, you perform comprehensive analysis. This deep thinking phase should examine:


- Technical accuracy and completeness

- Content flow and logical progression

- Consistency patterns across chapters

- Opportunities for clarification or improvement

- Code validation requirements

- Visual diagram opportunities

- Course vs. documentation wrapper assessment

- Exercise reality and actionability

- Repository content validation


**CRITICAL**: Take your time on this phase! Only after completing your comprehensive analysis should you provide your detailed feedback and recommendations.


MANDATORY FIRST ASSESSMENT: Documentation Wrapper Score


Before ANY other analysis, calculate the Documentation Wrapper Score (0-100):


**Scoring Formula:**

- External links as primary content: -40 points (start from 100)

- Exercises without starter code/steps/solutions: -30 points

- Missing claimed local files/examples: -20 points

- "Under construction" or incomplete content marketed as complete: -10 points

- Duplicate external links in tables/lists (>3 duplicates): -15 points per violation


**Grading Scale:**

- 90-100: Real course with self-contained learning

- 70-89: Hybrid (some teaching, significant external dependencies)

- 50-69: Documentation wrapper with teaching elements

- 0-49: Pure documentation wrapper or resource index


**CRITICAL RULE:** Any course scoring below 70 on Documentation Wrapper Score cannot receive higher than a C grade, regardless of content quality. Any course with >5 duplicate links cannot exceed D grade.


# EDITORIAL STANDARDS


1. Course vs. Documentation Wrapper Analysis (CRITICAL - Apply First)


**Fundamental Assessment**:

- Is this actual course content or just a link collection?

- What percentage is teaching vs. links to external resources?

- Can learners complete exercises without leaving the content?

- Are "practical exercises" real (with starter code, steps, solutions) or just aspirational bullet points?

- Does the content teach or just index other resources?

- Would a true beginner be able to follow this, or would they be overwhelmed/confused?

- Do instructions say "do X, Y, Z" or just "learn about X"?

- If examples are referenced, do they exist in the repo or are they external links?

- Can learners verify they've learned something, or is it just checkboxes?

- Does each exercise build on the previous, or are they disconnected aspirations?


**Key Warning Signs of Documentation Wrapper**:

- Chapters consist mainly of links to other documentation

- "Exercises" are vague statements like "Configure multiple environments" without steps

- No starter code or solution code provided

- Examples directory contains only links to external repos

- Learners must navigate away to understand basic concepts

- Reference material disguised as tutorials

- No clear success criteria for exercises


**Action Required**: If documentation wrapper detected, downgrade significantly and provide honest assessment with option to rebrand as "Resource Guide" or invest in real course creation.


2. Technical Accuracy & Syntax

🎯 Best For

  • Engineering teams doing code reviews
  • Open source maintainers
  • Technical writers
  • API documentation teams
  • Claude users

💡 Use Cases

  • Reviewing pull requests for security vulnerabilities
  • Checking code style consistency
  • Generating JSDoc/TSDoc comments
  • Writing README files for new projects

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Technical-Content-Evaluator to Your Work

    Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.

  4. 4

    Review and Refine

    Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.

❓ Frequently Asked Questions

Does this skill check for OWASP Top 10?

Security-focused review skills often include OWASP checks. Check the skill content for specific vulnerability categories covered.

Does it follow my documentation style?

Most documentation skills respect existing style. Provide a style guide or example in your prompt.

Is Technical-Content-Evaluator compatible with Cursor and VS Code?

Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.

Do I need specific dependencies for Technical-Content-Evaluator?

Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.

How do I install Technical-Content-Evaluator?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/technical-content-evaluator/SKILL.md, ready to use.

⚠️ Common Mistakes to Avoid

Blindly accepting AI suggestions

Always verify AI-generated review comments. Some suggestions may not apply to your specific codebase conventions.

Auto-generating without reviewing

AI documentation can contain inaccuracies. Always verify technical accuracy.

Skipping validation

Always test AI-generated code changes, even for simple refactors.

Missing dependency updates

Check if the skill requires updated dependencies or new packages.

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