cv-tailor

CV Tailor

Three pillars of resume optimization: Analyze keyword alignment against the target JD, rewrite experience bullets using the STAR method with quantified results, and run an ATS compatibility check — producing a highly targeted, high-pass-rate optimized resume.

Quick Start

The user provides their resume (content or file) and the target JD. The agent then automatically completes the optimization following the workflow below:

User: Help me optimize my resume — I'm applying for this role [attaches JD + resume]
Agent: [Follows the SOP workflow and outputs optimization recommendations plus a rewritten resume]

SOP Workflow

Phase 1: Input Collection & Initial Analysis

Goal: Gather the user's resume and target JD; establish an optimization baseline.

Steps:

1. Collect materials:

• Obtain the user's resume content (pasted text or file path)
• Obtain the target JD (pasted text or role description)
• If no JD is provided, ask about the target role direction (industry + position + level)

1. Resume baseline parsing:

• Identify resume sections (education, work experience, projects, skills, etc.)
• Count resume length, number of experience entries, and time span
• Note the current resume format type (reverse-chronological / functional / hybrid)

1. JD core element extraction:

• Job title and level
• Core responsibilities (Top 5)
• Hard requirements (must-haves)
• Nice-to-haves
• Key skill terms and industry jargon

Output: Resume status summary + JD element checklist

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Phase 2: JD Keyword Match Analysis

Goal: Systematically compare keyword coverage between the resume and JD to identify match gaps.

Steps:

1. Categorized keyword extraction:

Extract three categories of keywords from the JD:

| Category | Description | Examples |

|----------|-------------|----------|

| Hard skill keywords | Tech stack, tools, methodologies | Python, SQL, A/B testing, Scrum |

| Soft skill keywords | Competency requirements | Cross-team collaboration, data-driven, project management |

| Industry/domain keywords | Domain-specific terminology | DAU, conversion rate, user growth, SaaS |

1. Match analysis:

Search each keyword in the resume and generate a match matrix:

   | Keyword | JD Priority | In Resume? | Location | Recommendation |
   |---------|-------------|------------|----------|----------------|
   | Python  | Required    | ✅ Yes     | Skills + Project 1 | Keep; add specific use-case context |
   | SQL     | Required    | ❌ No      | -        | Add; weave into project experience |

1. Coverage scoring:

• Required keyword coverage = matched required keywords / total required keywords × 100%
• Nice-to-have coverage = matched nice-to-have keywords / total nice-to-have keywords × 100%
• Benchmark: Required keyword coverage ≥ 80% is passing, ≥ 90% is excellent

1. Gap-fill recommendations:

• For each unmatched required keyword, recommend which section and entry to add it to
• Provide specific integration approaches (add to skills section / embed in experience bullet / highlight in project outcomes)

Output: Keyword match matrix + coverage scores + gap-fill plan

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Phase 3: STAR Quantified Rewriting

Goal: Rewrite each experience entry using the STAR method, ensuring quantified data support.

STAR Method Definition:

| Element | Meaning | Checkpoint |

|---------|---------|------------|

| S - Situation | Context & background | When, what scenario, what scale |

| T - Task | Objective & responsibility | What was your role, what problem to solve |

| A - Action | Specific actions taken | What you did, what methods/tools you used |

| R - Result | Quantified outcomes | Data changes, efficiency gains, cost savings |

Steps:

1. Diagnose existing entries:

Evaluate STAR completeness for each experience bullet:

   Original: "Responsible for user growth initiatives"

   Diagnosis:
   - S (Situation): ❌ Missing — no product or stage context
   - T (Task): ⚠️ Vague — "initiatives" is too generic
   - A (Action): ❌ Missing — no specific actions described
   - R (Result): ❌ Missing — no data whatsoever
   Score: 1/4 (severely lacking)

1. Quantified rewriting:

After gathering additional details from the user, rewrite using the STAR structure:

   Rewritten: "During a user growth plateau for [Product Name] (DAU 500K+),
   led the design of a new-user activation funnel analysis framework (S+T),
   optimized 3 critical registration flow touchpoints + designed a 7-day retention incentive strategy (A),
   increasing new-user D1 retention from 32% to 45% and monthly active users by 18% within 3 months (R)"

1. Quantification guidance:

If the user is unsure about specific numbers, provide prompting questions:

| Dimension | Guiding Questions |

|-----------|-------------------|

| Scale metrics | How many people did you manage / product DAU / project budget |

| Efficiency gains | How long did it take before vs. after optimization |

| Growth metrics | Revenue / users / conversion rate change |

| Cost savings | Money / headcount / time saved |

| Impact scope | Users served / clients covered / teams affected |

Data integrity principles:

• All data must be based on the user's real experience — fabrication is strictly prohibited
• If the user cannot provide exact figures, use reasonable ranges (e.g., "improved by approximately 20%–30%")
• Encourage relative values over absolutes (e.g., "2× efficiency improvement" is safer than "saved 3.7 hours per day")

1. Rewrite quality checklist:

Each rewritten entry must satisfy:

• [ ] Contains at least 1 quantified data point
• [ ] Covers at least 3 of the 4 STAR elements
• [ ] Begins with an action verb (led, built, optimized, drove, designed…)
• [ ] No longer than 3 lines (ATS readability)
• [ ] Incorporates missing keywords identified in Phase 2

Output: Before/after comparison table for each entry + STAR score changes

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Phase 4: ATS Compatibility Check

Goal: Ensure the resume can pass ATS (Applicant Tracking System) automated screening.

ATS Basics:

ATS is the software companies use to automatically screen resumes. It parses resume text, matches keywords, and assigns scores to determine whether a resume reaches human review. Common systems include Workday, Greenhouse, Lever, Taleo, and iCIMS.

Steps:

1. Format compatibility check:

| Check Item | Passing Standard | Common Issues |

|------------|------------------|---------------|

| File format | PDF or DOCX (PDF preferred) | Image-based resumes cannot be parsed |

| Layout | Single-column, standard heading hierarchy | Multi-column layouts may parse incorrectly |

| Fonts | Standard fonts (Arial, Calibri, Times New Roman, Helvetica) | Decorative fonts may render incorrectly |

| Tables | Avoid complex table-based layouts | Text inside tables may be skipped |

| Headers/footers | Keep critical info out of headers/footers | Some ATS skip header/footer regions |

| Images/icons | Don't use images to convey key information | ATS cannot read text in images |

| Special characters | Avoid special Unicode bullet characters | Use standard bullets (•) or hyphens (-) |

1. Content structure check:

| Check Item | Passing Standard |

|------------|------------------|

| Section titles | Use standard headings ("Work Experience", "Education", "Projects", "Skills") |

| Date format | Consistent format (e.g., "Jan 2023 – Jun 2024" or "2023/01 – 2024/06") |

| Company/school names | Use full names, not abbreviations (e.g., "Amazon Web Services" not "AWS") |

| Contact information | Include name, phone, email — placed prominently at the top |

| File naming | Recommended format: "FirstName_LastName_TargetRole_Resume" (e.g., "John_Smith_Product_Manager_Resume.pdf") |

1. Keyword density check:

• Core keywords should appear at least 2–3 times (distributed across different sections)
• Avoid keyword stuffing (repeating the same keyword within one paragraph)
• Use the exact phrasing from the JD (if the JD says "data analysis," don't write "data mining")

1. ATS score output:

   ATS Compatibility Scorecard
   ===========================
   Format Compatibility:     ██████████ 90/100
   Section Standards:        ████████░░ 80/100
   Keyword Match Rate:       ███████░░░ 70/100 (see Phase 2)
   Content Structure:        █████████░ 85/100
   ──────────────────────────
   Overall Score:            81/100 (Good)

   ⚠️ Major deductions:
   1. Uses a two-column layout (−10 pts)
   2. Missing a standalone "Skills" section (−5 pts)
   3. "Data analysis" keyword appears only once (−5 pts)

Output: ATS compatibility scorecard + item-by-item results + fix recommendations

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Phase 5: Final Optimized Output

Goal: Consolidate findings from all four phases into a final optimization deliverable.

Steps:

1. Optimization summary:

   Resume Optimization Summary
   ===========================
   JD Keyword Coverage:      62% → 92% (+30%)
   STAR Completeness:        Avg 1.5/4 → 3.5/4
   ATS Compatibility Score:  55/100 → 88/100
   Entries Rewritten:        6/8
   Keywords Added:           7

1. Output the fully rewritten resume:

• Present the optimized resume text section by section
• Bold all changed portions for easy comparison
• Keep all factual information unchanged (schools, companies, dates, etc.)

1. Additional recommendations (if applicable):

• Resume length guidance (new grads: 1 page; 3–5 years experience: 1–2 pages; 10+ years: up to 2 pages)
• Section ordering suggestions (adjust education vs. experience placement based on career stage)
• Channel-specific tweaks (different emphasis for recruiter / company portal / referral submissions)

Output: Optimization summary + fully rewritten resume + additional recommendations

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Workflow Control Rules

Interaction Modes

| User Input | Mode | Behavior |

|------------|------|----------|

| Resume only, no JD | Guided mode | Ask about the target role and JD first, then begin analysis |

| Resume + JD | Standard mode | Execute Phases 1–5 in full |

| Requests a specific phase only | Single-phase mode | Execute only the requested Phase (e.g., ATS check only) |

| Says "just give it a quick look" | Diagnostic mode | Output three scores + Top 3 improvement suggestions — no full rewrite |

Quality Checklist

Before delivering the final output, verify each item:

• [ ] Keyword match matrix is complete (covers all required JD items)
• [ ] Every rewritten entry includes at least 1 quantified data point
• [ ] STAR rewrites preserve the authenticity of the user's real experience
• [ ] No data or experience has been fabricated
• [ ] ATS check covers all format items
• [ ] Rewritten resume length is appropriate
• [ ] Keywords are woven in naturally — not force-fitted
• [ ] Contact details and sensitive information have not been leaked or altered

Iterative Refinement

If the user provides feedback on the optimization:

1. Identify which Phase the feedback relates to
2. Re-execute from that Phase
3. Cascade updates to all downstream content
4. Maintain overall consistency (keywords, STAR rewrites, and ATS checks update in lockstep)

Core Principles

1. Authenticity first: All optimizations must be based on the user's real experience — fabricating data or experience is strictly prohibited
2. Targeted optimization: Every change should serve JD alignment — no aimless embellishment
3. Actionable advice: Recommendations must be directly usable — don't say "add metrics" without guiding the user on how
4. Privacy protection: Remind users to redact sensitive information (phone numbers, home addresses, etc.) when sharing their resume