Google ADK (Agent Development Kit) orchestration patterns — boundaries, agent composition, and tool seams. Trigger when designing or reviewing multi-agent systems built on ADK. Authoritative source: adk.dev.
日本語の概要は準備中です。原文の説明を表示しています。
Read-only SRE checkup of any GCP project: deterministic probes of the edge, Cloud Run services, 7-day error logs, Cloud Scheduler, alert policies and uptime checks, Secret Manager and IAM, the data stores and the machine's own scheduled jobs, audited into one fixed status table (LIVE / WARNING / RED / INCONCLUSIVE) with evidence, findings by severity, what could not be checked, and a single OVERALL line delivered as one notification. Parametrised by a per-project manifest, so the same routine runs on every project. Use when the operator says "cloud checkup", "SRE check", "is everything live", "what's healthy / warning / red", "any errors this week", "audit the infra", "weekly checkup", "set up the weekly checkup", before a deploy or demo, or after an incident. Cloud Run first; App Engine and GKE differ only in the serving probes.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
One routine, two entry points: in-session (you are the inspection layer) and headless (scheduled or on demand). Both fill the same template, so two runs are comparable.
Read-only throughout. It never deploys, never mutates cloud state, never restarts or unpauses anything.
| Layer | What it does | Survives without the next layer |
|---|---|---|
| 1. Deterministic probes | scripts/cloud_checkup.sh: curl, gcloud ... describe, gcloud logging read, scheduler, monitoring, secrets, IAM, local jobs. Writes a probes file. | Yes. The probes file is the artefact of record. |
| 2. Read-only inspection | Headless claude -p (or you, in session) reads the probes, queries the data stores, fills templates/CHECKUP-TEMPLATE.md into the report. | Yes. Without it the report is the probes file plus OVERALL: INCONCLUSIVE. |
| 3. One notification line | scripts/notify.sh posts the OVERALL verdict and the report path. | Yes. Suppressed with CC_NO_NOTIFY=1. |
Layer 1 exists so the routine still has value when the model is unavailable, rate-limited or wrong. Never let layer 2 be the only thing that ran.
| Status | Rule |
|---|---|
| 🟢 LIVE | The check passes and that feature had zero errors in the review window. |
| 🟡 WARNING | It serves, but had errors in the window, or is degraded, paused, stale beyond its threshold, or drifted from the source of truth. |
| 🔴 RED | It errors right now, is down, or is halted. |
| ⚪ INCONCLUSIVE | It could not be read from this seat. Say what would be needed. |
PERMISSION_DENIED reads as INCONCLUSIVE, never as zero. A denied logging read
returns no rows; scoring that as "no errors" turns a blind seat into a green light. Every
probe captures stderr and marks the row INCONCLUSIVE on any permission or API error.
INCONCLUSIVE is a capability gap of the seat, not a health signal.
The machine's default gcloud account drifts between identities, and a stored credential
can mint a token for a different principal than the one you asked for.
--account on every call. The script wraps gcloud in a gc() helper that
always passes it. Never gcloud config set account: it mutates state the operator's
other work depends on, and it survives the run.email back from
https://oauth2.googleapis.com/tokeninfo. If that email is not the pinned account, the
whole cloud plane is INCONCLUSIVE regardless of what the probes returned, because the
reads were made as somebody else. The token goes over stdin, never into argv, and never
into the probes file.Everything project-specific lives in one YAML file. Copy manifest.example.yaml to the
project (.cloud-checkup.yaml at the repo root is the convention) and fill it in: project,
region, account, services, public URL and edge routes, expected security headers, log
review window, data stores, local jobs, ticket map, extra rows, notification sink,
schedule. The probe script reads it with yq when present, PyYAML next, and a small
built-in parser last; the inspection layer reads the file itself, so richer structures
(data stores, ticket map, extra rows) need no parser support.
CC=~/.claude/skills/cloud-checkup
# In session: probes only, then you fill the template as layer 2.
CC_SKIP_AI=1 CC_NO_NOTIFY=1 CC_MANIFEST=.cloud-checkup.yaml "$CC/scripts/cloud_checkup.sh"
# Headless, full run (probes + inspection + notification).
CC_MANIFEST=.cloud-checkup.yaml "$CC/scripts/cloud_checkup.sh"
In-session, after the probes land: read the probes file, query each data store in
data_stores[] (see modules/ for the per-type routine), fill every row of
templates/CHECKUP-TEMPLATE.md, compare against the previous report in the report
directory for "Changed since", map findings to ticket_map, and end the file with exactly
one OVERALL: line. Point CC_OUT at a scratch path when running probes-only, so the
stub never lands on the canonical report name. Lead the chat reply with RED rows, then
WARNING, then the OVERALL line and the report path. Do not commit; the operator does.
Weekly is the right cadence: often enough to catch a dead writer before its data expires, rare enough that the report is read.
templates/launchd.plist.tmpl, install to ~/Library/LaunchAgents/,
launchctl load it. StartCalendarInterval uses local time, so the hour tracks the
operator's clock through DST with no UTC conversion.templates/cron.example, or a systemd timer of the same shape.Logs never go in /tmp. macOS purges /tmp after three days of no access, and it does
it while a long-lived process still holds the descriptors, so the failure that matters is
exactly the one whose log is gone. Both templates put stdout, stderr and the probes files
under ${CC_STATE_DIR:-$HOME/.local/state/cloud-checkup}.
Layer 2's headless tool grant is a positive allowlist of exact read-only subcommands,
not a denylist of dangerous ones. A denylist is a guess about what a future gcloud release
will name its mutating verbs; an allowlist fails closed when that guess is wrong. The list
lives in scripts/cloud_checkup.sh (AI_ALLOW) and holds only read forms: gcloud
describe / list / read / get-iam-policy, header-only and status-only curl, git log
and git status, local file reads, and the MongoDB MCP read tools by name (find,
aggregate, count, explain, list / schema / index / stats) — never a wildcard, which would
admit the server's insert, update, delete and drop tools. Launch the MongoDB server with
--readOnly as well. A short denylist sits behind it as defence in depth, not as the mechanism.
Write is the one grant that can change the working tree, and it is bounded by the prompt
(one report path) rather than by the grant. That is the routine's one residual: run the
checkup on a clean tree, and read the report's diff before committing it.
Not at any layer, not "just to confirm", not because a fix looks obvious:
gcloud run deploy, no gcloud builds submit, no image promotion.services update, no IAM binding, no secret version, no
alert policy or uptime check creation, no terraform apply.launchctl kickstart, no systemctl restart, no scheduler resume or
run, no unpausing anything.gcloud config set, no gcloud auth login, no credential rotation.git commit, git push, git checkout or branch change.Remediation is the operator's, or a separate agent's, under its own approval. The checkup's output is the ticket, not the fix.
The checkup is the thing that tells the operator whether to trust the estate, so a silent
regression in it is worse than a red row. Changes to the script, the template or the prompt
carry an independent review bound to the exact diff, in the repo's own review-trailer form,
for example Reviewer: PASS 2026-09-04 sha=<digest>. The writer never approves its own
change. Whoever changed a status rule states which rows can flip because of it.
Layers 2 and 3, the legend, the identity discipline and the template are platform-neutral. Only the serving probes in layer 1 change:
| Platform | Serving probe | Log filter | Currency row |
|---|---|---|---|
| Cloud Run (default) | run services describe, latest ready revision and traffic percent | resource.type="cloud_run_revision" | deployed image digest against the branch |
| App Engine | app services list, app versions list --hide-no-traffic | resource.type="gae_app" | serving version id and its traffic split |
| GKE | container clusters describe, then a read-only kubectl get deploy,pods | resource.type="k8s_container" | image tag on the deployment against the branch |
App Engine has no per-revision traffic pin, so the "latest revision at 100%" row becomes "the serving version is the one that was deployed". GKE adds rows the others do not need: node pool health, pod restart counts, and pending or evicted pods.
| Path | What |
|---|---|
manifest.example.yaml | Per-project inputs, commented. |
scripts/cloud_checkup.sh | Layer 1 probes, layer 2 dispatch, fallback report, layer 3 call. |
scripts/notify.sh | Pluggable notification sink. Slack incoming webhook by default. |
templates/CHECKUP-TEMPLATE.md | The fixed report. Generic rows plus {{EXTRA_ROWS}}. |
templates/checkup_prompt.md | The layer 2 inspection prompt, manifest-parametrised. |
templates/launchd.plist.tmpl, templates/cron.example | Schedule scaffolds. |
modules/mongodb_atlas.md | Data-store module: what to assess, and the assess/plan/apply/rollback retention pattern. |
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Google ADK (Agent Development Kit) orchestration patterns — boundaries, agent composition, and tool seams. Trigger when designing or reviewing multi-agent systems built on ADK. Authoritative source: adk.dev.
日本語の概要は準備中です。原文の説明を表示しています。
JP's signature red-team pass — "how would I break this?" Argue against your own approach before proceeding. Trigger on any high-stakes decision, architecture choice, or before marking work complete.
日本語の概要は準備中です。原文の説明を表示しています。
Cloud guardrails for any vendor workload — Google Cloud (GCP, Vertex AI, GKE), AWS (IAM, EKS, Bedrock), Azure (Entra ID, Policy, AKS), Alibaba Cloud (RAM, mainland/international residency). Enforces identity least-privilege, mechanical policy, data boundaries, residency, cost caps, network egress and observability, with official-source validation before any claim. Trigger on any cloud infrastructure design, review, Terraform plan, or LLM/agent deployment; the-architect routes here.
日本語の概要は準備中です。原文の説明を表示しています。
LLM and cloud cost awareness — model tiering, token budgets, right-sizing, and when a cheaper model suffices. Trigger before finalising any architecture that calls LLMs, before scaling a workload, or when a cost estimate is needed.
日本語の概要は準備中です。原文の説明を表示しています。
Decompose an epic into atomic parallelizable tasks, route each to the right skill, and keep the four delivery records straight — issues, STATUS, ROADMAP, CHANGELOG. Use as a meta-router when several skills could apply, and as the baseline for how delivery state is recorded. Trigger at the start of any multi-track epic, when the skill count exceeds ~12, or when the records have drifted from reality.
日本語の概要は準備中です。原文の説明を表示しています。
Validate agent output against declared domain rules and ground truth before trusting it downstream. Trigger after any agent produces output that will be used in a decision, stored persistently, or passed to another agent.
日本語の概要は準備中です。原文の説明を表示しています。