cv-tailor skill

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

Phase 2: JD Keyword Match Analysis

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

Steps:

Extract three categories of keywords from the JD:

1. Categorized keyword extraction:

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

1. Match analysis:

| 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

Phase 3: STAR Quantified Rewriting

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

STAR Method Definition:

Steps:

Evaluate STAR completeness for each experience bullet:

1. Diagnose existing entries:

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)

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

1. Quantified rewriting:

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)"

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

1. Quantification guidance:

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")

Each rewritten entry must satisfy:

1. Rewrite quality checklist:

• [ ] 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

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:

1. Content structure check:

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

Phase 5: Final Optimized Output

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