Resume Generator

Resume Generator

Generate tailored, ATS-optimized resumes that survive both algorithmic screening AND the 7-second human scan.

Critical Context

The 7-Second Reality: Recruiters spend 6-7 seconds on initial scan. 80% of viewing time concentrates on: name, current title/company, previous title/company, dates, education. Everything below the fold is largely ignored during yes/no/maybe sorting.

Dual-Audience Optimization: Resume must pass ATS keyword matching AND create human excitement. Optimize for both without alienating either.

Workflow Overview

0. KNOCKOUT SCREENING (Mandatory)
   └─ Verify no disqualifying factors before proceeding

1. CONTEXT SELECTION
   └─ User selects: seniority level, function type, company type

2. PARSE JOB DESCRIPTION
   └─ Extract: keywords, requirements, culture signals, seniority indicators

3. RETRIEVE & MATCH
   └─ Load achievements; score against JD; apply context-specific weights

4. ABOVE-THE-FOLD OPTIMIZATION
   └─ Place strongest evidence in high-attention zones (top-third, left-aligned)

5. GENERATE WITH AUTHENTICITY
   └─ Apply frameworks; vary patterns; use precise metrics

6. QUALITY VERIFICATION
   └─ Knockout re-check, authenticity audit, parsing confidence

7. PRODUCE OUTPUT
   └─ Render artifact; save file; provide tailoring summary with LinkedIn alert

Step 0: Knockout Screening (MANDATORY)

Complete before any optimization. A single knockout negates all other work.

Hard Knockouts (Flag if present)

Format Knockouts (Verify clean)

• Professional email address
• Phone in standard format
• LinkedIn URL functional
• No special characters that may corrupt parsing

STOP if hard knockout present without mitigation strategy.

Step 1: Context Selection

User MUST specify (ask if not provided):

Seniority Level

Function Type

Company Type

Load weight profile from references/context-profiles.md based on selection.

Step 2: Parse Job Description

Extract and categorize:

Create keyword frequency map. Top 10 keywords MUST appear in resume.

Step 3: Retrieve & Match Achievements

For each JD requirement, scan achievements-bank.md:

Scoring (apply context weights from Step 1):

• Direct match (same skill/outcome): 3 points × context weight
• Adjacent match (transferable): 2 points × context weight
• Contextual match (similar domain): 1 point × context weight

Prioritization:

1. Ensure ALL hard requirements have evidence
2. Rank by weighted score
3. Flag gaps (requirements without strong matches)

Step 4: Above-the-Fold Optimization

The First Screen Rule: What appears in top-third, left-aligned determines whether deeper engagement occurs.

Placement Requirements

Achievement Promotion Rules

If strongest achievements are from older roles:

• Option A: Promote to summary as "Career Highlights"
• Option B: Add "Key Achievement" callout in summary
• Option C: Restructure current role to demonstrate continued excellence

Verify: Can recruiter see qualification evidence within 7 seconds?

Step 5: Generate with Authenticity

Apply CAR Framework

See references/achievement-frameworks.md for full guidance.

[Action verb] [specific what] to address [challenge], resulting in [quantified outcome]

Authenticity Requirements

AVOID these AI-detection patterns:

• Consecutive buzzwords ("spearheaded... orchestrated... leveraged...")
• Same verb starting 3+ bullets
• All round numbers (50%, 25%, 100%)
• Generic phrases without specificity

INCLUDE these authenticity markers:

• At least one non-round metric (47.3%, $892K, 23-27%)
• Named projects, tools, or systems
• Exact team sizes when known
• Varied sentence structures

Factual Integrity (MANDATORY)

All data from XML source files is treated as immutable fact. Wording may be adjusted for flow and keyword optimization, but the underlying facts must not change.

Protected Facts (NEVER modify):

Allowed Modifications:

• Verb substitution (same meaning, different word)
• Sentence structure variation
• Condensing (omit details, but don't change remaining facts)
• Keyword insertion (add context, don't alter facts)

Prohibited:

• Inflating metrics (47% → 50%)
• Expanding scope ("team" → "cross-functional organization")
• Adding responsibilities not in XML
• Creating achievements not documented
• Rounding for readability (keep $892K, don't write $900K)

Excluded from Resume (interview prep only):

• <departure-context> elements — NEVER include departure reasons on resume
• <vulnerability> elements — for interview preparation only
• <reframe> elements — for interview preparation only
• Any explanation of why a role ended

Precision Metrics Rules

Rule: Only use precise metrics you can defend in interview.

Step 6: Quality Verification

Knockout Re-Check

• Zero typos (spell-check + read aloud)
• Zero grammatical errors
• Consistent date formatting (MM/YYYY)
• All claims verifiable if asked
• No orphan bullets

Authenticity Audit

• Power verbs vary (no verb used >2 times)
• At least one non-round metric present
• At least one named project/tool/system
• No 3+ consecutive bullets with identical structure

Above-the-Fold Check

• Current title visible in first screen
• Top 3 keywords visible in first screen
• Strongest achievement visible in first screen

Parsing Confidence

• Single-column layout
• Standard section headers
• No tables, graphics, or text boxes
• Dates in parseable format

Source Validation (MANDATORY)

Before generating final output, validate ALL facts against XML source files:

Validation Checklist:

• Each job title matches <title> element exactly
• Each company name matches <company> element exactly
• Each date range matches <start> and <end> elements exactly
• Each metric matches value in <result> element exactly
• Each team size matches count in source activity
• Each project/product name matches <activity> or <initiative> elements

Validation Process:

1. For each Experience entry, identify source <position> by matching company + date range
2. For each bullet, identify source <activity> by ID or content match
3. Verify all numerical claims trace to XML <result> elements
4. Flag any claim without XML source attribution

If validation fails: Do NOT generate output. List discrepancies and request correction.

Step 7: Produce Output

File Output

Save as resume-[company]-[role].md in /mnt/user-data/outputs/

Markdown Structure

# [Full Name]
[Email] | [Phone] | [Location] | [LinkedIn URL]

---

## Professional Summary
[3-5 lines: specific identity + top achievements + value proposition for THIS role]
[Include top 5 JD keywords naturally]

---

## Professional Experience

**[Job Title]** | [Company Name] | [Location]
*[MM/YYYY] – [MM/YYYY or Present]*

- [Strongest achievement—quantified, specific]
- [Second achievement—different verb, different structure]
- [Third achievement—demonstrates another JD requirement]

[Repeat for relevant roles]

---

## Skills
**[Category aligned to JD]:** [Skill 1], [Skill 2], [Skill 3]
**[Second category]:** [Skill 1], [Skill 2], [Skill 3]

---

## Education
**[Degree]** | [Institution] | [Year]
[Certifications if relevant to role]

Tailoring Summary (in conversation, not resume)

Provide:

1. Keywords incorporated: List with placement locations
2. Requirements matched: Each requirement → supporting achievement
3. Coverage gaps: Requirements without strong evidence
4. Authenticity score: Count of specificity markers
5. LinkedIn consistency alert: Flag any resume/LinkedIn divergence risk
6. Source attribution: For each bullet, note the XML activity ID it derives from

⚠️ LinkedIn Consistency Check:
Ensure your LinkedIn reflects [specific elements] before applying.
Recruiters cross-check—discrepancies trigger investigation.

Excitement Assessment

Rate candidate's "excitement potential" for this role:

• High: Multiple quantified wins visible in 7-second scan, unexpected specificity
• Medium: Meets requirements, lacks standout evidence
• Low: Gaps in hard requirements, may land in "maybe pile"

If LOW, explicitly flag: "Candidacy may be weak regardless of optimization. Consider: [specific strengthening recommendations]"

Reference Files

Load as needed:

• references/context-profiles.md — Weight calibration by seniority/function/company
• references/achievement-frameworks.md — CAR framework, power verbs, quantification, soft skill inference
• references/ats-optimization.md — Keyword placement, formatting, parsing rules