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 Pixel-Process-UG/superkit-agents --skill deploymentgit clone --depth 1 https://github.com/Pixel-Process-UG/superkit-agentsWrote 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/pixel-process-ug/superkit-agents/deployment)<a href="https://agentmods.dev/skills/pixel-process-ug/superkit-agents/deployment"><img src="https://agentmods.dev/badge/skills/pixel-process-ug/superkit-agents/deployment/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/pixel-process-ug/superkit-agents/deployment"><img src="https://agentmods.dev/badge/skills/pixel-process-ug/superkit-agents/deployment.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 | $0.00054 | $0.02153 |
| Opus 5.5 | $0.00022 | $0.00861 |
| Sonnet 5.5 | $0.00011 | $0.00431 |
| Haiku 4.5 | $0.00005 | $0.00215 |
Grade A, and why
deployment 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 1mo 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deployment
Overview
Set up CI/CD pipelines and deployment configurations that automate the path from code to production. This skill detects the deployment target, generates pipeline config, creates pre/post-deploy checklists, and configures monitoring — producing a fully automated, rollback-ready deployment pipeline.
Announce at start: "I am using the deployment skill to set up the deployment pipeline."
Phase 1: Detect Deployment Target
STOP after this phase — present findings to user for confirmation before proceeding.
Ask questions to identify the full deployment context:
Platform Detection:
- Where does this deploy? (Vercel, AWS, GCP, Azure, DigitalOcean, self-hosted)
- Container-based? (Docker, Kubernetes)
- Serverless? (Lambda, Cloud Functions, Edge Functions)
CI/CD Detection:
- What CI system? (GitHub Actions, GitLab CI, CircleCI, Jenkins)
- What triggers deployments? (push to main, tags, manual)
- Multi-environment? (dev, staging, production)
Infrastructure Detection:
- Database migrations needed?
- Environment variables management? (secrets manager, .env)
- CDN/caching? Asset pipeline?
- Monitoring/alerting? (Datadog, Sentry, New Relic)
Platform Selection Decision Table
| Project Type | Recommended Platform | CI/CD | Why |
|---|---|---|---|
| Static site / SPA | Vercel, Netlify, Cloudflare Pages | Built-in | Zero config, edge CDN |
| Node.js API | AWS ECS, Cloud Run, Railway | GitHub Actions | Container support, auto-scaling |
| Monorepo (frontend + backend) | Vercel + AWS / Railway | GitHub Actions | Split concerns, independent scaling |
| Enterprise / compliance-heavy | AWS EKS, GKE | GitLab CI, Jenkins | Full control, audit trails |
| Hobby / side project | Railway, Fly.io, Render | Built-in or GitHub Actions | Simple, low cost |
| ML / data pipelines | AWS SageMaker, GCP Vertex | GitHub Actions + Airflow | GPU support, pipeline orchestration |
Phase 2: Design Pipeline
STOP after this phase — present pipeline design to user for approval before generating config.
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.
- 1mo ago First seen · 264 lines · 54 tokens per session scan A a84751de4c3e
deployment is a skill published in the GitHub repository Pixel-Process-UG/superkit-agents (3 stars, last pushed 7mo ago), licensed MIT. It adds 54 tokens to every session and 2,153 once invoked, about $0.0002 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.
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