Jd Gap Analysis

JD → course gap analysis (AI topics only)

Goal: given a job description, tell the learner which AI/ML/GenAI skills the role wants that this learn-ai course does not yet teach — and where each gap would slot in. Ignore everything that is not an AI/ML topic.

The input ($ARGUMENTS / the args passed to the skill) is either raw JD text or a URL. If it is empty, ask the user to paste the JD text or give a URL, then stop.

Step 1 — Get the JD text

• Pasted text → use it directly.
• URL → fetch it:
  1. Try WebFetch(url, "Extract the full job description: responsibilities, requirements, tech stack, nice-to-haves. Return the raw text.").
  2. Many job pages (LinkedIn, Greenhouse, Lever, Workday, company SPAs) are JavaScript-rendered, so WebFetch returns only a title/shell. If the result looks empty or truncated, render it with the chrome-devtools MCP: new_page(url) → wait → evaluate_script(() => document.body.innerText) and use that text. (See how this repo's own gap analysis rendered a SPA curriculum.)
  3. If the page is behind a login wall (common for LinkedIn) or a captcha, say so and ask the user to paste the JD text instead. Do not guess the contents.

Step 2 — Extract the AI-related requirements ONLY

Scan the JD and pull out concrete AI/ML/GenAI items. Keep anything in these families:

• Agent frameworks / orchestration: LangChain (LCEL, memory, retrievers), LlamaIndex, Semantic Kernel, CrewAI, AutoGen, LangGraph, DSPy, Haystack, agent orchestration, workflow automation, multi-agent, tool use / function calling, ReAct, planning, MCP.
• RAG / retrieval: embeddings, vector databases (Chroma, Qdrant, Pinecone, pgvector, Weaviate, FAISS), chunking, reranking, hybrid search (BM25 + dense), HyDE, GraphRAG, contextual retrieval, citations/attribution.
• LLM integration / prompting: prompt engineering, few-shot, chain-of-thought, self-consistency, context engineering / optimization, structured output, streaming, prompt caching, token/cost management.
• Fine-tuning / training: SFT, LoRA / QLoRA / PEFT, RLHF, DPO, distillation, dataset prep, overfitting/eval.
• Eval / quality / safety: LLM-as-judge, evaluation frameworks, guardrails, prompt-injection defense, PII/DLP, OWASP LLM Top 10, red-teaming, hallucination detection, human-in-the-loop validation.
• LLMOps / observability / serving: tracing (Langfuse, LangSmith, OpenTelemetry), monitoring, regression gates, model routing/fallbacks, quantization, local inference, KV cache, serving AI behind an API (FastAPI/streaming/SSE for AI), batch APIs.
• Modalities: computer vision, multimodal LLMs, CLIP, image generation / diffusion, audio / speech (STT/TTS, Whisper, realtime, VAD), computer use / browser agents.
• Foundations / theory (often interview-tested): transformers/attention, tokenizers, classic ML (regression, bias-variance, metrics), deep learning (backprop, CNN, RNN), reasoning / test-time compute, classification.
• Providers / models: OpenAI, Anthropic/Claude, Gemini, Llama/Ollama, NVIDIA NIM, reasoning models, model selection/cost tradeoffs.

Drop (not AI gaps): general software engineering (git, REST, microservices, Docker, Kubernetes, CI/CD, cloud basics) unless it is specifically AI-serving infra; programming language proficiency; databases/SQL basics (unless text-to-SQL over an LLM); frontend frameworks; years of experience; soft skills; degrees; non-AI domain knowledge (healthcare, finance, etc.) — note the domain, but it is not itself a course topic.

Produce a clean list of the AI requirements you extracted (deduplicated, normalized to the vocabulary above).

Step 3 — Load what the course already covers

The course's coverage is authoritative in these files — read them, do not rely on memory:

• CURRICULUM.md — the module map + per-module tasks and "Done when" (the source of truth).
• README.md — the module table + the deep-dive companions (05b, 06b, 06c, 01b/01c/01d).
• docs/GLOSSARY.md — every abbreviation the course uses (quick check for whether a term appears at all).
• grep -ri "<topic>" modules/*/README.md — confirm depth for a specific topic before calling it covered or a gap.

Current coverage at a glance (verify against CURRICULUM.md, which may have grown): 00 setup/providers · 01 fundamentals · 01b classic ML · 01c deep learning · 01d transformers · 02 integration · 03 prompting · 04 embeddings/vectors · 05 RAG · 05b advanced RAG · 06 agents · 06b LangGraph · 06c agent frameworks (LangChain/CrewAI/AutoGen/LlamaIndex/Semantic Kernel) · 07 production (eval, tracing incl. Langfuse, caching, guardrails, serving) · 08 classification · 09 vision · 10 image gen · 11 ingestion · 12 text-to-SQL · 13 fine-tuning · 14 local inference/opt · 15 reasoning/test-time compute · 16 context engineering · 17 MCP · 18 computer use · 19 audio/speech · 20 AI security · 21 LLMOps/eval · 22 product UX · 23 capstone.