Add human-focused README.md to every skill and bundle directory

Each README is written for a human audience, explaining:
- What the skill does (not what format it follows)
- What benefit the user gets from installing it
- Quick setup and usage patterns
- When to load/trigger the skill
- What scripts, references, and templates it ships

data-scientist already had a README — left unchanged.

48 READMEs added across all skill and bundle directories.

langchain/README.md

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+# LangChain — LLM Application Framework
+
+An expert-level skill for building LLM-powered applications with LangChain — the most widely adopted LLM orchestration framework. LCEL chains, RAG pipelines, agents, LangSmith observability, and LangServe deployment.
+
+## Why Install This Skill
+
+When your agent loads this skill, it becomes a **LangChain expert** who can:
+
+- **Build chains with LCEL** — `prompt | model | parser` composition with the Runnable interface
+- **Create agents** — `create_agent` with tools (not legacy AgentExecutor)
+- **Implement RAG pipelines** — document loading, splitting, embedding, retrieval, generation
+- **Add observability** — LangSmith tracing for production debugging
+- **Deploy with LangServe** — REST API deployment for production
+
+## What You Get
+
+| Directory | Purpose |
+|-----------|---------|
+| `SKILL.md` | Core principles, pipeline modes, where-to-start table, quick reference |
+| `references/` | LCEL reference, RAG strategies, agent patterns, LangSmith, LangServe, framework comparisons |
+
+## Framework Comparison
+
+LangChain is the broadest LLM framework with 1000+ integrations. Its agents now run on LangGraph underneath. Use LangChain for rapid prototyping and broad integration support; drop to LangGraph when you need full state-machine control.
+
+## Requirements
+
+Python 3.8+ with `langchain`, `langchain-community`, and provider-specific packages.