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Aws adversarial audit before new pattern

Skill esaldgut/ai-native-engineering-workspace/global-skills/aws-go/aws-adversarial-audit-before-new-pattern

Before implementing a NEW AWS pattern for the first time in a repo (first Step Functions, first Bedrock Guardrails, first EventBridge Pipes, first SES account, first service X), pause the implementation and run a time-boxed adversarial doc-research pass against current official docs — ~8-10 sharp questions covering quotas, IAM action naming, regional availability, eventual consistency, error/retry semantics, cost at scale, observability gaps, reversibility, and what re:Post reports as currently broken. AWS docs move quarterly and re:Post surfaces issues the main docs don't; ~45 minutes of research saves hours of mid-implementation rework. Triggers ONLY on "first" instances, not on refactors of patterns the repo already uses.From its SKILL.md

Install
npx -y skills add esaldgut/ai-native-engineering-workspace --skill aws-adversarial-audit-before-new-pattern

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Adversarial audit before a new AWS pattern

The first time a repo adopts an AWS pattern, the failure modes are unknown and the docs are scattered. AWS services carry non-obvious surprises — service quotas, regional GA gaps, IAM action-naming inconsistencies, eventual-consistency windows, hard timeouts — documented across the main docs, the Service Authorization Reference, and re:Post threads. This skill is a pre-mortem (Klein, HBR 2007) applied to AWS adoption: before writing code, spend ~45 minutes adversarially interrogating current docs. The asymmetry is steep — discovering a blocker mid-implementation costs 10–100× discovering it up front.

When to invoke

Invoke only on a genuinely first instance in the repo:

  • First Step Functions state machine. First Bedrock Guardrails config. First EventBridge Pipes. First SES account / sending domain. First use of any service X the codebase has never touched.

Do not invoke for: another Lambda that uses an integration the repo already has 50 of; a refactor of a canonical, well-worn pattern. The marginal value collapses once the team has muscle memory.

Announce on invoke: "Using aws-adversarial-audit-before-new-pattern to run a time-boxed adversarial doc pass before standing up this first-of-its-kind AWS pattern."

Discipline (not a brainstorm)

  • Time-box it (~45 min). Without a box, the audit becomes the project.
  • Questions must be specific. "What could go wrong?" yields mush. "List 3 service quotas this hits at 10× P99 traffic" yields action.
  • Verify against current docs, not memory — AWS changes quarterly. A research-capable agent (WebFetch/WebSearch or AWS docs tooling) is effectively required.
  • Produce a written artifact (an ADR). An audit nobody wrote down is forgotten by sprint+2.
  • Define "first" sharply. "First state machine ever" → yes. "Another Lambda invoked by an existing state machine" → no.

The audit checklist (a starting frame, not gospel)

Adapt per pattern; the value is in answering against live docs, not the exact list:

  1. Quotas/limits — what service quotas could this hit at 10× current traffic? (docs.aws.amazon.com/general/latest/gr/<service>.html)
  2. IAM actions — which actions does each call require? Any naming oddities (e.g. s3:ListBucket vs s3:ListAllMyBuckets)? (Service Authorization Reference)
  3. Regional availability — is the feature GA in the deployment Region?
  4. Eventual consistency — any eventually-consistent read on the happy path?
  5. Error/retry semantics — for each call: typed error type, default retry, idempotency guarantee, any hard timeout (e.g. Cognito triggers' 5s synchronous cap).
  6. Cost at scale — marginal cost per 1M invocations / per state transition / per GB.
  7. Observability — which CloudWatch metrics ship by default; gap to the SLO dashboard.
  8. Reversibility — how to roll back; any destructive op on the happy path.
  9. Cross-account/region — what breaks if a second account/region is added later.
  10. Known issues — search re:Post for the last ~90 days: what's actively broken right now.

End with a decision: Proceed / Defer / Choose alternative, recorded in docs/adr/<NNNN>-<slug>.md.

Worked instance

A real audit return on EventBridge Pipes: question 9 ("fan-out?") surfaces that a pipe routes events from a single source to a single target — point-to-point, not many-to-many. If the design assumed fan-out, that's an event bus, not Pipes. Catching that in the 45-minute audit is the entire ROI; discovering it after wiring Pipes is the rework this skill prevents.

Anti-pattern to detect

  • Standing up a first-of-its-kind AWS service straight from training-data memory, no current-doc pass.
  • An unbounded "research spike" with no time-box and no written decision.
  • Running this audit on a routine refactor (over-applying it dilutes the signal).

Decision aid

  • Pattern is genuinely first in the repo? → run the time-boxed audit, write the ADR.
  • Pattern already canonical here? → skip; trust muscle memory.
  • Audit surfaces a blocker? → Defer or pick the alternative before writing code.

Related skills

  • global-skills/aws-go/verify-provider-api-supports-property/SKILL.md — its sibling: this skill finds issues before you start; that one verifies the claims a finished design makes. Run this first, that one at design-review.
  • global-skills/aws-go/cdk-three-source-drift-check/SKILL.md — audit whether the new resource type even supports drift detection as part of question 7 (observability).

Sources


Last verified: 2026-06-03. The process is grounded in the AWS Well-Architected Framework and the pre-mortem method (HBR 2007); the worked instance (EventBridge Pipes is point-to-point, not fan-out) re-confirmed live against the EventBridge Pipes user guide. Re-check after: AWS SDK Go v2 major / CDK CLI major, or by 2026-09-03. Decay risk: low (a process pattern; only the cited example facts can drift). Found a drift? Run /skill-pattern-freshness-audit aws-go.

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