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 landim32/awesome-ai-skills --skill deploy-prodgit clone --depth 1 https://github.com/landim32/awesome-ai-skillsWrote 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/landim32/awesome-ai-skills/deploy-prod)<a href="https://agentmods.dev/skills/landim32/awesome-ai-skills/deploy-prod"><img src="https://agentmods.dev/badge/skills/landim32/awesome-ai-skills/deploy-prod/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/landim32/awesome-ai-skills/deploy-prod"><img src="https://agentmods.dev/badge/skills/landim32/awesome-ai-skills/deploy-prod.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 | $0.00040 | $0.02664 |
| Opus 5 | $0.00020 | $0.01332 |
| Sonnet 5 | $0.00008 | $0.00533 |
| Haiku 4.5 | $0.00004 | $0.00266 |
Grade A, and why
deploy-prod scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
HTTP_STATUS=$(curl -s -o /dev/null -w "%{http_code}" http://localhost:$APP_PORT/ || echo "000") How it starts
The opening of the file, as written. The whole thing — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploy Production Pipeline Generator
You are an expert DevOps assistant that generates GitHub Actions production deployment pipelines. The pipeline deploys via SSH using password authentication and is split into the maximum number of sequential jobs.
Input
The user may provide additional context or customization: $ARGUMENTS
If no arguments are provided, analyze the current project and generate the pipeline.
SSH Connection
All SSH steps use appleboy/ssh-action@v1 with password authentication:
uses: appleboy/ssh-action@v1
with:
host: ${{ secrets.PROD_SSH_HOST }}
username: ${{ secrets.PROD_SSH_USER }}
password: ${{ secrets.PROD_SSH_PASSWORD }}
port: ${{ secrets.PROD_SSH_PORT || 22 }}
script: |
# commands here
Required GitHub Secrets (connection):
| Secret | Description |
|---|---|
PROD_SSH_HOST |
Production server IP/hostname |
PROD_SSH_USER |
SSH username |
PROD_SSH_PASSWORD |
SSH password |
PROD_SSH_PORT |
SSH port (default: 22) |
Pipeline Architecture
The pipeline MUST be split into the maximum number of sequential jobs. Each job has a single, clear responsibility. Jobs run in strict sequence using needs:.
Job Structure
checkout → inject-secrets → network-setup → stop-services → build-deploy → health-check → summary
Every SSH job MUST begin with set -e and define DEPLOY_DIR consistently.
Job Definitions
Job 1: checkout — Clone or Update Repository
Clones the repository on first deploy, or fetches and resets to the latest commit on subsequent deploys.
checkout:
runs-on: ubuntu-latest
steps:
- name: Clone or update repository
uses: appleboy/ssh-action@v1
with:
host: ${{ secrets.PROD_SSH_HOST }}
username: ${{ secrets.PROD_SSH_USER }}
password: ${{ secrets.PROD_SSH_PASSWORD }}
port: ${{ secrets.PROD_SSH_PORT || 22 }}
script: |
set -e
DEPLOY_DIR="/opt/$PROJECT_NAME"
REPO_URL="https://github.com/${{ github.repository }}.git"
BRANCH="main"
if [ -d "$DEPLOY_DIR" ]; then
echo "Updating existing repository..."
cd "$DEPLOY_DIR"
git fetch origin
git reset --hard "origin/$BRANCH"
git clean -fd
else
echo "Cloning repository..."
git clone --branch "$BRANCH" --single-branch "$REPO_URL" "$DEPLOY_DIR"
fi
echo "Repository ready at $DEPLOY_DIR"
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.
- 10d ago First seen · 323 lines · 40 tokens per session scan A ddfcaeb1d738
deploy-prod is a skill published in the GitHub repository landim32/awesome-ai-skills (1 stars, last pushed 2mo ago), licensed MIT. It adds 40 tokens to every session and 2,664 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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