Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add G1Joshi/Agent-Skills --skill serverlessgit clone --depth 1 https://github.com/G1Joshi/Agent-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/g1joshi/agent-skills/serverless)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/serverless"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/serverless/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/g1joshi/agent-skills/serverless"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/serverless.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00056 | $0.01187 |
| Opus 5.5 | $0.00022 | $0.00475 |
| Sonnet 5.5 | $0.00011 | $0.00237 |
| Haiku 4.5 | $0.00006 | $0.00119 |
Grade A, and why
serverless scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 17d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 158 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Serverless
Serverless is a cloud-native development model for building and running applications without managing servers. The cloud provider handles the routine work of provisioning, maintaining, and scaling the server infrastructure.
When to Use
- Event-Driven Workloads: Processing file uploads (S3), stream events (Kafka/Kinesis), and asynchronous queue workers.
- Variable & Spiky Traffic: Applications with unpredictable request volumes scaling automatically from 0 to thousands of instances.
- Low-Maintenance Web APIs: Deploying micro-APIs or webhook handlers without managing servers, operating systems, or patching.
- Scheduled Background Tasks: Running cron jobs, batch data transformations, and reporting tasks via EventBridge / CloudWatch.
Quick Start
// AWS Lambda Handler (Node.js)
export const handler = async (event) => {
console.log("Event received:", JSON.stringify(event));
const name = event.queryStringParameters?.name || "World";
return {
statusCode: 200,
body: JSON.stringify({ message: `Hello, ${name}!` }),
};
};
# serverless.yml (Serverless Framework)
service: my-api
provider:
name: aws
runtime: nodejs20.x
functions:
hello:
handler: handler.hello
events:
- httpApi:
path: /hello
method: get
Core Concepts
Ephemeral Stateless Execution
Instances spin up on demand and shut down after idle periods; in-memory state is destroyed when containers terminate:
// AWS Lambda / Cloudflare Worker Handler
export const handler = async (
event: APIGatewayProxyEvent,
): Promise<APIGatewayProxyResult> => {
// Global variables persist across warm starts, but local handler state resets
const result = await processItem(event.body);
return {
statusCode: 200,
headers: { "Content-Type": "application/json" },
body: JSON.stringify(result),
};
};
Cold Start Lifecycle & Mitigation
Understanding initialization phases:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 17d ago Changed · +1 lines 27455e5efe86
- 21d ago Changed · +60 lines · +36 tokens per session f0912582eaa7
- 1mo ago First seen · 97 lines · 20 tokens per session scan A 7136a3f9394d
serverless is a skill published in the GitHub repository G1Joshi/Agent-Skills (15 stars, last pushed 18d ago), licensed MIT. It adds 56 tokens to every session and 1,187 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
shipping-and-launch
Prepares production launches. Use when preparing to deploy to production, or when asking what needs to be in place before shipping. Use when you need a pre-launch checklist, when setting up monitoring, when planning a staged rollout, or when you need a rollback strategy.
cost-optimization
Guides systematic cloud and infrastructure cost reduction. Use when cloud bills are growing unpredictably, when rightsizing resources, when evaluating reserved capacity, or when building cost-aware architecture. Use when you need to optimize spend without sacrificing reliability or performance.
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high…
git-workflow-and-versioning
Structures git workflow practices. Use when making any code change. Use when committing, branching, resolving conflicts, splitting uncommitted work in a messy working tree into clean atomic commits, opening or reviewing a pull request (PR), pushing to a remote, or when you need to organize work across multiple…
observability-and-instrumentation
Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.