Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add AlexK020908/infra-designer/plugin install infra-designerWrote 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/commands/alexk020908/infra-designer/deploy)<a href="https://agentmods.dev/commands/alexk020908/infra-designer/deploy"><img src="https://agentmods.dev/badge/commands/alexk020908/infra-designer/deploy.svg" alt="Measured on agentmods" 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 | $0.00640 |
| Opus 5 | $0.00009 | $0.00320 |
| Sonnet 5 | $0.00003 | $0.00128 |
| Haiku 4.5 | $0.00002 | $0.00064 |
Grade A, and why
deploy 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 5d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploy infrastructure
Compile the spec to Terraform and apply it. Keep the fail → read-error → fix loop in the terminal — that's where you're strongest.
Spec path: $ARGUMENTS. If omitted, look under infra/ in the current working directory
for a <project-name>/spec.json; if there are several projects, ask the user which one.
All paths are relative to the user's cwd, never ${CLAUDE_PLUGIN_ROOT}. Below,
<dir> is the folder the spec lives in (e.g. infra/todo-app) — write all generated
output back into that same folder.
1. Compile the spec
Pick a backend. Default is Terraform; offer awscli if the user prefers not to use
IaC.
# Terraform (default — full feature set, all wiring)
python ${CLAUDE_PLUGIN_ROOT}/engine/cli.py render <dir>/spec.json <dir>/terraform
# or AWS CLI (idempotent shell scripts, no IaC)
python ${CLAUDE_PLUGIN_ROOT}/engine/cli.py render <dir>/spec.json <dir>/deploy --backend=awscli
Terraform writes providers.tf + main.tf (+ network.tf); awscli writes
deploy.sh + destroy.sh. Both write <dir>/spec.lock.json pinning component
versions — commit the lock so the handoff is reproducible. Read the output and
confirm it matches the spec before applying.
2. Preview
- Terraform:
cd <dir>/terraform && terraform init && terraform plan -out tfplan. Ifterraformisn't installed, tell the user how to install it and stop. - AWS CLI: read
<dir>/deploy/deploy.shaloud — it's the exact set of commands that will run. Requires theawsCLI configured with credentials.
Show the user what will be created and get explicit confirmation before applying — this creates real, billable cloud resources.
3. Apply and iterate
- Terraform:
terraform apply tfplan - AWS CLI:
bash <dir>/deploy/deploy.sh(idempotent — safe to re-run;destroy.shtears down)
If apply fails:
- Read the actual Terraform error.
- The fix usually belongs in
<dir>/spec.json(a config value, a missing edge), not in the generated Terraform — regenerate with step 1 after editing the spec, so the spec stays the source of truth. Only hand-editmain.tffor a genuine engine gap, and report that gap. - Re-plan and re-apply.
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.
- 5d ago First seen · 63 lines · 17 tokens per session scan A d45b976dbe0d
deploy is a command published in the GitHub repository AlexK020908/infra-designer (2 stars, last pushed 2mo ago), licensed MIT. It adds 17 tokens to every session and 640 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
root-cause
Use when any test fails, bug appears, or behaviour surprises you, before proposing a fix - find the cause and prove it, by reading real evidence, tracing bad values back to their origin, comparing against a working case, and testing one hypothesis at a time.
k8s-manifest
Generate production-ready Kubernetes manifests for the current application.
configure-blob
Create and configure an Azure Blob Storage account with security best practices.
setup-lambda
Configure and deploy an AWS Lambda function with proper settings.
workflow-create
Command "workflow-create" from smith-horn/skillsmith, covering workflow-create, usage, options, examples and author deploy-api.yaml, then register it as a template.
init
Generate FILETREE.md from scratch. Confirms overwrite if it already exists. --self-only keeps every artifact out of git.