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 agentmods add skills/aws/agent-toolkit-for-aws/aws-serverlessnpx skills add aws/agent-toolkit-for-aws --skill aws-serverlessgit clone --depth 1 https://github.com/aws/agent-toolkit-for-awsWrote 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/aws/agent-toolkit-for-aws/aws-serverless)<a href="https://agentmods.dev/skills/aws/agent-toolkit-for-aws/aws-serverless"><img src="https://agentmods.dev/badge/skills/aws/agent-toolkit-for-aws/aws-serverless.svg" alt="Measured on agentmods" 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 | $0.00168 | $0.01772 |
| Opus 5 | $0.00084 | $0.00886 |
| Sonnet 5 | $0.00034 | $0.00354 |
| Haiku 4.5 | $0.00017 | $0.00177 |
Grade A, and why
aws-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 4d 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AWS Serverless
Domain expertise for building serverless applications on AWS: Lambda, API Gateway, Step Functions, EventBridge, event source mappings, concurrency, cold starts, deployment, and troubleshooting.
Works best with the AWS MCP server — run CLI commands, query CloudWatch, validate configs directly. All guidance also works with standard AWS CLI access.
Specialized skills — check these first
These cover capabilities and procedures the general references below do not. Several are specialized features or step-by-step tested procedures you would otherwise miss. Route to the matching skill before falling back to the references.
Advanced Lambda compute (easy to overlook)
| Use this skill | When the workload involves |
|---|---|
| aws-lambda-microvms | Strong tenant isolation, sandboxed/untrusted code execution (AI agent code sandboxes, REPLs, notebooks, CI runners), long-lived sessions, suspend/resume with preserved state, port-listening servers (gRPC, WebSocket, custom TCP), Firecracker microVMs, snapshot-resumable compute, up to 8-hour lifetimes |
| aws-lambda-durable-functions | Durable execution, checkpoint-and-replay, long-running multi-step workflows written as plain code (TS/Python/Java), automatic state persistence, saga pattern in code, human-in-the-loop callbacks, executions up to 1 year, context.step/context.wait/context.invoke, withDurableExecution, durable-execution-sdk |
| aws-lambda-managed-instances | Lambda Managed Instances (LMI), capacity providers, EC2-backed Lambda, steady high-volume traffic (50M+ req/mo) wanting Savings Plans / Reserved Instance pricing, PerExecutionEnvironmentMaxConcurrency, CapacityProviderConfig, multi-concurrent execution environments |
Workflow orchestration
Route here when the user wants to coordinate multiple steps, services, or functions. Triggers include "orchestration", "workflow", "state machine", "multi-step coordination", "coordinate Lambda functions", "durable execution", "pipeline with retries", or intent to build saga/compensation, human-in-the-loop approval, fan-out, or long-running async coordination.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/powertools-handler.py 1.6 KB runs code
- references/api-gateway.md 5.9 KB
- references/architecture.md 6.5 KB
- references/concurrency.md 5.6 KB
- references/deployment.md 3.8 KB
- references/event-sources.md 8.2 KB
- references/lambda.md 9.8 KB
- references/orchestration.md 6.8 KB
- references/production.md 8.2 KB
- references/troubleshooting.md 10 KB
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.
- 4d ago First seen · 68 lines · 168 tokens per session scan A a3d2a30ab5bb
aws-serverless is a skill published in the GitHub repository aws/agent-toolkit-for-aws (2,518 stars, last pushed today), licensed Apache-2.0. It adds 168 tokens to every session and 1,772 once invoked, about $0.0008 per session on Opus 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…