Pinecone
Skill ActiveManaged vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
To enable users to leverage Pinecone as a fully managed, auto-scaling vector database for production AI applications like RAG, recommendation systems, and semantic search at scale, without needing to manage underlying infrastructure.
Features
- Managed serverless vector database
- Auto-scaling to billions of vectors
- Low latency (<100ms p95)
- Hybrid search (dense + sparse)
- Metadata filtering and namespaces
Use Cases
- Production RAG applications
- Recommendation systems
- Semantic search at scale
- Building serverless AI applications
Non-Goals
- Self-hosted vector databases (Chroma, Weaviate)
- Offline similarity search (FAISS)
- Managing Pinecone infrastructure directly
Security
- warning:Secret ManagementThe example code shows the API key being passed directly as a string (`api_key="your-api-key"`), which is not best practice for production. It should ideally be loaded from environment variables or a secure configuration.
Trust
- warning:Issues AttentionIn the last 90 days, there were 17 issues opened and 4 closed, indicating a low closure rate of 23.5% for the volume of open issues.
Code Execution
- info:ValidationThe example Python code demonstrates basic parameter passing to the Pinecone client but does not explicitly show schema validation libraries like Zod or Pydantic for input arguments.
- info:Error HandlingThe provided Python code examples do not explicitly show try-catch blocks or structured error reporting for Pinecone client operations, though the library itself likely handles some errors.
Errors
- info:Actionable error messagesWhile the Pinecone client library is expected to provide error messages, the provided examples do not explicitly demonstrate how these errors are caught, framed, or remediated by the skill itself.
Execution
- info:Pinned dependenciesThe skill declares `pinecone-client` as a dependency but does not provide a lockfile or explicitly state a minimum version requirement for the interpreter or the dependency, beyond what the package manager might enforce.
Protocol
- info:Idempotent retry & timeoutsThe examples do not explicitly detail idempotency for mutations or hard timeouts per call, though the underlying Pinecone client library may implement some of these.
Practical Utility
- info:Edge casesWhile the skill covers common operations, explicit documentation for failure modes (e.g., API rate limits, invalid credentials, dimension mismatches) and their recovery steps is not detailed within the SKILL.md or README.
Safety
- info:Halt on unexpected statePreconditions for operations like matching embedding dimensions are implied but not explicitly listed as machine-readable checks or documented as halting conditions in the SKILL.md.
Installation
npx skills add davila7/claude-code-templatesRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
Trust Signals
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