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/itsmostafa/aws-agent-skills/lambdanpx skills add itsmostafa/aws-agent-skills --skill lambdagit clone --depth 1 https://github.com/itsmostafa/aws-agent-skillsWhat 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 | $0.00038 | $0.02057 |
| Opus 5 | $0.00019 | $0.01028 |
| Sonnet 5 | $0.00008 | $0.00411 |
| Haiku 4.5 | $0.00004 | $0.00206 |
Grade A, and why
lambda 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 yesterday.
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 — 344 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AWS Lambda
AWS Lambda runs code without provisioning servers. You pay only for compute time consumed. Lambda automatically scales from a few requests per day to thousands per second.
Table of Contents
Core Concepts
Function
Your code packaged with configuration. Includes runtime, handler, memory, timeout, and IAM role.
Invocation Types
| Type | Description | Use Case |
|---|---|---|
| Synchronous | Caller waits for response | API Gateway, direct invoke |
| Asynchronous | Fire and forget | S3, SNS, EventBridge |
| Poll-based | Lambda polls source | SQS, Kinesis, DynamoDB Streams |
Execution Environment
Lambda creates execution environments to run your function. Components:
- Cold start: New environment initialization
- Warm start: Reusing existing environment
- Handler: Entry point function
- Context: Runtime information
Layers
Reusable packages of libraries, dependencies, or custom runtimes (up to 5 per function).
Common Patterns
Create a Python Function
AWS CLI:
# Create deployment package
zip function.zip lambda_function.py
# Create function
aws lambda create-function \
--function-name MyFunction \
--runtime python3.12 \
--role arn:aws:iam::123456789012:role/lambda-role \
--handler lambda_function.handler \
--zip-file fileb://function.zip \
--timeout 30 \
--memory-size 256
# Update function code
aws lambda update-function-code \
--function-name MyFunction \
--zip-file fileb://function.zip
boto3:
import boto3
import zipfile
import io
lambda_client = boto3.client('lambda')
# Create zip in memory
zip_buffer = io.BytesIO()
with zipfile.ZipFile(zip_buffer, 'w') as zf:
zf.writestr('lambda_function.py', '''
def handler(event, context):
return {"statusCode": 200, "body": "Hello"}
''')
zip_buffer.seek(0)
# Create function
lambda_client.create_function(
FunctionName='MyFunction',
Runtime='python3.12',
Role='arn:aws:iam::123456789012:role/lambda-role',
Handler='lambda_function.handler',
Code={'ZipFile': zip_buffer.read()},
Timeout=30,
MemorySize=256
)
What ships with it
2 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.
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.
- yesterday First seen · 344 lines · 38 tokens per session scan A 79a78fcd14e8
lambda is a skill published in the GitHub repository itsmostafa/aws-agent-skills (1,150 stars, last pushed 8d ago), licensed MIT. It adds 38 tokens to every session and 2,057 once invoked, about $0.0002 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
aws-essentials
Use when standing up the core AWS surface a small product needs: hardening a fresh account, a private S3 bucket, encrypted RDS Postgres, ECS Fargate vs EC2, CloudFront + OAC, or scoping an IAM policy to least privilege. NOT the CI pipeline that ships the container (that is deployment), NOT app-code access-control…
automated-remediation
알려진 장애 패턴에 대해 사전 정의된 Runbook 기반 자동 복구를 실행한다. RCA 결과 또는 incident-response의 가설을 입력으로 받아 매칭되는 remediation playbook을 선택하고, 복구 전/후 상태를 검증하며, 실패 시 에스컬레이션한다. SEV2/3만 자동 복구 대상이며 SEV1은 사람 전용이다.
root-cause-analysis
인시던트 발생 시 관련 메트릭·로그·이벤트·변경 이력을 자동 수집하고 인과 관계를 추론하여 근본 원인을 식별한다. 타임라인 기반 이벤트 상관관계 분석, 변경-장애 매핑, 의존성 그래프 탐색을 수행하며 RCA 보고서를 자동 생성한다.
anomaly-detection
CloudWatch 메트릭과 Prometheus 시계열 데이터에서 통계적 이상 징후를 자동 탐지하여 incident-response의 입력 소스로 제공한다. 베이스라인 학습(7일 이동 평균 + 3σ), 다변량 상관 분석, 계절성 보정을 수행하며 탐지된 anomaly를 severity 분류하여 알람을 생성한다.
predictive-scaling
과거 트래픽 패턴과 시계열 예측을 기반으로 리소스 수요를 사전 예측하고 스케일링을 권고한다. 시간대별/요일별 계절성 분석, 이벤트 기반 수요 급증 예측, 비용 대비 성능 최적 구성 제안을 수행하며 cost-governance와 연동하여 예산 범위 내 스케일링을 보장한다.
slo-management
SLI 메트릭을 자동 수집하여 SLO 대비 추적하고, Error Budget 소진율에 따라 배포 게이트를 제어한다. 번다운 차트 생성, 예측 기반 SLO 위반 사전 경고, Error Budget 정책(freeze/slow-down/normal) 자동 적용을 수행하며 continuous-eval의 품질 게이트를 보완한다.