resume-builder

name

resume-builder

description

Use this skill whenever a user wants to build, improve, review, or tailor their CV or resume for AI/ML roles including AI PM, AI Engineer, ML Engineer, Data Scientist, AI Strategist, or any AI-adjacent position. Triggers include: "help me write my resume", "review my CV", "update my resume for an AI role", "how do I add AI keywords to my CV", "write my bullet points", "tailor my resume to this job description", "help me highlight my AI experience", "what should I put on my resume", or when a user shares their existing CV or job description and asks for improvements. Also trigger after completing the AI Job Strategy skill — the resume is always the next step. Use proactively whenever resume or CV improvement for AI roles is mentioned, even casually.

AI Resume Builder

This skill helps users build, upgrade, and tailor a high-impact, AI-optimized resume for AI/ML product and engineering roles. It uses a proven impact formula, curated AI keyword bank, and real bullet point examples to produce a resume that gets past ATS and impresses hiring managers.

Reference file: Load references/keywords-and-bullets.md whenever you need the keyword bank, bullet examples, or format checklist.

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HOW TO USE THIS SKILL

Work section by section using ask_user_input to gather information. Draft each section immediately after collecting inputs — don't wait until the end. Keep it conversational and encouraging.

Two modes — detect which applies:

• Build from scratch: User has no existing CV → go through all sections in order
• Review & upgrade: User shares existing CV → run a gap analysis first, then improve section by section

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WELCOME MESSAGE

"Let's build your AI-optimized resume! 🚀 I'll ask you a few questions about your experience, then craft every bullet point using a proven impact formula. We'll make sure your CV is loaded with the right AI keywords and tells a compelling story. Let's start!"

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STEP 0 — DETECT MODE

Q0 — Starting Point

ask_user_input:
  question: "Where are we starting from?"
  type: single_select
  options:
    - "I have an existing CV — help me upgrade it"
    - "Starting from scratch — build me a new one"
    - "I have a job description — tailor my resume to it"
    - "Just review my bullet points and improve them"

If they share an existing CV: Run a gap analysis against references/keywords-and-bullets.md checklist before asking any questions. Identify: missing keywords, weak bullets (no metrics), wrong tense, title mismatches, length issues. Present the gap analysis to the user first, then proceed section by section.

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STEP 1 — PROFILE & TARGET ROLE

Q1 — Target Role

ask_user_input:
  question: "Which AI role are you targeting with this resume?"
  type: single_select
  options:
    - "AI Product Manager (AI PM)"
    - "Generative AI PM / LLM Product Owner"
    - "ML Engineer / AI Engineer"
    - "Data Scientist / AI Analyst"
    - "AI Strategy / Business Lead"
    - "Multiple roles — build a flexible base resume"

Q2 — Years of Experience

ask_user_input:
  question: "How many years of total work experience do you have?"
  type: single_select
  options:
    - "0–2 years (student / early career)"
    - "3–5 years"
    - "6–10 years"
    - "10+ years (senior / leadership)"

Q3 — AI Experience Level

ask_user_input:
  question: "How much direct AI/ML experience do you have?"
  type: single_select
  options:
    - "None yet — pivoting into AI"
    - "Some — worked with AI tools or alongside AI teams"
    - "Moderate — shipped 1–2 AI features or products"
    - "Strong — led multiple AI products or ML systems"

Draft the resume header and summary section:

• Name, Title (use the TARGET title, not current), LinkedIn/GitHub/Portfolio links
• 3-line professional summary: [Who you are] + [Domain expertise] + [AI focus] + [Career goal]
• Tailor summary keywords to the target role using the keyword bank

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STEP 2 — WORK EXPERIENCE (Most Critical Section)

Go role by role. For each role, ask:

Q4 — Role Details (repeat per role)

ask_user_input:
  question: "Tell me about your most recent / most relevant role. What did you actually do day-to-day?"
  type: single_select  [use as prompt — collect free text or follow up]
  options:
    - "I'll describe it — ask me follow-up questions"
    - "I'll paste my existing bullet points to improve"
    - "Let me describe the role and you write the bullets"

Q5 — AI Impact Areas (per role)

ask_user_input:
  question: "Which AI/ML areas did this role touch? (pick all that apply)"
  type: multi_select
  options:
    - "Built or managed LLM / Generative AI features"
    - "Used Prompt Engineering or RAG"
    - "Worked with ML models or data pipelines"
    - "Ran A/B tests or experimentation frameworks"
    - "AI roadmap, strategy, or ethics decisions"
    - "Cost/latency optimization of AI systems"
    - "Cross-functional delivery of AI product"
    - "Computer Vision or NLP features"
    - "No direct AI — but adjacent work"

Q6 — Metrics Available

ask_user_input:
  question: "Do you have any numbers or metrics from this role?"
  type: multi_select
  options:
    - "% improvement in a metric (speed, accuracy, engagement)"
    - "$ revenue or cost impact"
    - "Number of users or teams impacted"
    - "Time saved (hours, weeks, months)"
    - "Delivery speed (launched X% ahead of schedule)"
    - "No hard numbers — help me estimate"

For each role, produce 3–5 bullets using the impact formula: Accomplished [X] as measured by [Y] by doing [Z]

Rules:

• Past tense always
• Start each with a strong action verb from the reference file
• Include at least one AI technology by name (LLM, RAG, NLP, etc.)
• Lead with the most impressive bullet
• Pull matching bullet structures from references/keywords-and-bullets.md as inspiration
• If no metrics: help user estimate ("roughly how many users saw this?", "did it save hours per week?") Job Title Check: After drafting, flag if the user's internal HR title undersells the actual AI work. Suggest the appropriate market title (e.g. "AI PM" over "Senior Associate").

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STEP 3 — AI PROJECTS & SIDE WORK

Q7 — Projects to Include

ask_user_input:
  question: "Do you have any AI projects, certifications, or side work to include?"
  type: multi_select
  options:
    - "Built an AI side project (personal or startup)"
    - "Completed an AI certification (DeepLearning.AI, Reforge, etc.)"
    - "GitHub contributions / open source AI work"
    - "Published LinkedIn posts, articles, or a blog on AI"
    - "Prototype or proof-of-concept built at work"
    - "None yet — skip this section"

For each project, draft:

• Project name + one-line description
• Technologies used (pull from keyword bank — tools, models, frameworks)
• 1–2 impact bullets using the formula
• Link if available (GitHub, demo, article) Pro tip to add: If they have weak or no projects, suggest 2–3 quick high-impact project ideas tailored to their domain using agentic RAG, LLMs, or AI automation.

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STEP 4 — SKILLS SECTION

Q8 — Technical Skills Inventory

ask_user_input:
  question: "Which of these have you actually worked with? (pick all that apply)"
  type: multi_select
  options:
    - "Python / SQL"
    - "LangChain / LangFlow / CrewAI"
    - "OpenAI API / Anthropic API / Gemini"
    - "Hugging Face / PyTorch / TensorFlow"
    - "RAG / Vector DBs (Chroma, Pinecone, Weaviate)"
    - "AWS SageMaker / Azure ML / Google Vertex AI"
    - "MLflow / Kubeflow / LLMOps tools"
    - "Power Automate / n8n / Zapier (AI automation)"
    - "Prompt Engineering"
    - "A/B Testing / Experimentation"
    - "Figma / Product Tools (Jira, Linear, etc.)"

Draft Skills Section organized into 3 tiers:

1. AI/ML Tools & Frameworks — tools they've actually used
2. AI Concepts & Methods — relevant concepts from keyword bank matched to target role
3. Domain & Product Skills — domain expertise + PM/eng fundamentals Keyword optimization: Cross-reference against the target role's likely ATS keywords from references/keywords-and-bullets.md. Add all relevant terms the user qualifies for.

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STEP 5 — EDUCATION & CERTIFICATIONS

Q9 — Credentials

ask_user_input:
  question: "What education or certifications do you have or are completing?"
  type: multi_select
  options:
    - "University degree (any field)"
    - "AI PM certification (e.g. Reforge, Product School)"
    - "DeepLearning.AI / Coursera ML courses"
    - "AWS / Azure / GCP AI certifications"
    - "Bootcamp or intensive program"
    - "Currently enrolled — in progress"
    - "None — plan to add soon"

Draft education section. If certifications are in progress, include them with "(In Progress — Expected [Month Year])".

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STEP 6 — JOB DESCRIPTION TAILORING (if applicable)

If the user has a specific job description to target:

Q10 — Tailoring Mode

ask_user_input:
  question: "Do you want to tailor this resume to a specific job description?"
  type: single_select
  options:
    - "Yes — I'll paste the job description now"
    - "Yes — help me create a master resume I can tailor from"
    - "No — keep it general for now"

If yes — run tailoring process:

1. Extract top 5–7 required skills/keywords from the JD
2. Map each to existing resume bullets — identify gaps
3. Rewrite the top 3 bullets of the most relevant role to front-load those keywords
4. Update the professional summary to mirror the JD language
5. Flag any JD requirements the user genuinely can't claim — suggest honest framing Pro Tip to share with user:

"Create one master resume, then for each application update just the summary and the top bullet of your most relevant role to match the top 3 skills in that JD. This keeps quality high without rewriting from scratch every time."

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STEP 7 — FINAL REVIEW & CHECKLIST

After drafting all sections, run the full checklist from references/keywords-and-bullets.md and present results:

✅ / ❌  1 page (< 10 yrs) or 2 pages (senior+)
✅ / ❌  Every bullet follows X → Y → Z formula
✅ / ❌  Past tense throughout
✅ / ❌  Most impactful bullet listed first per role
✅ / ❌  3–5 bullets per role max
✅ / ❌  AI keywords match target role
✅ / ❌  Strategic AI thinking demonstrated
✅ / ❌  Job titles reflect actual AI work done
✅ / ❌  Skills section covers tools used
✅ / ❌  No generic filler phrases ("responsible for", "helped with")

Flag any ❌ items and offer to fix them immediately.

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FINAL OUTPUT FORMAT

Produce the complete resume in clean markdown:

# [Full Name]
[Target Job Title] | [City, Country] | [Email] | [LinkedIn] | [GitHub/Portfolio]
 
---
 
## Professional Summary
[3 sentences: Who you are + Domain expertise + AI focus + What you're seeking]
 
---
 
## Work Experience
 
### [Job Title — use market title, not HR title]
**[Company Name]** | [City] | [Start Date] – [End Date or Present]
- [Most impactful bullet — metric + AI tech + outcome]
- [Second bullet]
- [Third bullet]
- [Fourth bullet — if strong]
 
### [Previous Role]
...
 
---
 
## AI Projects & Initiatives
### [Project Name]
*[One-line description] | Technologies: [list]*
- [Impact bullet]
 
---
 
## Skills
**AI/ML Tools & Frameworks:** [list]
**AI Concepts & Methods:** [list]
**Domain & Product:** [list]
 
---
 
## Education & Certifications
**[Degree / Cert Name]** — [Institution] | [Year]
**[Cert in progress]** — [Institution] | Expected [Month Year]

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QUALITY STANDARDS

• Never use: "responsible for", "helped with", "assisted in", "worked on" — these are weak; always rewrite
• Always name the AI tech: Don't say "built a chatbot" — say "built an LLM-powered chatbot using GPT-4 and LangChain"
• Metrics first: If a bullet has no number, push user to estimate one before finalizing
• Domain + AI intersection: Every resume should show the user's domain expertise PLUS AI application in that domain
• ATS-safe format: No tables, no graphics, no columns in the text output — clean markdown only

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EDGE CASES

• No AI experience at all: Focus on transferable skills + adjacent work; add a "Learning & Projects" section for in-progress AI work; be honest but frame positively
• Career gap: Address briefly in summary — "Following a career break, now focused on transitioning into AI product management"; don't hide it, contextualize it
• Too many roles: Help user select the 3–4 most relevant; archive older roles in a 1-line "Earlier Career" section
• Overqualified / senior: Ensure resume shows leadership scale, team size managed, org-level AI strategy — not just execution
• Student / no work experience: Lead with projects, certifications, and coursework; use internships and academic AI work as primary experience
• After finishing: Always offer to (a) export to Word/PDF, (b) tailor to a specific JD, (c) write a matching LinkedIn headline and About section