[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6H9Y04X9XDPBPFJDW814SWY":3,"skill-same:01M6H9Y04X9XDPBPFJDW814SWY":52},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":23,"linkOnly":24,"markdownContent":25,"markdownBody":26,"frontMatter":27,"refName":30,"commitSha":31,"skillDirectory":32,"forksCount":14,"repositoryHtmlUrl":33,"repositoryHomepageUrl":6,"ownerHtmlUrl":34,"ownerBlogUrl":35,"ownerDescription":36,"updatedAt":37,"install":38,"resources":51},"01M6H9Y04X9XDPBPFJDW814SWY","indexing",null,"Database indexing strategies","NeuralBlitz\u002FMito","NeuralBlitz","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F79354388?v=4","01M6H8T2TFH477HRS0KTSTKR2E","David Riggs","01M6H8T2YQQDXPYM092P670Z21",0,"MIT","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Findexing\u002FSKILL.md",[],[5,19,20,21,22],"database","opencode","databases","strategies","2026-11-10 10:12:30",false,"---\nname: indexing\ndescription: Database indexing strategies\nlicense: MIT\ncompatibility: opencode\nmetadata:\n  audience: developers\n  category: databases\n---\n\n## What I do\n- Design efficient indexes\n- Choose index types\n- Optimize query performance\n\n## When to use me\nWhen creating database indexes.\n\n## Index Types\n\n### B-Tree Index\n```python\nclass BTreeIndex:\n    \"\"\"B-Tree index implementation\"\"\"\n    \n    def __init__(self, order: int = 3):\n        self.root = BTreeNode(order)\n        self.order = order\n    \n    def insert(self, key: any, value: any):\n        \"\"\"Insert key-value pair\"\"\"\n        root = self.root\n        \n        if root.is_full():\n            new_root = BTreeNode(self.order)\n            new_root.children.append(self.root)\n            new_root.split_child(0)\n            self.root = new_root\n        \n        self.root.insert_non_full(key, value)\n    \n    def search(self, key: any) -> any:\n        \"\"\"Search for key\"\"\"\n        return self.root.search(key)\n```\n\n### Hash Index\n```python\nclass HashIndex:\n    \"\"\"Hash-based index\"\"\"\n    \n    def __init__(self, size: int = 100):\n        self.buckets = [[] for _ in range(size)]\n        self.size = size\n    \n    def _hash(self, key: any) -> int:\n        return hash(key) % self.size\n    \n    def insert(self, key: any, value: any):\n        \"\"\"Insert with hash collision handling\"\"\"\n        bucket = self._hash(key)\n        self.buckets[bucket].append((key, value))\n    \n    def search(self, key: any) -> any:\n        \"\"\"Search for key\"\"\"\n        bucket = self._hash(key)\n        for k, v in self.buckets[bucket]:\n            if k == key:\n                return v\n        return None\n```\n\n### Composite Index\n```python\nclass CompositeIndex:\n    \"\"\"Multi-column index\"\"\"\n    \n    def __init__(self, columns: List[str]):\n        self.columns = columns\n        self.index = {}\n    \n    def create_key(self, row: dict) -> tuple:\n        \"\"\"Create composite key from row\"\"\"\n        return tuple(row.get(col) for col in self.columns)\n    \n    def insert(self, row: dict):\n        \"\"\"Insert row into index\"\"\"\n        key = self.create_key(row)\n        self.index[key] = row\n    \n    def search(self, **criteria) -> List[dict]:\n        \"\"\"Search using partial key\"\"\"\n        partial = tuple(criteria.get(col) for col in self.columns)\n        \n        return [row for key, row in self.index.items()\n                if key[:len(partial)] == partial]\n```\n\n### Index Selection\n```python\nclass IndexSelector:\n    \"\"\"Choose appropriate indexes\"\"\"\n    \n    @staticmethod\n    def recommend_indexes(queries: List[dict]) -> List[dict]:\n        \"\"\"Recommend indexes based on queries\"\"\"\n        recommendations = []\n        \n        for query in queries:\n            if \"WHERE\" in query:\n                recommendations.append({\n                    \"columns\": query[\"where_columns\"],\n                    \"type\": \"btree\",\n                    \"reason\": \"Equality\u002Frange query\"\n                })\n            \n            if \"ORDER BY\" in query:\n                recommendations.append({\n                    \"columns\": query[\"order_columns\"],\n                    \"type\": \"btree\",\n                    \"reason\": \"Sort optimization\"\n                })\n        \n        return recommendations\n```\n","## What I do\n- Design efficient indexes\n- Choose index types\n- Optimize query performance\n\n## When to use me\nWhen creating database indexes.\n\n## Index Types\n\n### B-Tree Index\n```python\nclass BTreeIndex:\n    \"\"\"B-Tree index implementation\"\"\"\n    \n    def __init__(self, order: int = 3):\n        self.root = BTreeNode(order)\n        self.order = order\n    \n    def insert(self, key: any, value: any):\n        \"\"\"Insert key-value pair\"\"\"\n        root = self.root\n        \n        if root.is_full():\n            new_root = BTreeNode(self.order)\n            new_root.children.append(self.root)\n            new_root.split_child(0)\n            self.root = new_root\n        \n        self.root.insert_non_full(key, value)\n    \n    def search(self, key: any) -> any:\n        \"\"\"Search for key\"\"\"\n        return self.root.search(key)\n```\n\n### Hash Index\n```python\nclass HashIndex:\n    \"\"\"Hash-based index\"\"\"\n    \n    def __init__(self, size: int = 100):\n        self.buckets = [[] for _ in range(size)]\n        self.size = size\n    \n    def _hash(self, key: any) -> int:\n        return hash(key) % self.size\n    \n    def insert(self, key: any, value: any):\n        \"\"\"Insert with hash collision handling\"\"\"\n        bucket = self._hash(key)\n        self.buckets[bucket].append((key, value))\n    \n    def search(self, key: any) -> any:\n        \"\"\"Search for key\"\"\"\n        bucket = self._hash(key)\n        for k, v in self.buckets[bucket]:\n            if k == key:\n                return v\n        return None\n```\n\n### Composite Index\n```python\nclass CompositeIndex:\n    \"\"\"Multi-column index\"\"\"\n    \n    def __init__(self, columns: List[str]):\n        self.columns = columns\n        self.index = {}\n    \n    def create_key(self, row: dict) -> tuple:\n        \"\"\"Create composite key from row\"\"\"\n        return tuple(row.get(col) for col in self.columns)\n    \n    def insert(self, row: dict):\n        \"\"\"Insert row into index\"\"\"\n        key = self.create_key(row)\n        self.index[key] = row\n    \n    def search(self, **criteria) -> List[dict]:\n        \"\"\"Search using partial key\"\"\"\n        partial = tuple(criteria.get(col) for col in self.columns)\n        \n        return [row for key, row in self.index.items()\n                if key[:len(partial)] == partial]\n```\n\n### Index Selection\n```python\nclass IndexSelector:\n    \"\"\"Choose appropriate indexes\"\"\"\n    \n    @staticmethod\n    def recommend_indexes(queries: List[dict]) -> List[dict]:\n        \"\"\"Recommend indexes based on queries\"\"\"\n        recommendations = []\n        \n        for query in queries:\n            if \"WHERE\" in query:\n                recommendations.append({\n                    \"columns\": query[\"where_columns\"],\n                    \"type\": \"btree\",\n                    \"reason\": \"Equality\u002Frange query\"\n                })\n            \n            if \"ORDER BY\" in query:\n                recommendations.append({\n                    \"columns\": query[\"order_columns\"],\n                    \"type\": \"btree\",\n                    \"reason\": \"Sort optimization\"\n                })\n        \n        return recommendations\n```\n",{"name":5,"license":15,"metadata":28,"description":7,"compatibility":20},{"audience":29,"category":21},"developers","main","2ea764e153842704d24a2e9aa4170441a7f03081",".opencode\u002Fskills\u002Findexing","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito","https:\u002F\u002Fgithub.com\u002FNeuralBlitz","nuralnexus.org","Independent Multidisciplinary Researcher, Creative Producer, and AI Systems Architect\r\nAn independent creator and research-driven systems designer.","2026-11-12 03:13:05",{"source":8,"skillName":5,"commands":39,"prompt":49,"zipAvailable":50},[40,43,46],{"tool":41,"command":42},"npx","npx skills add NeuralBlitz\u002FMito --skill indexing",{"tool":44,"command":45},"bunx","bunx skills add NeuralBlitz\u002FMito --skill indexing",{"tool":47,"command":48},"pnpm","pnpm dlx skills add NeuralBlitz\u002FMito --skill 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