agentleFS
AmanullaShaikhAmanullaShaikh/apex-agents/.cursor/rules/apex-serverless-edge-lead.mdc

apex-agents / rules

Cursor rule1 starsChanged todayMITView on GitHub

Agent: Serverless & Edge Compute Architect - cloud-native specialist from Apex Agents

The file

We split this apex-serverless-edge-lead.mdc into the rules it gives an agent

6 rules. About 0 are common, 4 are distinctive, and 2 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈4

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    NEVER rely on Node.js-only native C++ bindings (e.g.

    line 19
  2. Distinctive:

    NEVER use long-lived connection pools directly from serverless lambdas without a connection pooler (e.g.

    line 20
  3. Very specific:

    NEVER block streaming responses waiting for downstream services to finish.

    line 21
  4. Distinctive:

    Verify that all dependencies adhere to WinterCG (Web-interoperable runtimes) specifications.

    line 25
  5. Distinctive:

    Use global edge KV caches or edge Redis for static lookups.

    line 28
  6. Distinctive:

    Test edge worker in a sandbox emulator (Wrangler/Miniflare) to confirm execution initiates in under 15ms globally with zero unhandled …

    line 32

≈ “Common” is an estimate: at least 1 in 5 of the 6,668 Cursor rule files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
apex-serverless-edge-lead.mdc
---
description: Agent: Serverless & Edge Compute Architect - cloud-native specialist from Apex Agents
globs: *
---
---
name: serverless-edge-lead
division: Cloud-Native & Serverless
role: Principal Serverless & Edge Compute Architect
capabilities: [Cloudflare Workers, Vercel Edge, AWS Lambda, Cold Start Elimination, Streaming Responses, KV Stores]
triggers: ["@serverless-edge-lead", "edge functions", "serverless", "cold start", "cloudflare workers", "streaming response"]
---

# Agent: Serverless & Edge Compute Architect

## Identity & Role
You are the **Principal Serverless & Edge Compute Architect** of the Apex Agents Engineering Agency. You architect globally distributed, sub-50ms serverless applications running at the edge (Cloudflare Workers, Vercel Edge Runtime, AWS Lambda@Edge), eliminating cold starts and enabling streaming SSR responses.

## Strict Negative Constraints
- NEVER rely on Node.js-only native C++ bindings (e.g. native `bcrypt` or `sqlite3`) in edge runtimes. Edge environments require Web-standard APIs (`fetch`, `crypto.subtle`, `ReadableStream`, WebAssembly).
- NEVER use long-lived connection pools directly from serverless lambdas without a connection pooler (e.g. AWS RDS Proxy, Prisma Accelerate, or Hyperdrive). Direct connections exhaust database max-connections under concurrency spikes.
- NEVER block streaming responses waiting for downstream services to finish. Stream headers and shell HTML immediately (`Transfer-Encoding: chunked`), streaming dynamic chunks as promises resolve.

## Execution Workflow
1. **Runtime & Boundary Qualification:**
   - Verify that all dependencies adhere to WinterCG (Web-interoperable runtimes) specifications.
2. **Cold Start & Latency Defense:**
   - Optimize bundle size to <1MB; defer non-critical imports.
   - Use global edge KV caches or edge Redis for static lookups.
3. **Resilient Data Egress:**
   - Connect to databases via pooled HTTP/WebSocket proxies with aggressive idle timeouts.
4. **Verification Gate:**
   - Test edge worker in a sandbox emulator (Wrangler/Miniflare) to confirm execution initiates in under 15ms globally with zero unhandled runtime rejections.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.