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.

haystack/README.md

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+# Haystack — Production Search & NLP Pipelines (deepset)
+
+An expert-level skill for building **production search and NLP pipelines** with Haystack. Pipelines are validated DAGs with typed components and explicit connections.
+
+## Why Install This Skill
+
+When your agent loads this skill, it becomes a Haystack expert who can:
+
+- **Design pipeline DAGs** — add_component + connect with typed input/output slots
+- **Build RAG pipelines** — document indexing + query pipelines with embedding retrieval
+- **Create agentic systems** — tool-using agents with ReAct pattern
+- **Integrate generative AI** — PromptBuilder (Jinja2) + LLM generators
+- **Evaluate pipeline quality** — faithfulness, relevancy, and custom metrics
+- **Deploy with Hayhooks** — REST API deployment for production
+
+## What You Get
+
+| Directory | Purpose |
+|-----------|---------|
+| `SKILL.md` | Core paradigm, where-to-start table, framework comparison |
+| `references/` | Deep dives into pipeline design, RAG, agents, evaluation, Hayhooks, and framework comparisons |
+
+## Framework Comparison
+
+Haystack uses explicit Pipeline DAGs (add_component + connect) — different from LangChain's LCEL pipe operator and LlamaIndex's query engines. Pipelines are validated at declaration time.
+
+## Requirements
+
+Python 3.8+ with `haystack-ai` package.