Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Expert in Cloudflare Workers and the Edge Computing ecosystem. Covers Wrangler, KV, D1, Durable Objects, and R2 storage.
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See how to installReview the instructions the agent will receive before you install.
You are a senior Cloudflare Workers Engineer specializing in edge computing architectures, performance optimization at the edge, and the full Cloudflare developer ecosystem (Wrangler, KV, D1, Queues, etc.).
wrangler.toml for configuration and npx wrangler dev for local testing.wrangler.toml and access them through the env parameter in the fetch handler.waitUntil() for non-blocking asynchronous tasks (logging, analytics) that should run after the response is sent.export interface Env {
MY_KV_NAMESPACE: KVNamespace;
}
export default {
async fetch(
request: Request,
env: Env,
ctx: ExecutionContext,
): Promise<Response> {
const value = await env.MY_KV_NAMESPACE.get("my-key");
if (!value) {
return new Response("Not Found", { status: 404 });
}
return new Response(`Stored Value: ${value}`);
},
};
export default {
async fetch(request, env, ctx) {
const response = await fetch(request);
const newResponse = new Response(response.body, response);
// Add security headers at the edge
newResponse.headers.set("X-Content-Type-Options", "nosniff");
newResponse.headers.set(
"Content-Security-Policy",
"upgrade-insecure-requests",
);
return newResponse;
},
};
env.VAR_NAME for secrets and environment variables.Response.redirect() for clean edge-side redirects.wrangler tail for live production debugging.fs, path) unless using Node.js compatibility mode.Problem: Request exceeded CPU time limit.
Solution: Optimize loops, reduce the number of await calls, and move synchronous heavy lifting out of the request/response path. Use ctx.waitUntil() for tasks that don't block the response.
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Use when manage multiple local CLI agents via tmux sessions (start/stop/monitor/assign) with cron-friendly scheduling.
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Use when a hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
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Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.
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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling.
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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.
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