Add 122 Python example scripts + .env.example

Each of the 122 code slides now has a standalone .py script in
examples/slide-NNN/script.py with the LLM provider pattern from
bro-js/agents/teacher/assistant/src/angry_teacher/llm.py:
  - load_dotenv() + ChatOpenAI with base_url=https://llm.brojs.ru/v1
  - OPENAI_API_KEY from .env (with safe (or '' or None) pattern)
  - Same comment block with alternative model/base_url choices

Layout:
  - sec1/sec5: code extracted from slide-NN.js (addCodeBlock)
  - sec2/sec3/sec4: code extracted from sectionN.pptx via python-pptx
    (JetBrains Mono font shapes)

100 scripts contain runnable code, 22 are visual-only (dividers,
intro, recap) and just print the slide title.

Setup:
  cp .env.example .env       # fill OPENAI_API_KEY (or symlink to your
                              # teacher/assistant/.env)
  pip install -r requirements.txt
  python examples/slide-005/script.py

examples/slide-054/script.py

@@ -0,0 +1,54 @@
+"""
+Slide 54: Slide 27
+Section 2: LangGraph 1.0
+Source: slides/section2-langgraph/section2.pptx
+
+Сгенерировано автоматически из slide-NN.js / sectionN.pptx.
+Паттерн провайдера скопирован из
+bro-js/agents/teacher/assistant/src/angry_teacher/llm.py.
+
+Запуск:
+  cd langchain-evolution-deck
+  cp .env.example .env   # заполни OPENAI_API_KEY
+  python examples/slide-054/script.py
+"""
+import os
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+
+load_dotenv()
+
+llm = ChatOpenAI(
+    # model="openrouter/free",
+    # model="qwen/qwen3.5-35b-a3b",
+    model="openai/gpt-oss-20b",
+    # model="nvidia/nemotron-3-nano",
+    # model="qwen/qwen3.5-9b",
+    base_url="https://llm.brojs.ru/v1",
+    # base_url="https://api.minimax.io/v1",
+    # base_url="http://0.0.0.0:8090/v1",
+    # base_url="https://openrouter.ai/api/v1",
+    # api_key=os.getenv("MINIMAX_API_KEY"),
+    api_key=(os.getenv("OPENAI_API_KEY") or "").strip() or None,
+    temperature=0.7,
+    stream_usage=True,
+)
+
+
+# === Code from slide 54 ===
+  from langgraph.graph import StateGraph, START, END
+  from langgraph.prebuilt import ToolNode
+  # tools, llm, agent, route -- из предыдущего слайда
+  g = StateGraph(State)
+  g.add_node("agent", agent)
+> g.add_node("tools", ToolNode(tools))
+  g.add_edge(START, "agent")
+  g.add_conditional_edges("agent", route, {"tools": "tools", END: END})
+  g.add_edge("tools", "agent")
+> app = g.compile()
+
+  # Цикл: agent решает -> tools исполняет -> agent снова читает результат
+> result = app.invoke({
+>     "messages": [{"role": "user", "content": "What is 2 + 3?"}]
+  })
+  print(result["messages"][-1].content)