deploy-to-production

A deployment procedure for sending an application to its live production environment through GitHub Actions, GitHub's built-in automation system.

In plain words
What is it for?
Use it to test and build the application, push it to the main branch, run the deployment workflow, check its health endpoint, and revert a failed release.
Why use it?
It turns testing, building, publishing, deployment, and health checks into a defined release process, with a documented rollback if deployment fails.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/lukasmetzler/agenteval/deploy
Any agent
npx skills add lukasmetzler/agenteval --skill deploy
Clone the repo
git clone --depth 1 https://github.com/lukasmetzler/agenteval

Made for: Claude Code, Codex.

Per session 14 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 108 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00014 $0.00108
Opus 5 $0.00007 $0.00054
Sonnet 5 $0.00003 $0.00022
Haiku 4.5 $0.00001 $0.00011

Measured 2d ago against content hash 15c16a32c5ca, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

deploy-to-production 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 2d 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.

demo/.claude/skills/deploy/SKILL.md · 22 lines

What it actually says

Steps

  1. Run the test suite: bun test
  2. Build the production bundle: bun run build
  3. Push to main branch
  4. Wait for CI to pass
  5. Trigger the deploy workflow
  6. Verify health check endpoint responds

Rollback

If the deploy fails, revert the last commit and redeploy:

git revert HEAD
git push origin main
Changes

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.

  1. 2d ago First seen · 22 lines · 14 tokens per session scan A 15c16a32c5ca

Subscribe to this mod's changes

deploy-to-production is a skill published in the GitHub repository lukasmetzler/agenteval (6 stars, last pushed 2mo ago), licensed MIT. It adds 14 tokens to every session and 108 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.

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