[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6BXKKPCCGTSYAWNN3AVC601":3,"skill-same:01M6BXKKPCCGTSYAWNN3AVC601":57},{"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":27,"linkOnly":28,"markdownContent":29,"markdownBody":30,"frontMatter":31,"refName":35,"commitSha":36,"skillDirectory":37,"forksCount":38,"repositoryHtmlUrl":39,"repositoryHomepageUrl":6,"ownerHtmlUrl":40,"ownerBlogUrl":6,"ownerDescription":41,"updatedAt":42,"install":43,"resources":56},"01M6BXKKPCCGTSYAWNN3AVC601","neo4j-query",null,"Query Neo4j knowledge graph for entities, relationships, and graph analysis.","OriNachum\u002Fautonomous-intelligence","OriNachum","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F20955789?v=4","01M6BXKB8XY7RHN166G1TY4FRF","Ori Nachum","01M6BXKBCPVY67KNQ7D7QFH1PD",245,"MIT","https:\u002F\u002Fgithub.com\u002FOriNachum\u002Fautonomous-intelligence\u002Fblob\u002Fmain\u002Fqq\u002F.agent\u002Fskills\u002Fneo4j-query\u002FSKILL.md",[],[19,20,21,22,23,24,25,26],"neo4j","neo4j query","graph","knowledge graph","graph query","analysis","cypher","knowledge","2026-11-10 10:12:30",false,"---\nname: neo4j-query\ndescription: Query Neo4j knowledge graph for entities, relationships, and graph analysis.\ntriggers:\n  - neo4j\n  - knowledge graph\n  - entities\n  - relationships\n  - graph query\n  - cypher\n---\n\n# Neo4j Query Skill\n\nQuery the Neo4j knowledge graph to investigate entities, relationships, and graph structure.\n\n## Connection Details\n\n- **URI**: `bolt:\u002F\u002Flocalhost:7687` (or `NEO4J_URI` env var)\n- **User**: `neo4j` (or `NEO4J_USER` env var)\n- **Password**: `refinerypass` (or `NEO4J_PASSWORD` env var)\n\n## Python Usage\n\n```python\nfrom qq.knowledge.neo4j_client import Neo4jClient\n\n# Initialize client\nclient = Neo4jClient()\n\n# Execute raw Cypher query\nresults = client.execute(\"MATCH (n) RETURN n LIMIT 10\")\n\n# Get graph summary (entity and relationship counts by type)\nsummary = client.get_graph_summary()\n\n# Get entity by name\nentity = client.get_entity(\"entity_name\")\n\n# Get related entities (up to N hops)\nrelated = client.get_related_entities(\"entity_name\", depth=2, limit=20)\n\n# Close when done\nclient.close()\n```\n\n## Common Cypher Queries\n\n### Count entities and relationships\n```cypher\n-- Total entities\nMATCH (n) RETURN count(n) as total_entities\n\n-- Entities by type\nMATCH (n) RETURN labels(n)[0] as type, count(n) as count ORDER BY count DESC\n\n-- Total relationships\nMATCH ()-[r]->() RETURN count(r) as total_relationships\n\n-- Relationships by type\nMATCH ()-[r]->() RETURN type(r) as type, count(r) as count ORDER BY count DESC\n```\n\n### Find orphan entities (no relationships)\n```cypher\n-- Entities with no incoming or outgoing relationships\nMATCH (n) WHERE NOT (n)--() RETURN n.name, labels(n)[0] as type\n```\n\n### Analyze connectivity\n```cypher\n-- Entities with most connections\nMATCH (n)-[r]-() RETURN n.name, labels(n)[0] as type, count(r) as connections ORDER BY connections DESC LIMIT 10\n\n-- Distribution of relationship counts per entity\nMATCH (n)\nOPTIONAL MATCH (n)-[r]-()\nWITH n, count(r) as rel_count\nRETURN rel_count, count(n) as entities_with_this_count ORDER BY rel_count\n```\n\n### Sample entities and relationships\n```cypher\n-- Sample entities with their properties\nMATCH (n) RETURN n.name, labels(n)[0] as type, n.description LIMIT 20\n\n-- Sample relationships with endpoints\nMATCH (a)-[r]->(b) RETURN a.name, type(r), b.name LIMIT 20\n```\n\n## CLI Usage\n\n```bash\n# Use cypher-shell directly\ndocker exec -it qq-neo4j-1 cypher-shell -u neo4j -p refinerypass \"MATCH (n) RETURN count(n)\"\n```\n","# Neo4j Query Skill\n\nQuery the Neo4j knowledge graph to investigate entities, relationships, and graph structure.\n\n## Connection Details\n\n- **URI**: `bolt:\u002F\u002Flocalhost:7687` (or `NEO4J_URI` env var)\n- **User**: `neo4j` (or `NEO4J_USER` env var)\n- **Password**: `refinerypass` (or `NEO4J_PASSWORD` env var)\n\n## Python Usage\n\n```python\nfrom qq.knowledge.neo4j_client import Neo4jClient\n\n# Initialize client\nclient = Neo4jClient()\n\n# Execute raw Cypher query\nresults = client.execute(\"MATCH (n) RETURN n LIMIT 10\")\n\n# Get graph summary (entity and relationship counts by type)\nsummary = client.get_graph_summary()\n\n# Get entity by name\nentity = client.get_entity(\"entity_name\")\n\n# Get related entities (up to N hops)\nrelated = client.get_related_entities(\"entity_name\", depth=2, limit=20)\n\n# Close when done\nclient.close()\n```\n\n## Common Cypher Queries\n\n### Count entities and relationships\n```cypher\n-- Total entities\nMATCH (n) RETURN count(n) as total_entities\n\n-- Entities by type\nMATCH (n) RETURN labels(n)[0] as type, count(n) as count ORDER BY count DESC\n\n-- Total relationships\nMATCH ()-[r]->() RETURN count(r) as total_relationships\n\n-- Relationships by type\nMATCH ()-[r]->() RETURN type(r) as type, count(r) as count ORDER BY count DESC\n```\n\n### Find orphan entities (no relationships)\n```cypher\n-- Entities with no incoming or outgoing relationships\nMATCH (n) WHERE NOT (n)--() RETURN n.name, labels(n)[0] as type\n```\n\n### Analyze connectivity\n```cypher\n-- Entities with most connections\nMATCH (n)-[r]-() RETURN n.name, labels(n)[0] as type, count(r) as connections ORDER BY connections DESC LIMIT 10\n\n-- Distribution of relationship counts per entity\nMATCH (n)\nOPTIONAL MATCH (n)-[r]-()\nWITH n, count(r) as rel_count\nRETURN rel_count, count(n) as entities_with_this_count ORDER BY rel_count\n```\n\n### Sample entities and relationships\n```cypher\n-- Sample entities with their properties\nMATCH (n) RETURN n.name, labels(n)[0] as type, n.description LIMIT 20\n\n-- Sample relationships with endpoints\nMATCH (a)-[r]->(b) RETURN a.name, type(r), b.name LIMIT 20\n```\n\n## CLI Usage\n\n```bash\n# Use cypher-shell directly\ndocker exec -it qq-neo4j-1 cypher-shell -u neo4j -p refinerypass \"MATCH (n) RETURN count(n)\"\n```\n",{"name":5,"triggers":32,"description":7},[19,22,33,34,23,25],"entities","relationships","main","7f34d35030f36ee7a62c7fbab25446956053e524","qq\u002F.agent\u002Fskills\u002Fneo4j-query",15,"https:\u002F\u002Fgithub.com\u002FOriNachum\u002Fautonomous-intelligence","https:\u002F\u002Fgithub.com\u002FOriNachum","AI Expert, Tipalti, a Jetson AI Lab community leader, AWS Community Builder, A maintainer of dusty-nv\u002Fjetson-containers, Culture.dev and other OSS","2026-04-09 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pure-logic unit tests and optional integration tests against live TTS\u002FSTT services.","https:\u002F\u002Fgithub.com\u002FOriNachum\u002Fautonomous-intelligence\u002Fblob\u002Fmain\u002Frealtime-api\u002F.agent\u002Fskills\u002Ftest\u002FSKILL.md",[64],{"categoryNo":65,"categoryName":66,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[60,70,71,72,73,74,75,76,77],"tts","verify","services","integration","run","run tests","unit tests","integration tests",{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":84,"publishedAt":27},"01M6BXM2DCQE8ZQ2P9MHZFC3ZD","speak","Speak text aloud using the Magpie TTS container in the realtime-api Docker stack. Zero external dependencies — just curl + aplay.","https:\u002F\u002Fgithub.com\u002FOriNachum\u002Fautonomous-intelligence\u002Fblob\u002Fmain\u002Frealtime-api\u002F.agent\u002Fskills\u002Fspeak\u002FSKILL.md",[],[80,85,86,70,87,88,89,90],"text","stack","api","ZeRO","realtime","docker",{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":95,"categories":96,"keywords":105,"publishedAt":27},"01M6BXM0RKDRNRQ3Y76040A4ZH","deploy","Build and deploy the realtime-api Docker container with full verification that deployed code matches local source.","https:\u002F\u002Fgithub.com\u002FOriNachum\u002Fautonomous-intelligence\u002Fblob\u002Fmain\u002Frealtime-api\u002F.agent\u002Fskills\u002Fdeploy\u002FSKILL.md",[97,100],{"categoryNo":98,"categoryName":99,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[93,90,89,106,107,87,108],"code","verification","ship",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":119,"publishedAt":27},"01M6BXKXCQQM74B1TK7YH87Y2E","memory","Unified memory management for notes, knowledge graph, RAG search, and file analysis. Use when working with: (1) Core memory — protected identity, projects, relationships, and system facts that should never be forgotten, (2) Working notes — per-session ephemeral notes organized by section, (3) MongoDB RAG — vector-search-enabled notes with importance scoring, decay, deduplication, and archival, (4) Neo4j knowledge graph — entities, relationships, merge duplicates, reinforce mentions, Cypher queries, (5) File analysis — deep file reading that extracts knowledge into all memory layers, (6) Service initialization — health-check, start\u002Fstop MongoDB, Neo4j, TEI embeddings via docker-compose with partial setup support.","https:\u002F\u002Fgithub.com\u002FOriNachum\u002Fautonomous-intelligence\u002Fblob\u002Fmain\u002Fqq\u002Fskills\u002Fmemory\u002FSKILL.md",[115,116],{"categoryNo":98,"categoryName":99,"taxonomyType":67,"taxonomyTypeDesc":68},{"categoryNo":117,"categoryName":118,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000021","资料检索与研究",[111,26,120,121,122,123,19,25,124,24,90,125,22,126,127,128,129,130,131,132],"projects","system","archive","health","analyze","deduplication","file","vector","search","core","notes","remember","mongodb",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":140,"publishedAt":27},"01M6BXKVJAD0VDHBRMKXCVHNMK","coding","Enhanced coding assistance","https:\u002F\u002Fgithub.com\u002FOriNachum\u002Fautonomous-intelligence\u002Fblob\u002Fmain\u002Fqq\u002Fskills\u002Fcoding\u002FSKILL.md",[139],{"categoryNo":101,"categoryName":102,"taxonomyType":103,"taxonomyTypeDesc":104},[135,106,141,142,143,144,145],"debug","javascript","implement","python","typescript",{"skillNo":147,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":152,"publishedAt":27},"01M6BXKR9JFCTPFKM9QMH3DDD4","https:\u002F\u002Fgithub.com\u002FOriNachum\u002Fautonomous-intelligence\u002Fblob\u002Fmain\u002Fqq\u002F.claude\u002Fskills-test\u002Fmemory\u002FSKILL.md",[150,151],{"categoryNo":98,"categoryName":99,"taxonomyType":67,"taxonomyTypeDesc":68},{"categoryNo":117,"categoryName":118,"taxonomyType":67,"taxonomyTypeDesc":68},[111,90,125,121,120,26,123,19,25,24,132,122,22,126,127,128,129,130,131,124]]