Cold Email Outreach

Cold Email Outreach Skill

Generates hyper-specific, angle-driven cold emails for internship outreach. NOT generic. Every email must have a concrete hook tied to something real the company is doing.

About the Sender (Rudra Dudhat)

• Name: Rudra Dudhat
• College: IIT Bhilai, B.Tech Data Science & AI (2nd year, graduating 2028)
• CGPA: 9.48
• Target role: Applied Multimodal AI Engineering — vision-language models (VLMs), multimodal systems
• Portfolio: rudradudhat2509.github.io
• GitHub: github.com/RudraDudhat2509
• X/Twitter: @rudrabuilds

Key Projects (pick the most relevant 1-2 per email)

1. VLM Research Paper Tutor — ingests papers including figures and citations, teaches content actively through coding assignments, maintains a skill tree. Stack: Docling, Qdrant, LangGraph, RAGAS. Strong research angle.
2. Cascade AI — multi-agent LLM backend on Firebase with self-healing routing and per-user memory via Firestore.
3. OptiQuant — ensemble ML system with SHAP explainability, deployed on AWS EC2 with Docker and GitHub Actions CI/CD.
4. diffprompt — CLI tool for behavioral prompt diffing (github.com/RudraDudhat2509/diffprompt). In active development.
5. Multi-agent GAIA benchmark system — built using smolagents targeting HuggingFace Agents Course leaderboard.
6. Personal Outreach Engine — automated CCP outreach pipeline using Groq LLM, Gmail SMTP/IMAP, Google Sheets, CLI-based human-in-the-loop approval.

Skills

• VLMs: CLIP, LLaVA/Idefics, Florence-2
• Fine-tuning: LoRA/QLoRA, PEFT
• Inference: vLLM, ONNX
• Agents/infra: LangGraph, FAISS/Qdrant, smolagents
• Eval: RAGAS, LangSmith
• Stack: Python, PyTorch, HuggingFace, FastAPI, Docker, AWS

Workflow

Step 1: Research the Company

Use web search to find:

• What the company actually builds (not just their homepage blurb)
• Their specific AI/ML work — models, papers, products, recent launches
• Any pain points or open problems visible in their blog, GitHub, job postings, or press
• Key people (founders, AI leads, research heads) — find a real name to address

Search queries to use:

• "[company name]" AI research blog
• "[company name]" machine learning paper
• "[company name]" internship OR "applied AI" site:linkedin.com
• "[company name]" GitHub

Step 2: Find the Angle

The angle is the ONE specific thing that connects Rudra's work to the company's real problems. It must be:

• Tied to something the company actually does (cite it specifically — a product feature, a paper, a blog post)
• Connected to a specific project or skill of Rudra's
• Something that shows he's done his homework, not just read the About page

Bad angle: "I am passionate about AI and your company does great work." Good angle: "Your qXR-Detect product surfaces lesion-level confidence scores — I've been exploring attention map visualization in VLMs and built a research paper tutor that reasons over figure-level embeddings, which maps directly to the explainability pipeline you'd need at scale."

Step 3: Draft the Email

Structure:

1. Hook (1-2 lines) — The angle. Specific thing they do + why it caught your attention. Name the product/paper/feature.
2. What you've built (2-3 lines) — 1-2 most relevant projects. Be concrete: what it does, what stack, what result.
3. The ask (1-2 lines) — Clear, low-friction. Internship or a short conversation. Not begging, not entitled.
4. Links — Portfolio + GitHub. One line, clean.

Rules:

• Address a real person by name if found. If not, use a role ("Hi [AI team]" or "Hi [Hiring team]").
• No em dashes. Use semicolons or periods instead.
• No filler phrases: "I am passionate about", "I would love the opportunity", "As a quick introduction"
• Subject line must be specific — reference the company's actual work, not just "Internship Inquiry"
• Length: whatever the angle demands. Don't pad. Don't cut substance.

Subject line formula: [Their specific product/problem] + [Your relevant angle] Example: VLM explainability for qXR-Detect | IIT Bhilai undergrad

Step 4: Output Format

Present the email like this:

SUBJECT: [subject line]

[email body]

---
Links:
Portfolio: rudradudhat2509.github.io
GitHub: github.com/RudraDudhat2509

Then add a short note (2-3 lines) explaining:

• What angle was used and why
• Which projects were picked and why those specifically
• Any uncertainty (e.g., "couldn't find a specific person to address, used team instead")

Edge Cases

• No AI/ML work found at the company: Flag it. Ask the user if they still want to proceed or want a different company.
• Very small/stealth company: Use GitHub or LinkedIn to find their stack. If still nothing, write around their problem domain (e.g., "companies building X typically face Y problem").
• User provides a JD: Extract required skills from it and prioritize matching projects accordingly.
• User already has context: If they paste a company description or specific detail, use that as the primary source; supplement with search.