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Case 04453

Skill knownasnaffy/prompthound/dataset/case_04453

A fast, offline static risk analysis CLI for AI agent skill files. Detects malicious instructions, steganographic payloads, and dangerous capability chains.

Install
npx -y skills add knownasnaffy/prompthound --skill case_04453

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What its author says it does

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Comprehensive review and analysis of software engineering resumes in PDF format (frontend, backend, or ML domains). Use this skill when users ask to review, analyze, provide feedback on, or evaluate a resume PDF. The skill analyzes technical content quality, career progression, ATS compatibility, grammar, structure, and provides actionable improvement suggestions based on current industry trends.

SKILL.md

5.7 KB, ~1.0k tokens by cl100k_base, as published. Nobody here has run it

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  • Modern vs. outdated technology usage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

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.