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:
-
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)
-
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)
-
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:
-
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 -
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 | -
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
-
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:
-
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) -
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)" -
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")
-
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:
-
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 (-) -
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") -
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")
-
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:
-
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 -
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.)
-
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
| 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:
- Identify which Phase the feedback relates to
- Re-execute from that Phase
- Cascade updates to all downstream content
- Maintain overall consistency (keywords, STAR rewrites, and ATS checks update in lockstep)
Core Principles
- Authenticity first: All optimizations must be based on the user's real experience — fabricating data or experience is strictly prohibited
- Targeted optimization: Every change should serve JD alignment — no aimless embellishment
- Actionable advice: Recommendations must be directly usable — don't say "add metrics" without guiding the user on how
- Privacy protection: Remind users to redact sensitive information (phone numbers, home addresses, etc.) when sharing their resume