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Resume ATS Optimizer

Validate resume parsing, map job requirements to evidence, and improve role-specific machine readability without inventing universal ATS scores.

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splash0047/resume-generator34 stars(GitHub 34 + cccskills 0)
Works withAWS

Run score Not tested

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SKILL.md (original)

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Resume ATS Optimizer

When to Use This Skill

Use this skill when the user wants to:

  • optimize a resume for a specific job;
  • diagnose low response rates;
  • check whether a PDF/DOCX is likely to parse cleanly;
  • identify missing required qualifications or weak evidence;
  • compare a resume against a job description.

Do not present an internal heuristic as the employer's ATS score or claim that a particular percentage guarantees progression.

Core Principle

Treat resume screening as a sequence of distinct checks:

  1. Eligibility — graduation batch, degree, location/work authorization, availability, required years of experience, and other hard application conditions.
  2. Parsing — can the submitted file be extracted in a sensible reading order?
  3. Requirement evidence — does the resume show credible evidence for the job's required and preferred criteria?
  4. Human readability — can a recruiter quickly identify role fit, recent education/experience, and relevant projects?
  5. Downstream stages — assessments and interviews are separate from resume quality and must be diagnosed separately.

Different employers configure different workflows. Never imply that all ATS products rank candidates using keyword density.

Step 1 — Eligibility Gate

Extract explicit conditions from the job description and application form. Classify each as:

  • Hard requirement — missing it may make the application ineligible.
  • Preferred qualification — useful but not automatically disqualifying.
  • Unknown / needs confirmation — wording is ambiguous.

Examples: expected graduation year, student status, degree discipline, location, work authorization, internship dates, minimum professional experience.

If a hard requirement is not met, state that clearly before optimizing wording.

Step 2 — Parsing Validation

For the actual exported file:

  • verify it is text-based rather than scanned;
  • extract the text and inspect reading order;
  • confirm name, contact details, education, dates, skills, links, and section headings survive extraction;
  • check whether application fields populated from the upload are correct;
  • avoid graphics, photos, text boxes, complex tables, multi-column layouts, and contact details placed only in headers/footers;
  • use the employer's requested file format.

A parser warning is not automatically a rejection. Report exactly what failed or was lost.

Step 3 — Requirement-to-Evidence Matrix

Build a table with these columns:

Job criterionTypeEvidence in resumeEvidence strengthAction
PythonRequiredRoot Cause Analyzer + internshipDirectKeep and lead with it
AWSPreferredAWS re/Start credential onlyIndirectDo not imply production AWS ownership
2+ years experienceRequiredStudent internships onlyMissingEligibility risk

Evidence strength:

  • Direct — implemented, used, measured, or owned in a project/experience with supporting detail.
  • Indirect — coursework, certification, README claim, or adjacent experience.
  • Missing — no truthful evidence available.

Do not add a keyword unless it accurately describes the candidate's work or credential.

Step 4 — Role-Specific Keyword Use

Use exact job terminology when truthful, but do not optimize for repetition counts.

Priority:

  1. Technical Skills for concise discoverability.
  2. Experience bullets for proof of use.
  3. Project bullets for engineering depth.

A required term appearing once with strong evidence is more useful than repeating it several times without proof.

Step 5 — Resume Output Rules

  • Use standard section names: Education, Experience, Projects, Technical Skills.
  • For students/new graduates, make expected graduation explicit.
  • Keep the resume focused on one role family at a time.
  • Prefer 2–3 relevant projects over a long inventory.
  • Use measured outcomes when available; otherwise use concrete implementation detail.
  • Do not fabricate percentages, user counts, latency improvements, production scale, or "production-ready" claims.
  • Distinguish training programs/badges from exam-based certifications.
  • Ensure linked repositories visibly support project claims.

Diagnostic Output

Return:

  1. Eligibility findings
  2. Parsing findings
  3. Requirement-to-evidence matrix
  4. Unsupported or ambiguous claims
  5. Recommended role-specific edits
  6. Export validation checklist
  7. Outcome-tracking recommendation

You may compute a private coverage ratio such as "7 of 9 required criteria have direct evidence" for explanation. Do not label it a universal ATS score or assign a pass/fail threshold unless the employer publishes one.

Outcome Tracking

When the user is applying repeatedly, track:

  • job URL and role family;
  • eligibility status;
  • resume version;
  • application channel;
  • submission date;
  • outcome: pending, rejected before assessment, assessment received, interview received, offer/reject after interview.

Use those outcomes to diagnose where progression stops. Do not assume every rejection is caused by ATS parsing or keywords.

Guardrails

  • Never promise a shortlist, callback rate, or interview multiplier.
  • Never invent a metric to make a bullet look stronger.
  • Never equate a parser issue with automatic rejection unless the employer documents that behavior.
  • Never claim a certification proves hands-on production experience.
  • Never hide an eligibility mismatch with wording.
  • Always prefer verifiable evidence over keyword density.

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