adk-expert
Google ADK (Agent Development Kit) orchestration patterns — boundaries, agent composition, and tool seams. Trigger when designing or reviewing multi-agent…
Check proposed stack, data flows, cloud choices, and delivery controls against declared enterprise policies, compliance frameworks, and security controls. Trigger at R2/R3 risk classification, before locking architecture decisions, or when the operating profile names a
$ npx -y skills add jpantsjoha/ai-native-developer-experience --skill governance-guardrail --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/governance-guardrailContext preview
The summary Claude sees to decide when to auto-load this skill.
Check proposed stack, data flows, cloud choices, and delivery controls against declared enterprise policies, compliance frameworks, and security controls. Trigger at R2/R3 risk classification, before locking architecture decisions, or when the operating profile names a
name: governance-guardrail description: Check proposed stack, data flows, cloud choices, and delivery controls against declared enterprise policies, compliance frameworks, and security controls. Trigger at R2/R3 risk classification, before locking architecture decisions, or when the operating profile names a governance pointer.
> **A policy position the team cannot cite is a policy the team will unknowingly violate.**
This skill checks alignment between what the team is building and the enterprise policies, compliance frameworks, and security controls that govern it. It never invents a policy position — it surfaces gaps as explicitly owned unknowns.
security baseline, approved-technology list)
approved-vendor constraints
Governance alignment is not an audit that happens after delivery. It is a constraint that shapes architecture from day one. Discovering a compliance gap after a decision is locked is expensive; discovering it during bootstrap or spec is cheap.
This skill operates at the policy layer, above the cloud-expert skills:
technical guardrails: IAM, data residency, cost. Use them to implement correctly within a chosen platform.
data model are permitted by enterprise policy in the first place.
Route cloud-specific implementation questions to the cloud-expert skills after this skill confirms the architecture is policy-compliant. Feed open findings into `adversarial-gate` before high-stakes decisions are locked.
The project operating profile (`docs/operating-model/PROJECT-OPERATING-PROFILE.md`) should name one of:
If no pointer exists, record it as an explicit unknown with an owner and resolving trigger. Do not proceed to stack or data-flow checks until the pointer is named — checking against an unknown policy is not a check.
For each component of the technical stack, confirm:
Flag any component that has no confirmed policy position.
For each data flow that crosses a boundary (service, team, region, or tenant):
Confirm the following controls are in place or explicitly deferred with a named owner:
Every gap becomes an explicit unknown in the operating profile:
Pass open R2/R3-blocking findings to `adversarial-gate` before those decisions are locked.
operating profile. Record where they live and confirm they are accessible to the team.
is flagged, not assumed acceptable.
approval.
permission.
| Excuse | Counter | |---|---| | "We'll check compliance before launch" | A compliance gap found after architecture is locked costs 10× to fix. Check at spec time. | | "We're using standard tools, they must be approved" | Standard in the industry ≠ approved in this enterprise. Confirm the pointer. | | "Security is the security team's job" | Security is the team's constraint. The security team approves; the team is responsible for alignment. | | "There's no policy document, so there's no policy" | Absence of a cited policy is the gap. Surface it with an owner and trigger. | | "We already did this for the last project" | Policy changes. Stack changes. Check against the current pointer for this project. |
A team-project AI harness bootstrap that gives humans and agents a shared operating contract from day one, moving AI leverage from an individual “IC superhero” advantage to a repeatable team capability on an equal playing field.
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