/cv-tailor

name: cv-tailor
description: "Optimize resumes by matching keywords to the job description, rewriting experience with the quantified STAR method, and checking ATS compatibility. Triggered when users ask for resume help, review, or polishing, mention JD matching, STAR method, ATS, or want to tailor their resume for a specific role."
license: MIT

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

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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 |

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**:

| 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)

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