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 ancoleman/ai-design-components --skill deploying-applicationsgit clone --depth 1 https://github.com/ancoleman/ai-design-componentsWrote 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/ancoleman/ai-design-components/deploying-applications)<a href="https://agentmods.dev/skills/ancoleman/ai-design-components/deploying-applications"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/deploying-applications/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/ancoleman/ai-design-components/deploying-applications"><img src="https://agentmods.dev/badge/skills/ancoleman/ai-design-components/deploying-applications.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 | $0.00075 | $0.03065 |
| Opus 5.5 | $0.00030 | $0.01226 |
| Sonnet 5.5 | $0.00015 | $0.00613 |
| Haiku 4.5 | $0.00007 | $0.00307 |
Grade A, and why
deploying-applications 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 — 440 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploying Applications
Production deployment patterns from Kubernetes to serverless and edge functions. Bridges the gap from application assembly to production infrastructure.
Purpose
This skill provides clear guidance for:
- Selecting the right deployment strategy (Kubernetes, serverless, containers, edge)
- Implementing Infrastructure as Code with Pulumi or OpenTofu
- Setting up GitOps automation with ArgoCD or Flux
- Choosing serverless databases (Neon, Turso, PlanetScale)
- Deploying edge functions (Cloudflare Workers, Deno Deploy)
When to Use This Skill
Use this skill when:
- Deploying applications to production infrastructure
- Setting up CI/CD pipelines and GitOps workflows
- Choosing between Kubernetes, serverless, or edge deployment
- Implementing Infrastructure as Code (Pulumi, OpenTofu, SST)
- Migrating from manual deployment to automated infrastructure
- Integrating with
assembling-componentsfor complete deployment flow
Deployment Strategy Decision Tree
WORKLOAD TYPE?
├── COMPLEX MICROSERVICES (10+ services)
│ └─ Kubernetes + ArgoCD/Flux (GitOps)
│ ├─ Helm 4.0 for packaging
│ ├─ Service mesh: Linkerd (5-10% overhead) or Istio (25-35%)
│ └─ See references/kubernetes-patterns.md
├── VARIABLE TRAFFIC / COST-SENSITIVE
│ └─ Serverless
│ ├─ Database: Neon/Turso (scale-to-zero)
│ ├─ Compute: Vercel, AWS Lambda, Cloud Functions
│ ├─ Edge: Cloudflare Workers (<5ms cold start)
│ └─ See references/serverless-dbs.md and references/edge-functions.md
├── CONSISTENT LOAD / PREDICTABLE TRAFFIC
│ └─ Containers (ECS, Cloud Run, Fly.io)
│ ├─ ECS Fargate: AWS-native, serverless containers
│ ├─ Cloud Run: GCP, scale-to-zero containers
│ └─ Fly.io: Global edge, multi-region
├── GLOBAL LOW-LATENCY (<50ms)
│ └─ Edge Functions + Edge Database
│ ├─ Cloudflare Workers + D1 (SQLite)
│ ├─ Deno Deploy + Turso (libSQL)
│ └─ See references/edge-functions.md
└── RAPID PROTOTYPING / STARTUP MVP
└─ Managed Platform as a Service
├─ Vercel (Next.js, zero-config)
├─ Railway (any framework)
└─ Render (auto-deploy from Git)
IaC CHOICE?
├─ TypeScript-first → Pulumi (Apache 2.0, multi-cloud)
├─ HCL-based → OpenTofu (CNCF, Terraform-compatible)
└─ Serverless TypeScript → SST v3 (built on Pulumi)
What ships with it
18 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.
- examples/k8s-argocd/argocd/application.yaml 820 B
- examples/k8s-argocd/base/deployment.yaml 1008 B
- examples/k8s-argocd/README.md 2.9 KB
- examples/pulumi-aws/index.ts 4.9 KB runs code
- examples/pulumi-aws/package.json 457 B
- examples/pulumi-aws/Pulumi.yaml 351 B
- examples/pulumi-aws/README.md 2.8 KB
- examples/pulumi-aws/tsconfig.json 347 B
- outputs.yaml 12 KB
- references/deployment-strategies.md 24 KB
- references/edge-functions.md 14 KB
- references/gitops-argocd.md 12 KB
- references/kubernetes-patterns.md 11 KB
- references/opentofu-guide.md 29 KB
- references/pulumi-guide.md 18 KB
- references/serverless-dbs.md 14 KB
- scripts/generate_k8s_manifests.py 6.2 KB runs code
- scripts/validate_deployment.py 8.3 KB runs code
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 · 440 lines · 75 tokens per session scan A 7b67605c4aed
deploying-applications is a skill published in the GitHub repository ancoleman/ai-design-components (524 stars, last pushed 10mo ago), licensed MIT. It adds 75 tokens to every session and 3,065 once invoked, about $0.0003 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 2026-08-30.
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Important: Before you begin, fill in the generatedBy property in the meta section of .actor/actor.json. Replace it with the tool and model you're currently using, such as "Claude Code with Claude Sonnet 4.5". This helps Apify monitor and improve AGENTS.md for specific AI tools and models.
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