Qdrant
DatabaseCommonly used with
Skills using Qdrant (29)
omni-settings
Read and update global application settings: system prompts, thinking budget, IP filters, payload rules, combo defaults, and require-login configuration.
index
Reindexes KB for semantic search via vector store (Qdrant). Triggers: reindex KB, rebuild index, vector reindex, refresh embeddings.
skill-builder
Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.
chroma
Embedding database for RAG and semantic search.
onboarding
First-run onboarding — guides new users through Genesis setup on their first CC session. Configures user profile, essential API keys, Telegram, GitHub backup, and service verification. Triggered automatically when ~/.genesis/setup-complete is absent. Re-runnable by asking Genesis to "run setup" or "reconfigure [section]".
assessing-vector-and-embedding-weaknesses
Test vector stores for embedding inversion, cross-tenant leakage, and poisoning.
retrieval
Retrieval - vector DBs, embeddings, hybrid search, reranking.
hunt-rag-vector
Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from one-shot indirect prompt injection, which is owned by hunt-llm-ai), cross-tenant vector-database IDOR (unauthenticated or unscoped queries against Pinecone/Weaviate/Chroma/Milvus/Qdrant/pgvector), source-text/metadata leakage in similarity-search results, and retrieval-hijack via adversarial embedding proximity ('SEO poisoning' for RAG). Targets: any app with a shared knowledge base, document upload feeding a chatbot, or a directly reachable vector-DB port. Validate: a second, clean session/account must inherit a poisoned result, or a cross-tenant artifact must be independently verifiable — confabulation is not a finding, same bar as hunt-llm-ai. Use when target is RAG-backed, exposes a vector-DB port, or lets users upload documents that other users' queries later retrieve.
ai-engine-wordpress-mcp-server-and-ai-automation
AI Engine is a WordPress plugin by Meow Apps that connects sites to OpenAI, Claude, Gemini, and other models while exposing WordPress actions through MCP and REST interfaces. This skill helps agents configure providers, enable the plugin's MCP capabilities, and automate content, chatbots, media, and site-management workflows from WordPress.
agenticx-memory-architect
Guide for setting up and using the AgenticX memory system including Mem0 integration, long-term memory, context management, and memory-enhanced agents. Use when the user wants to add memory to agents, persist conversation history, build memory-aware workflows, or integrate with Mem0 for long-term recall.
agenticx-quickstart
AgenticX zero-to-hero quickstart guide. Use when the user wants to get started with AgenticX, create their first project, build their first agent, or run their first workflow. Covers installation, project scaffolding, agent creation, task execution, and CLI basics.
review-pr-comments
Fetch PR review comments via gh CLI, classify them (valid & fix / valid & skip / invalid), fix the valid ones, reply and resolve the threads, and fix any CI failures. Invoke only when the user types /review-pr-comments — never auto-trigger.
ai-native-development
Build AI-first applications with RAG pipelines, embeddings, vector databases, agentic workflows (ReAct, multi-agent, Opus 4.5), LLM integration, prompt engineering, streaming, and cost optimization. Use when: building an AI feature, integrating an LLM, setting up vector search, or designing agent architectures. Triggers on: AI app, LLM integration, RAG, vector database, agentic, prompt engineering, AI-native, semantic search, multi-agent, embeddings pipeline, cost optimization
sota-databases
State-of-the-art database engineering rules (2026) for designing, building, and auditing data layers. Covers engine selection, schema modeling, migrations, query and index craft, transactions and concurrency, reliability and scale, security, and vector/AI workloads. Use when designing a new data layer, writing or reviewing schemas/migrations/queries, debugging slow or contended database workloads, or auditing an existing database for correctness, performance, and security. Not for ETL or streaming data pipelines — use sota-data-engineering. Trigger keywords: database, SQL, Postgres, schema, migration, index, query, ORM, transaction, NoSQL, Redis, vector DB, pgvector, replication, partitioning, connection pool, RLS, EXPLAIN, deadlock, sharding, caching, SurrealDB, SurrealQL, Qdrant, multi-model, graph database.
ccc-data
complete data ecosystem — 8 skills in one. Data pipelines, SQL optimization, visualization, machine learning, data quality, analytics, reporting, and vector search.
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
vaultspec-adr
Capture an architectural decision as an ADR in .vault/adr/. Use after research, before planning, when a significant design choice and its trade-offs must be recorded.
vaultspec-code-research
Ground a coding task in real source code, reference implementations, and library docs. Use before implementing a complex feature or when documentation is thin.
vaultspec-code-review
Run a formal code review for safety, intent, and quality. Use to verify completed work before marking it done.
vaultspec-curate
Reconcile the ADR architecture corpus against the codebase and the feature lifecycle documents against the single-home-fact boundary. Use to audit ADR status and supersession, find ADR-vs-ADR, ADR-vs-code, and document-vs-document conflicts (restated grounding, displaced decisions, forked facts), and action them. Mechanical .vault/ hygiene is the CLI's job; this skill does the semantic reconciliation the CLI cannot.
vaultspec-documentation
Write one polished user-facing document through a structured pipeline. Use to create or rewrite a README, guide, or feature doc.
vaultspec-execute
Execute an approved implementation plan, dispatching agent personas per step. Use when a plan document is ready to build.
vaultspec-projectmanager
Coordinate GitHub Projects: triage issues, track milestones, provision worktrees, manage releases. Use for project management outside the pipeline.
vaultspec-rag-discovery
Semantic codebase and architecture-decision discovery with vaultspec-rag - find code and the ADRs that govern it by meaning, then narrow with advanced filters and noise controls. Use to locate where or how something is done, or the decision behind it, instead of guessing identifiers or sweeping with keyword/grep search.
vaultspec-research
Explore an unfamiliar problem and weigh options before committing. Use when unsure how to approach a complex feature, refactor, or bug.
vaultspec-team
Start a multi-agent coding team for a hard challenge. Use when a problem is too large for a single agent.
vaultspec-write
Write an implementation plan of waves, phases, and steps. Use only after the authorizing ADR - or the cluster of ADRs a roll-up plan executes - is approved.
senior-rag-engineer
Use when designing, building, reviewing, or operating retrieval augmented generation systems: corpus parsing, chunking, embedding, indexing, retrieval (semantic, lexical, hybrid), reranking, citation, evaluation, and ingestion freshness. Covers vector stores (pgvector, Pinecone, Weaviate, Qdrant, Vespa, Milvus, Elastic kNN), embedding models (text-embedding-3, bge-large, nomic-embed, voyage, cohere), BM25 and reciprocal rank fusion, cross encoder rerankers, ColBERT, MMR, and retrieval specific evaluation. Triggers: RAG, retrieval augmented generation, retrieval, embedding, vector store, hybrid search, BM25, reranker, citation, chunking, document parsing, freshness, retrieval eval, recall, precision, NDCG, MRR. Produces parsing plans, chunking configs, vector store schemas, hybrid retrieval pipelines, retrieval eval harnesses. Not for the LLM app around retrieval, see senior-llm-app-engineer; not for eval harness rigor, see senior-eval-engineer.
rag-specialist
Build Retrieval Augmented Generation (RAG) pipelines with vector databases, embeddings, and context-aware responses. Adapted from Anthropic's Claude Cookbooks.
Integration detected automatically from skill content. Some results may be false positives.