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 DevelopersGlobal/ai-agent-skills --skill production-deploymentgit clone --depth 1 https://github.com/DevelopersGlobal/ai-agent-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/developersglobal/ai-agent-skills/production-deployment)<a href="https://agentmods.dev/skills/developersglobal/ai-agent-skills/production-deployment"><img src="https://agentmods.dev/badge/skills/developersglobal/ai-agent-skills/production-deployment.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.01081 |
| Opus 5 | $0.00016 | $0.00541 |
| Sonnet 5 | $0.00006 | $0.00216 |
| Haiku 4.5 | $0.00003 | $0.00108 |
Grade A, and why
production-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 8d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Production is not a test environment. Every deployment is a live operation with real consequences — user impact, data integrity risks, and potential outages. This skill encodes the discipline senior engineers apply before, during, and after every production deployment.
The core rule: never deploy without a rollback plan you've verified can execute in under 5 minutes.
When to Use
- Before any deployment to a production or production-equivalent environment
- When reviewing deployment scripts or CI/CD pipelines
- When adding new services or infrastructure changes
Process
Step 1: Pre-Deployment Checklist
- All tests pass — CI is green on the exact commit being deployed. Not "mostly green."
- Migrations are backward-compatible — The old code must work with the new schema (for zero-downtime). New columns are nullable; columns aren't dropped until after full rollout.
- Feature flags configured — New features are behind flags, off by default.
- Rollback plan written — Document exactly how to rollback: which commands, which configs, estimated time.
- Deployment window confirmed — Low-traffic period? On-call engineer available?
- Stakeholders notified — Anyone affected by downtime or behavior change knows.
Verify: All 6 checklist items confirmed. Do not proceed if any is blocked.
Step 2: Staged Rollout
- Never deploy to 100% of traffic immediately. Use a staged rollout:
- Canary: 1–5% of traffic
- Staged: 10% → 25% → 50% → 100%
- Monitor key metrics at each stage for at least 15 minutes before expanding:
- Error rate (baseline vs. current)
- Latency p50, p95, p99
- Business metrics (conversion, orders, etc.)
- Define your abort threshold before starting: "If error rate exceeds X% or latency p99 exceeds Y ms, rollback immediately."
Verify: Rollout stages and abort thresholds are documented before deployment begins.
Step 3: Deploy
- Execute the deployment using your CI/CD pipeline (not manual commands).
- Monitor dashboards in real-time during the rollout.
- Keep communication channel open with on-call engineer.
- Do not perform any other changes during a deployment (no "quick fixes").
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.
- 8d ago First seen · 109 lines · 32 tokens per session scan A 20e374a84289
production-deployment is a skill published in the GitHub repository DevelopersGlobal/ai-agent-skills (65 stars, last pushed 4mo ago), licensed MIT. It adds 32 tokens to every session and 1,081 once invoked, about $0.0002 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.
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