vector-db
Vector Databases
What Vector Databases Do
Vector databases store high-dimensional numerical representations (embeddings) and enable fast similarity search. Unlike traditional databases that match exact values, vector databases find the closest vectors to a query vector, enabling semantic matching.
Core capabilities:
- Store embeddings alongside metadata and original content
- Perform approximate nearest neighbor (ANN) search at scale
- Filter results by metadata combined with vector similarity
- Handle millions to billions of vectors with sub-second query times
Embedding Basics
An embedding is a fixed-length array of floats capturing semantic meaning. Text with similar meaning produces vectors that are close together in the embedding space.
- Dimensions: Vector length. Common sizes: 384, 768, 1536, 3072. Higher = more nuance, more cost.
- Embedding model: Converts raw data into vectors. Different models produce different dimensions.
- Distance metric: How similarity between two vectors is measured.
More from 1mangesh1/dev-skills-collection
curl-http
HTTP request construction and API testing with curl and HTTPie. Use when user asks to "test API", "make HTTP request", "curl POST", "send request", "test endpoint", "debug API", "upload file", "check response time", "set auth header", "basic auth with curl", "send JSON", "test webhook", "check status code", "follow redirects", "rate limit testing", "measure API latency", "stress test endpoint", "mock API response", or any HTTP calls from the command line.
28database-indexing
Database indexing internals, index type selection, query plan analysis, and write-overhead tradeoffs across PostgreSQL, MySQL, and MongoDB. Use when user asks to "optimize queries", "create indexes", "fix slow queries", "read EXPLAIN output", "reduce query time", "index strategy", "database performance", "composite index", "covering index", "partial index", "index bloat", "unused indexes", or needs help diagnosing and resolving database performance problems.
13testing-strategies
Testing strategies, patterns, and methodologies across the full testing spectrum. Use when asked about unit tests, integration tests, e2e tests, test pyramid, mocking, test doubles, TDD, property-based testing, snapshot testing, test coverage, mutation testing, contract testing, performance testing, test data management, CI/CD testing, flaky tests, test anti-patterns, test organization, test isolation, test fixtures, test parameterization, or any testing strategy, approach, or methodology.
10secret-scanner
This skill should be used when the user asks to "scan for secrets", "find API keys", "detect credentials", "check for hardcoded passwords", "find leaked tokens", "scan for sensitive keys", "check git history for secrets", "audit repository for credentials", or mentions secret detection, credential scanning, API key exposure, token leakage, password detection, or security key auditing.
10terraform
Terraform infrastructure as code for provisioning, modules, state management, and workspaces. Use when user asks to "create infrastructure", "write Terraform", "manage state", "create module", "import resource", "plan changes", or any IaC tasks.
10kubernetes
Kubernetes and kubectl mastery for deployments, services, pods, debugging, and cluster management. Use when user asks to "deploy to k8s", "create deployment", "debug pod", "kubectl commands", "scale service", "check pod logs", "create ingress", or any Kubernetes tasks.
10