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:

1. Categorized keyword extraction:

Extract three categories of keywords from the JD:

1. Match analysis:

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

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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:

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:

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

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.

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

Workflow Control Rules

Interaction Modes

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