Free Resume Reviewer

Comprehensive review and analysis of software engineering resumes in PDF format (frontend, backend, or ML domains). Use this skill when users ask to review,...

Security Scan

The skill's purpose and local script are coherent for PDF resume review, but it omits required runtime dependencies (python3 and the pdfplumber library) and has no install instructions—an inconsistency that should be clarified before use.

Purpose & Capability

The name, description, reference docs, and the included extract_pdf_text.py script align with a resume-PDF review tool. However, the skill requires running a Python script (python3) and a third-party package (pdfplumber) even though the registry metadata declares no required binaries or dependencies. That mismatch is unexpected.

Instruction Scope

SKILL.md restricts actions to: (1) always run the provided script to extract PDF text, (2) consult local reference files, and (3) generate feedback. It does not instruct network calls or reading unrelated system files. One scope consideration: the extraction script prints the entire resume text (which may contain sensitive PII); the skill will therefore process and output full resume contents as part of analysis.

Install Mechanism

There is no install spec. The provided script imports pdfplumber, a third-party Python package that must be installed in the runtime. Without an install step or declared dependency, the script will fail in many environments or an agent may attempt to install packages at runtime—this operational gap is an incoherence and an operational risk.

Credentials

The skill requests no environment variables, credentials, or config paths, which is appropriate for a local resume analysis tool. No extraneous secrets are requested.

Persistence & Privilege

The skill is not marked always:true and does not request elevated persistence. It does not modify other skills or system-wide settings per the provided files.

What to consider before installing

This skill appears to be a coherent resume reviewer, but confirm these before installing or running: (1) Ensure a Python 3 runtime is available—SKILL.md requires running `python3 scripts/extract_pdf_text.py`. (2) Install the pdfplumber package (e.g., pip install pdfplumber) or add an explicit install step; otherwise the script will fail or an agent might try to install packages at runtime. (3) Review the short extract_pdf_text.py script (it only uses pdfplumber and prints extracted text) to confirm you are comfortable with local execution and that no network exfiltration is present. (4) Be aware resumes contain sensitive PII; run the tool in a trusted/isolated environment and avoid sending raw extracted text to untrusted external services. If you plan to rely on this skill long-term, ask the publisher to add declared runtime dependencies and an install spec (python3 and pdfplumber) so the environment requirements are explicit.

Resume Reviewer for Software Engineers

This skill provides comprehensive analysis and feedback for software engineering resumes (frontend, backend, or ML domains) in PDF format, typically created with LaTeX.

Analysis Framework

Perform analysis in the following order:

1. Extract Resume Content

CRITICAL: Always use the provided script to extract PDF text:

python3 scripts/extract_pdf_text.py <path_to_resume.pdf>

This ensures consistent text extraction and proper handling of LaTeX-formatted PDFs.

2. Initial Assessment

Identify:

• Candidate's domain (frontend, backend, ML)
• Experience level (junior: 0-2 years, mid: 3-5 years, senior: 6+ years)
• Target role type (based on recent experience)
• Resume format (1-page or 2-page)

3. Technical Content Analysis

Read references/tech_trends.md to understand current technology landscape and evaluation criteria for the identified domain.

Evaluate:

Theme Identification:

• Primary technical focus areas
• Technology stack evolution over time
• Specializations or niche expertise
• Career trajectory and progression

Company and Impact Analysis:

• Quality and reputation of companies
• Scale of systems worked on (users, data volume, traffic)
• Cross-company skill progression
• Industry diversity or specialization

Technical Depth:

• Modern vs. outdated technology usage
• Alignment with current industry trends
• Breadth vs. depth of expertise
• Evidence of continuous learning

Major Contributions:

• Quantified business impact
• System design and architecture work
• Technical leadership indicators
• Open source or community contributions
• Cross-functional collaboration

Improvement Opportunities:

• Missing relevant technologies for target role
• Weak quantification of impact
• Lack of leadership/mentoring evidence
• Outdated technology focus
• Missing key skills for domain

4. Content Structure and Writing Quality

Read references/writing_quality.md for detailed grammar and style guidelines.

Evaluate:

Section Organization:

• Logical flow and hierarchy
• Section completeness (Experience, Skills, Education, etc.)
• Appropriate emphasis on relevant sections
• Optimal use of available space

Writing Quality:

• Action verb usage and strength
• Tense consistency
• Conciseness and clarity
• Grammar and punctuation
• Parallel structure in lists

Bullet Point Effectiveness:

• Impact-focused vs. responsibility-focused
• Specificity and quantification
• Business value communication
• Technical detail appropriateness

Formatting Consistency:

• Date formats
• Capitalization
• Punctuation style
• Technology name casing
• Number representation

5. ATS Compatibility Analysis

Evaluate:

Structure:

• Standard section headers
• Chronological organization
• Contact information placement
• Overall layout simplicity

LaTeX-Specific Issues:

• Multi-column layout problems
• Special characters or symbols
• Text extractability (verify with script output)
• Graphics or custom formatting

Keyword Optimization:

• Presence of relevant technical keywords
• Natural keyword integration
• Acronym definitions
• Industry-standard terminology

Formatting Risks:

• Tables or text boxes for critical content
• Headers/footers with important information
• Non-standard fonts
• Complex nested structures

6. Generate Comprehensive Feedback

Structure feedback as follows:

Overall Assessment

• 2-3 sentence summary of resume strength
• Primary domain and experience level confirmation
• Key differentiators or standout qualities

Strengths (What's Good)

• Specific examples of effective content
• Well-executed sections or bullet points
• Strong technical expertise demonstrated
• Effective quantification or storytelling
• Good formatting choices

Technical Content Recommendations

• Missing relevant modern technologies
• Opportunities to strengthen impact statements
• Suggestions for better technical positioning
• Areas to highlight or expand
• Technologies to add based on target roles

Content Structure and Writing Improvements

• Grammar or style issues with specific examples
• Bullet point enhancements with before/after examples
• Section reorganization suggestions
• Consistency fixes needed
• Conciseness improvements

ATS Optimization Recommendations

• Specific parsing risks identified
• Keyword additions or improvements
• Formatting changes for better compatibility
• Section header standardization

Priority Action Items

• Rank top 5-7 improvements by impact
• Quick wins vs. larger rewrites
• Critical issues vs. nice-to-haves

Output Format

Present feedback in clear, actionable format using markdown headers and bullet points. Use specific examples from the resume when citing issues or strengths. Provide before/after suggestions for concrete improvements.

Be encouraging and constructive while being honest about weaknesses. Frame criticism as opportunities for improvement.

Important Notes

• Always extract PDF text using the provided script first
• Consult reference files for domain-specific and writing guidelines
• Tailor feedback to candidate's experience level and target domain
• Focus on high-impact improvements first
• Provide specific, actionable recommendations with examples
• Consider both ATS parsing and human readability