Cursor Rules Template: Log Analytics with Ingestion, Search, Filters, Dashboards and NLQ

Overview

Cursor rules configuration for a log analytics stack defines how Cursor AI assists engineers in ingestion, indexing, search, filtering, dashboards, and natural language querying. This template targets a modern stack using a streaming ingestion path, OpenSearch for indexing and search, Grafana for dashboards, and NLQ to translate natural language queries into precise search and filter expressions. It provides concrete guardrails so Cursor AI writes safe, observable, and maintainable code in this stack.

When to Use These Cursor Rules

• When building an end-to-end log analytics platform with real-time ingestion and indexing.
• When enabling complex search across vast log datasets with filters and aggregations.
• When constructing dashboards in Grafana with NLQ-driven queries.
• When enforcing secure data handling, authentication, and least-privilege access.
• When you need a repeatable, copyable rules block to bootstrap new projects.

Copyable .cursorrules Configuration

FrameworkRole: Cursor AI Rules for a Log Analytics Platform
FrameworkContext: Ingest logs from diverse sources, index into OpenSearch, provide search and filters, expose dashboards in Grafana, and support NLQ.
CodeStyleAndGuides: TypeScript with ESLint and Prettier; strict types; consistent naming; no console.log in production code.
ArchitectureAndDirectoryRules:
  ingestion: src/ingestion
  indexing: src/indexing
  search: src/search
  dashboards: src/dashboards
  nlq: src/nlq
  tests: tests
AuthenticationAndSecurity:
  - auth: JWT + API keys
  - tls: TLS 1.2+
  - secretManagement: vault
DatabaseAndOrm:
  - indexStore: OpenSearch
  - dataLake: s3
  - orm: none
TestingAndLinting:
  - unit: jest
  - integration: cypress
  - lint: eslint, prettier
ProhibitedActionsAndAntiPatterns:
  - doNotUsePrismaOrMongoose
  - doNotSkipTls
  - doNotInlineSecrets

Recommended Project Structure

project-root/
  ingestion/
  indexing/
  search/
  dashboards/
  nlq/
  tests/
  config/

Core Engineering Principles

• Explicit data contracts and interface boundaries between ingestion, indexing, and search.
• Defensive defaults: secure by default, least privilege, and auditable changes.
• Observability: structured logs, metrics, and tracing for Cursor-driven actions.
• Idempotent operations across ingestion and indexing to handle retries cleanly.
• Deterministic code construction: modular, testable components with clear ownership.

Code Construction Rules

• Ingestion path must normalize timestamps and preserve original log data for audit.
• OpenSearch indexing should follow a per-tenant index pattern and avoid hot-writes to avoid contention.
• Search queries must use parameterized DSL with explicit filters to prevent injection and ensure index discipline.
• NLQ should map natural language intents to predefined OpenSearch queries or Grafana dashboard filters, with fallback to safe defaults.
• All code must be covered by unit tests; integration tests must simulate end-to-end ingestion to a test OpenSearch cluster.

Security and Production Rules

• Use TLS for all in-transit data; rotate secrets; store credentials in a vault.
• Implement role-based access control and enforce least privilege in dashboards and log access.
• Protect dashboards and NLQ endpoints with authentication; audit every NLQ operation that touches data.
• Do not log sensitive fields; redact and scrub PII before indexing.
• Run production-grade linting, testing, and CI checks on every push.

Testing Checklist

• Unit tests for ingestion/ parsing logic; mock OpenSearch interactions.
• Integration tests for end-to-end ingestion to a test OpenSearch instance.
• End-to-end tests for NLQ mapping to Grafana filters; verify permissions.
• Security tests: TLS, JWT validation, and access control checks.
• CI/CD: lint, type checks, and test suite run on every PR.

Common Mistakes to Avoid

• Hardcoding secrets or embedding credentials in code or config blocks.
• Assuming a single monolithic indexing path; ignore per-tenant or multi-region considerations.
• Using unsafe query construction that risks injection or leakage of sensitive data.
• Skipping tests for ingestion logic or NLQ mappings; neglecting observability.

Related Cursor rules templates

Explore adjacent Cursor rules templates for similar stacks, workflows, and production constraints.

• KPI Dashboard Builder — Cursor Rules Template for Cursor AI
• Cursor Rules Template: ERP Dashboard with Next.js, PostgreSQL, Prisma, Clerk and Shadcn UI
• Cursor Rules Template: BI SaaS with Data Connectors, Charts, Dashboards, SQL Editor and AI Explanations
• Cursor Rules Template: Manufacturing Execution Dashboard (NestJS + PostgreSQL)

FAQ

What is this Cursor Rules Template for Log Analytics?

This Cursor Rules Template provides a concrete set of guardrails for building a log analytics platform with ingestion, OpenSearch indexing, search, filters, dashboards, and NLQ. It defines a structure for files, directories, and security rules, plus a ready-to-paste .cursorrules block that you can drop into your project root to bootstrap development with Cursor AI.

Which stack components are covered?

The template targets a stack with ingestion (log sources into a streaming processor), OpenSearch as the search/index layer, Grafana for dashboards, and an NLQ layer to translate natural language queries into search expressions. It enforces security, testing, and maintainability specific to this stack.

How do I paste the .cursorrules into my project?

Copy the code block from the Copyable .cursorrules Configuration section and paste it into a new file named .cursorrules at the project root. Ensure your repository includes the Ingestion and OpenSearch client setups referenced by the rules.

How does NLQ integrate with dashboards?

NLQ maps user questions to predefined dashboard filters and OpenSearch queries. It should default to safe, read-only operations, log NLQ actions for audit, and offer fallbacks if a query cannot be resolved.

What are the recommended security practices?

Use TLS for all traffic, rotate secrets via a vault, implement JWT-based access control, redact sensitive fields before indexing, and restrict dashboard access per user role. Regularly review access tokens and log NLQ events for traceability.