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/roboco-io/plugins/aws-wa-performancenpx skills add roboco-io/plugins --skill aws-wa-performancegit clone --depth 1 https://github.com/roboco-io/pluginsWhat 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 | $0.00031 | $0.01271 |
| Opus 5 | $0.00015 | $0.00635 |
| Sonnet 5 | $0.00006 | $0.00254 |
| Haiku 4.5 | $0.00003 | $0.00127 |
Grade A, and why
aws-wa-performance 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 2d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Efficiency Pillar
리소스 효율, 적정 규모, 성능 모니터링 관점에서 IaC 코드를 검토합니다.
핵심 원칙
- 고급 기술의 대중화: 관리형 서비스 활용
- 몇 분 만에 전 세계 배포: CloudFront, Global Accelerator
- 서버리스 아키텍처 사용: Lambda, Fargate
- 더 자주 실험: A/B 테스트
- 기계적 공감: 워크로드에 맞는 리소스 선택
검토 항목
PERF-01: 인스턴스 세대
| 심각도 | 탐지 유형 |
|---|---|
| Medium | 자동 탐지 |
검토 내용: 구세대 인스턴스 사용 여부 확인
구세대 인스턴스 목록:
- t2, m4, m3, c4, c3, r4, r3, i2, d2
Terraform 패턴:
# 취약 - 구세대 인스턴스
resource "aws_instance" "bad" {
instance_type = "t2.micro"
}
# 안전 - 현세대 인스턴스
resource "aws_instance" "good" {
instance_type = "t3.micro"
}
PERF-02: Graviton 인스턴스
| 심각도 | 탐지 유형 |
|---|---|
| Low | 자동 탐지 |
검토 내용: ARM 기반 Graviton 인스턴스 사용 권장
Graviton 인스턴스:
- t4g, m6g, m7g, c6g, c7g, r6g, r7g
Terraform 패턴:
# 권장 - Graviton 인스턴스 (비용 효율적, 에너지 효율적)
resource "aws_instance" "good" {
instance_type = "t4g.micro"
}
PERF-03: 캐싱 레이어
| 심각도 | 탐지 유형 |
|---|---|
| Medium | 자동 탐지 |
검토 내용: ElastiCache, CloudFront 등 캐싱 레이어 존재 여부 확인
Terraform 패턴:
# 권장 - ElastiCache 사용
resource "aws_elasticache_cluster" "redis" {
cluster_id = "my-cache"
engine = "redis"
node_type = "cache.t3.micro"
num_cache_nodes = 1
}
# 권장 - CloudFront 사용
resource "aws_cloudfront_distribution" "cdn" {
enabled = true
# ...
}
PERF-04: RDS 성능 설정
| 심각도 | 탐지 유형 |
|---|---|
| Medium | 자동 탐지 |
검토 내용: RDS 인스턴스 크기, IOPS 설정 확인
Terraform 패턴:
# 고성능 요구 시 - Provisioned IOPS
resource "aws_db_instance" "high_perf" {
instance_class = "db.r5.large"
storage_type = "io1"
iops = 3000
allocated_storage = 100
}
PERF-05: Lambda 설정
| 심각도 | 탐지 유형 |
|---|---|
| Medium | 자동 탐지 |
검토 내용: Lambda 메모리, 타임아웃 적정성 확인
Terraform 패턴:
# 검토 필요 - 과도한 타임아웃
resource "aws_lambda_function" "review" {
memory_size = 128
timeout = 900 # 15분 - 정말 필요한가?
}
# 권장 - 적절한 설정
resource "aws_lambda_function" "good" {
memory_size = 256
timeout = 30
}
What ships with it
4 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.
- 2d ago First seen · 179 lines · 31 tokens per session scan A 207db684a489
aws-wa-performance is a skill published in the GitHub repository roboco-io/plugins (21 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 1,271 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.
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