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 alexei-led/cc-thingz --skill deploying-infragit clone --depth 1 https://github.com/alexei-led/cc-thingzWrote 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/alexei-led/cc-thingz/deploying-infra)<a href="https://agentmods.dev/skills/alexei-led/cc-thingz/deploying-infra"><img src="https://agentmods.dev/badge/skills/alexei-led/cc-thingz/deploying-infra/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/alexei-led/cc-thingz/deploying-infra"><img src="https://agentmods.dev/badge/skills/alexei-led/cc-thingz/deploying-infra.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00107 | $0.01286 |
| Opus 5 | $0.00053 | $0.00643 |
| Sonnet 5 | $0.00021 | $0.00257 |
| Haiku 4.5 | $0.00011 | $0.00129 |
Grade A, and why
deploying-infra 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 today.
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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploy Infrastructure
Validate first. Apply only after explicit confirmation. Never invent deploy paths, release names, workspaces, namespaces, accounts, or environments.
Scope
Use for dry-run validation, Terraform/Helm/Kustomize/Kubernetes apply after confirmation, and rollout verification after apply.
Do not use for live troubleshooting, rollback investigation, cloud inspection,
authoring infra, pushing Docker images, triggering GitHub Actions workflows, or
applying without plan/diff evidence. Use operating-infra for inspection and
troubleshooting.
Dockerfiles and GitHub Actions are validate-only in this skill.
Usage
/deploying-infra --dry-run [environment] [scope] validates only. /deploying-infra --apply <environment> [scope] validates, asks, applies, then verifies. /deploying-infra --background --dry-run [environment] [scope] starts background validation.
Rules:
- Default mode is
--dry-run. --backgroundis valid only with--dry-run.--applyrequires authorization covering the reviewed artifact and exact destination. Reuse existing explicit authorization when those inputs are unchanged.- Production confirmation must include the exact environment name.
- If environment, context, namespace, workspace, chart, release, path, or account is unclear, ask one question.
Workflow
- Parse mode, environment, and optional scope.
- Detect infra with
Glob,Grep, andReadbefore shell commands. - Classify detected types: apply-capable Terraform/Helm/Kustomize/Kubernetes; validate-only Dockerfile/GitHub Actions.
- Check required CLIs with safe version or discovery commands.
- Read
references/validation-checklists.mdand use only detected sections. - Run validation and inspect source-backed policy checks.
- For apply-capable types, show plan/diff evidence.
- Stop on
--dry-run; on--apply, confirm any unapproved artifact/destination and apply. - Verify changed resources after apply.
Use a background or delegated validation agent only for large scans or explicit
--background. The agent validates only; it must not apply changes.
What ships with it
2 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.
- today Changed · +8 lines e262cc465c6d
- 9d ago First seen · 143 lines · 107 tokens per session scan A c16fb064b45f
deploying-infra is a skill published in the GitHub repository alexei-led/cc-thingz (35 stars, last pushed today), licensed MIT. It adds 107 tokens to every session and 1,286 once invoked, about $0.0005 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 2026-08-30.
Other skills, from other repositories
infra-review
Review infrastructure-as-code and rendered deployment configuration without modifying it. Use before plan, apply, or deploy when targeting, replacement, state, availability, cost, ordering, or recovery blast radius may change.
gentle-ai-collab-perfect
Trigger: contributing to Gentleman-Programming/gentle-ai as an external collaborator. Strict issue-first workflow, honest PR bodies, contributor-vs-maintainer scope, chained-PR strategy, verification protocol, docstring coverage. Load whenever the active repo is Gentleman-Programming/gentle-ai and any part of the…
issue-creation
Trigger: issue creation, bug reports, feature requests, or issue approval. Create and triage GitHub issues from repository evidence.
sdd-tasks
Break an SDD change into implementation tasks. Trigger: orchestrator launches task planning for a change.
work-unit-commits
Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.
cognitive-doc-design
Design docs that reduce cognitive load. Trigger: writing guides, READMEs, RFCs, onboarding, architecture, or review-facing docs.