resume-project-analyzer

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Transform codebases into authentic, interview-defensible resume project experience. Use when analyzing a codebase for: (1) Extracting resume-ready project descriptions, (2) Preparing for technical interview questions about past projects, (3) Understanding the engineering depth and value of a codebase, (4) Identifying defensible technical achievements. Prioritizes correctness and interview credibility over exaggeration.

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Sourcehubvue/skills
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NPX Install

npx skill4agent add hubvue/skills resume-project-analyzer

Tags

Translated version includes tags in frontmatter
resume-project-analyzercodebase-analysistechnical-interview-prepengineering-value-extractionresume-content-creation

SKILL.md Content

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Resume Project Analyzer

Core Principles

  • Do NOT fabricate achievements or metrics
  • Do NOT assume ownership or leadership without evidence
  • When information cannot be reliably inferred from code, ask reflective follow-up questions
  • Resume content must always be interview-defensible

Workflow

Follow this 5-step workflow to transform codebase analysis into authentic resume content.

STEP 1 — Project Analysis

Analyze the repository to understand the project's nature and technical scope.
Explore:
  • Use 
    Glob
    and 
    Grep
    to understand the codebase structure
  • Read key files: package.json, requirements.txt, go.mod, README, main entry points
  • Identify project type, tech stack, and architecture
Document:
  • Project type: backend, frontend, ML/AI, system, tool, library
  • Tech stack: languages, frameworks, infra, storage, concurrency patterns, ML tooling
  • Architecture: patterns, non-trivial components, integrations
  • Overall complexity: shallow, medium, or deep engineering depth
Output format:
## Project Analysis

- **Type**: [project type]
- **Tech Stack**: [list technologies]
- **Architecture**: [brief description]
- **Complexity**: [shallow/medium/deep]

STEP 2 — Engineering Value Extraction

Identify the real technical problems solved and visible constraints.
Look for:
  • Core technical problems: What is being solved? (performance, scalability, reliability, UX, data consistency)
  • Visible constraints: What shaped the design? (SLAs, scale requirements, browser support, regulatory requirements)
  • Engineering judgment indicators: Trade-offs, architecture choices, custom solutions vs libraries
Avoid:
  • Boilerplate code that doesn't require real engineering
  • Standard patterns without customization
  • Claims not supported by visible evidence
Output format:
## Engineering Value

- **Core Problems Solved**: [list]
- **Visible Constraints**: [list]
- **Engineering Decisions**: [list with evidence]

STEP 3 — Confidence Classification

For each inferred contribution, classify confidence level.
Use analysis_framework.md as reference.
LevelDefinitionWhen to Finalize
HIGHClearly supported by codeCan finalize immediately
MEDIUMReasonable but incomplete inferenceFinalize ONLY after user clarification
LOWCannot be inferred safelyFinalize ONLY after user confirmation
Rule: Do NOT finalize MEDIUM or LOW confidence claims without user input.

STEP 4 — Reflective Questioning (CRITICAL)

Before writing resume bullets, ask targeted questions to resolve uncertainty.
Question guidelines:
  • Be concrete and specific
  • Reflect real interviewer thinking
  • Help clarify responsibility, decisions, and impact
Good reflective questions:
  • "Which modules here were you responsible for end-to-end?"
  • "Was this design chosen due to performance issues or future scalability?"
  • "What scale was this system designed for, even if not fully reached?"
  • "What was the hardest technical trade-off you had to make?"
  • "Did you implement [specific feature] or was it already there?"
Avoid generic questions:
  • "What did you work on?" (too vague)
  • "Is this accurate?" (yes/no, doesn't provide context)
Ask only what is necessary to improve resume accuracy and interview readiness.

STEP 5 — Resume & Interview Output

After receiving user clarification, generate the final output.
Use resume_templates.md for phrasing guidance.
Use interview_defense.md for interview prep.

Output Format (Fixed)

Generate this exact structure:
## Project Summary
[1-2 concise sentences describing the project]

## Resume-Ready Project Experience
- [Bullet 1: action + what + how + outcome]
- [Bullet 2: action + what + how + outcome]
- [Bullet 3: ...]

## Key Technical Highlights
- [Architecture / algorithms / infra / tooling that demonstrate depth]
- [Specific patterns, optimizations, or design decisions]

## Interview Defense Preparation
- [Likely interviewer follow-up questions with suggested explanation angles]
- [Areas where user should prepare detailed explanations]

## Confidence Notes
- [Which claims are strongly supported by code (HIGH)]
- [Which claims rely on user-provided clarification (MEDIUM)]

Style Constraints

  • Sound like a real engineer, not marketing copy
  • Prefer: action + constraint + outcome
  • Be concise, technical, and honest
  • Optimize for interview credibility, not impressiveness

Weak Verbs to Avoid

  • "Responsible for"
  • "Participated in"
  • "Worked on"
  • "Helped with"
  • "Contributed to"

Strong Action Verbs

  • Built / Designed / Engineered / Developed / Created
  • Implemented / Integrated / Deployed / Delivered
  • Optimized / Improved / Accelerated / Streamlined
  • Scaled / Architected / Structured

Resources

references/analysis_framework.md

Detailed framework for:
  • Confidence classification
  • Engineering value extraction
  • Project type indicators
  • Depth assessment

references/resume_templates.md

Templates and guidelines for:
  • Project description patterns by type
  • Strong vs weak verbs
  • Effective resume formula

references/interview_defense.md

Interview preparation for:
  • Common follow-up questions
  • Answer strategies
  • STAR method
  • Confidence levels by question type