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 skills add fmind/dot --skill cloud-rungit clone --depth 1 https://github.com/fmind/dotWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/fmind/dot/cloud-run)<a href="https://agentmods.dev/skills/fmind/dot/cloud-run"><img src="https://agentmods.dev/badge/skills/fmind/dot/cloud-run/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/fmind/dot/cloud-run"><img src="https://agentmods.dev/badge/skills/fmind/dot/cloud-run.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00017 | $0.01078 |
| Opus 5.5 | $0.00007 | $0.00431 |
| Sonnet 5.5 | $0.00003 | $0.00216 |
| Haiku 4.5 | $0.00002 | $0.00108 |
Grade A, and why
cloud-run 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 3d 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cloud Run Deployment
Deploy a Python service to Cloud Run through an immutable image digest, private invocation, keyless CI, and a dedicated runtime identity. containerize owns the image; gcloud owns account, project, and region context.
Workflow
- Resolve target and authority: verify the gcloud account, project, region, service, Artifact Registry image repository, runtime permissions, and approved mutation scope. Registry pushes, signing, IAM changes, infrastructure apply, deployment, and traffic changes each require authority for the named target.
- Configure identities once: read bootstrap.md for APIs, registry, runtime service account, deployer service account, and Workload Identity Federation. Keep deployer and runtime identities distinct.
- Validate locally: pin, lock, and install Trivy and Cosign before any image scan or registry push, then build the pinned non-root Python image and run its tests and
check:imagescan; follow deployment.md steps 1-2 and containerize. - Publish and prove provenance: after push authority is explicit, follow deployment.md: take one digest (the build action's output in CI, BuildKit metadata locally), then scan, SBOM, sign, verify the expected identity and issuer, and attest it before deployment.
- Deploy privately: pass the digest reference and dedicated
--service-account; keep--invoker-iam-check --no-allow-unauthenticatedand verify both access controls after deployment. Use service.yaml when settings warrant a declarative service specification; a successful update does not prove private IAM. - Use infrastructure as code when needed: manage repeatable services, IAM, registries, and fleet-level infrastructure per infra-as-code; review the plan before apply.
- Wire CD when requested: adopt deploy.yml and verify-private.py per the "Wire CD" step of deployment.md.
- Verify the live result: record the ready revision, deployed digest, pinned secret versions, runtime account, IAM policy, health result, and traffic split. Keep a known-good revision for rollback.
What ships with it
5 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.
- 3d ago Changed be164ab2c44f
- 7d ago Changed 278cd30cb89f
- 12d ago Changed · -1 lines 3960b5c1cc2a
- 14d ago Changed 37e8d8e24911
- 15d ago Changed · -2 lines · -3 tokens per session c0b52c12e527
- 17d ago Changed e1df2b804a43
- 23d ago Changed 91a5b23f7bd0
- 26d ago Changed 807e970d16dc
- 29d ago Changed cfa3dcae582d
- 1mo ago First seen · 48 lines · 20 tokens per session scan A 2c85e57ad7ba
cloud-run is a skill published in the GitHub repository fmind/dot (12 stars, last pushed today), licensed MIT. It adds 17 tokens to every session and 1,078 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
cloud-run
Deploy container services to Google Cloud Run with Artifact Registry, keyless CI identity, Secret Manager, ko, or Dockerfiles.
google-cloud-operations
Operate Google Cloud with gcloud: pin account, configuration, project, and billing context for IAM, APIs, logs, and audits outside deployment.
terraform-stack
Canonical infrastructure-as-code stack — OpenTofu-first with tflint, trivy config scans, terraform-docs, native tests, and GCS state. Use for any Terraform or OpenTofu work.
dotfiles-bootstrap
Bootstrap a workstation with the dotfiles framework. Takes a GitHub user / owner+repo / explicit clone URL and runs dot init (which shells out to chezmoi) with the right safety prompts. Honors the active agent profile (ask / plan / apply / audit) so it defaults to dry-run in safer modes and full apply in apply.
vibe
Delegate a coding task to a cheap AI model (Mistral Vibe by default, but any provider Vibe knows about — DeepSeek, Gemini Flash, etc.) and supervise the result via git diff. Claude orchestrates, the cheap model codes. Claude consumes 500-1500 tokens per delegation regardless of how many file reads the delegate does…
scaffold-terraform
Scaffold a Terraform infrastructure project with CI/CD (plan on PR, apply on push), AWS OIDC auth, .envrc, and standard files. Uses terraform CLI as the native tool. Loads on top of scaffold-project (run that first for cross-language standard files). Use when creating infrastructure repos, or when the user mentions…