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feat: add more problems
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‎.amazonq/rules/problem-creation.md‎

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@@ -6,11 +6,17 @@ When user requests a problem by **number** or **name/slug**, the assistant will:
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1. **Scrape** problem data using `.templates/leetcode/scrape.py`
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2. **Transform** data into proper JSON template format
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3. **Create** JSON file in `.templates/leetcode/json/{problem_name}.json`
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4. **Update** Makefile with `PROBLEM ?= {problem_name}`
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5. **Generate** problem structure using `make p-gen`
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6. **Verify** with `make lint` - fix template issues in JSON if possible, or manually fix generated files if template limitations
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7. **Iterate** if JSON fixes: re-run `make p-gen PROBLEM={problem_name} FORCE=1` and `make lint` until passes to ensure reproducibility
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3. **CRITICAL: Include images** - Extract image URLs from scraped data and add to readme_examples with format: `![Example N](image_url)\n\n` before code blocks
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- Check scraped data for image URLs in the `raw_content` field
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- Look for patterns: `https://assets.leetcode.com/uploads/...` or `<img alt="" src="..." />`
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- Common patterns: `kthtree1.jpg`, `kthtree2.jpg`, `clone_graph.png`, `container.jpg`
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- Images provide crucial visual context, especially for tree and graph problems
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- Always verify images are included in `readme_examples` and accessible
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4. **Create** JSON file in `.templates/leetcode/json/{problem_name}.json`
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5. **Update** Makefile with `PROBLEM ?= {problem_name}`
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6. **Generate** problem structure using `make p-gen`
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7. **Verify** with `make lint` - fix template issues in JSON if possible, or manually fix generated files if template limitations
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8. **Iterate** if JSON fixes: re-run `make p-gen PROBLEM={problem_name} FORCE=1` and `make lint` until passes to ensure reproducibility
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## Scraping Commands
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@@ -46,7 +52,7 @@ Required fields for `.templates/leetcode/json/{problem_name}.json`:
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"readme_description": "Given an array of integers `nums` and an integer `target`, return indices of the two numbers such that they add up to `target`.",
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"readme_examples": [
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{
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"content": "```\nInput: nums = [2,7,11,15], target = 9\nOutput: [0,1]\n```\n**Explanation:** Because nums[0] + nums[1] == 9, we return [0, 1]."
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"content": "![Example 1](https://example.com/image1.jpg)\n\n```\nInput: nums = [2,7,11,15], target = 9\nOutput: [0,1]\n```\n**Explanation:** Because nums[0] + nums[1] == 9, we return [0, 1]."
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}
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],
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"readme_constraints": "- 2 <= nums.length <= 10^4\n- -10^9 <= nums[i] <= 10^9\n- -10^9 <= target <= 10^9\n- Only one valid answer exists.",

‎.templates/leetcode/json/kth_smallest_element_in_a_bst.json‎

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{
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"problem_name": "kth_smallest_element_in_a_bst",
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"solution_class_name": "Solution",
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"problem_number": "230",
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"problem_title": "Kth Smallest Element in a BST",
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"difficulty": "Medium",
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"topics": "Tree, Depth-First Search, Binary Search Tree, Binary Tree",
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"tags": [],
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"readme_description": "Given the `root` of a binary search tree, and an integer `k`, return the `k`th smallest value (1-indexed) of all the values of the nodes in the tree.",
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"readme_examples": [
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{
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"content": "![Example 1](https://assets.leetcode.com/uploads/2021/01/28/kthtree1.jpg)\n\n```\nInput: root = [3,1,4,null,2], k = 1\nOutput: 1\n```"
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},
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{
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"content": "![Example 2](https://assets.leetcode.com/uploads/2021/01/28/kthtree2.jpg)\n\n```\nInput: root = [5,3,6,2,4,null,null,1], k = 3\nOutput: 3\n```"
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}
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],
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"readme_constraints": "- The number of nodes in the tree is `n`.\n- `1 <= k <= n <= 10^4`\n- `0 <= Node.val <= 10^4`",
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"readme_additional": "**Follow up:** If the BST is modified often (i.e., we can do insert and delete operations) and you need to find the kth smallest frequently, how would you optimize?",
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"solution_imports": "from leetcode_py import TreeNode",
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"solution_methods": [
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{
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"name": "kth_smallest",
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"parameters": "root: TreeNode | None, k: int",
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"return_type": "int",
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"dummy_return": "0"
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}
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],
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"test_imports": "import pytest\nfrom leetcode_py.test_utils import logged_test\nfrom leetcode_py import TreeNode\nfrom .solution import Solution",
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"test_class_name": "KthSmallestElementInABst",
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"test_helper_methods": [
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{ "name": "setup_method", "parameters": "", "body": "self.solution = Solution()" }
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],
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"test_methods": [
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{
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"name": "test_kth_smallest",
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"parametrize": "root_list, k, expected",
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"parametrize_typed": "root_list: list[int | None], k: int, expected: int",
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"test_cases": "[([3, 1, 4, None, 2], 1, 1), ([5, 3, 6, 2, 4, None, None, 1], 3, 3), ([1], 1, 1)]",
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"body": "root = TreeNode.from_list(root_list)\nresult = self.solution.kth_smallest(root, k)\nassert result == expected"
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}
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],
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"playground_imports": "from solution import Solution\nfrom leetcode_py import TreeNode",
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"playground_test_case": "# Example test case\nroot_list = [3, 1, 4, None, 2]\nk = 1\nexpected = 1",
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"playground_execution": "root = TreeNode.from_list(root_list)\nresult = Solution().kth_smallest(root, k)\nresult",
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"playground_assertion": "assert result == expected"
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}

‎Makefile‎

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PYTHON_VERSION = 3.13
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PROBLEM ?= task_scheduler
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PROBLEM ?= kth_smallest_element_in_a_bst
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FORCE ?= 0
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sync_submodules:

‎leetcode/kth_smallest_element_in_a_bst/README.md‎

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# Kth Smallest Element in a BST
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**Difficulty:** Medium
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**Topics:** Tree, Depth-First Search, Binary Search Tree, Binary Tree
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**Tags:**
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**LeetCode:** [Problem 230](https://leetcode.com/problems/kth-smallest-element-in-a-bst/description/)
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## Problem Description
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Given the `root` of a binary search tree, and an integer `k`, return the `k`th smallest value (1-indexed) of all the values of the nodes in the tree.
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## Examples
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### Example 1:
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![Example 1](https://assets.leetcode.com/uploads/2021/01/28/kthtree1.jpg)
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```
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Input: root = [3,1,4,null,2], k = 1
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Output: 1
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```
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### Example 2:
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![Example 2](https://assets.leetcode.com/uploads/2021/01/28/kthtree2.jpg)
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```
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Input: root = [5,3,6,2,4,null,null,1], k = 3
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Output: 3
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```
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## Constraints
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- The number of nodes in the tree is `n`.
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- `1 <= k <= n <= 10^4`
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- `0 <= Node.val <= 10^4`
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**Follow up:** If the BST is modified often (i.e., we can do insert and delete operations) and you need to find the kth smallest frequently, how would you optimize?

‎leetcode/kth_smallest_element_in_a_bst/__init__.py‎

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‎leetcode/kth_smallest_element_in_a_bst/playground.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "imports",
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"metadata": {},
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"outputs": [],
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"source": [
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"from solution import Solution\n",
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"\n",
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"from leetcode_py import TreeNode"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "setup",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Example test case\n",
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"root_list = [3, 1, 4, None, 2]\n",
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"k = 1\n",
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"expected = 1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "execute",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"root = TreeNode.from_list(root_list)\n",
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"result = Solution().kth_smallest(root, k)\n",
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"result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "6dc42838",
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"metadata": {},
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"outputs": [
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"data": {
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}
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],
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"metadata": {
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‎leetcode/kth_smallest_element_in_a_bst/solution.py‎

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from leetcode_py import TreeNode
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class Solution:
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# Inorder Recursive
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# Time: O(k)
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# Space: O(h)
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def kth_smallest(self, root: TreeNode | None, k: int) -> int:
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def inorder(node: TreeNode | None):
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if not node:
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return
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yield from inorder(node.left)
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yield node.val
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yield from inorder(node.right)
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for i, val in enumerate(inorder(root)):
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if i == k - 1:
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return val
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raise ValueError(f"Tree has fewer than {k} nodes")
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# Binary Tree Traversal Patterns
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#
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# def inorder(node):
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# if node:
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# inorder(node.left)
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# print(node.val)
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# inorder(node.right)
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#
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# def preorder(node):
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# if node:
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# print(node.val)
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# preorder(node.left)
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# preorder(node.right)
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#
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# def postorder(node):
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# if node:
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# postorder(node.left)
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# postorder(node.right)
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# print(node.val)

‎leetcode/kth_smallest_element_in_a_bst/tests.py‎

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import pytest
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from leetcode_py import TreeNode
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from leetcode_py.test_utils import logged_test
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from .solution import Solution
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class TestKthSmallestElementInABst:
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def setup_method(self):
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self.solution = Solution()
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@pytest.mark.parametrize(
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"root_list, k, expected",
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[([3, 1, 4, None, 2], 1, 1), ([5, 3, 6, 2, 4, None, None, 1], 3, 3), ([1], 1, 1)],
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)
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@logged_test
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def test_kth_smallest(self, root_list: list[int | None], k: int, expected: int):
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root = TreeNode.from_list(root_list)
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result = self.solution.kth_smallest(root, k)
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assert result == expected

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