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)

2. 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)

3. 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:

   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

2. 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 |

3. 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

4. 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)

2. 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)"

3. 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")

4. 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