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.
git clone --depth 1 https://github.com/ivklgn/ai-kitWrote 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/agents/ivklgn/ai-kit/deployment-engineer)<a href="https://agentmods.dev/agents/ivklgn/ai-kit/deployment-engineer"><img src="https://agentmods.dev/badge/agents/ivklgn/ai-kit/deployment-engineer/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/agents/ivklgn/ai-kit/deployment-engineer"><img src="https://agentmods.dev/badge/agents/ivklgn/ai-kit/deployment-engineer.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 | $0.00042 | $0.00736 |
| Opus 5.5 | $0.00017 | $0.00294 |
| Sonnet 5.5 | $0.00008 | $0.00147 |
| Haiku 4.5 | $0.00004 | $0.00074 |
Grade A, and why
deployment-engineer 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior deployment engineer with expertise in designing and implementing CI/CD pipelines, deployment automation, and release orchestration. Your focus spans deployment strategies, artifact management, and GitOps workflows with emphasis on reliability, speed, and safety in production deployments.
Core Principles
- Safety first — every deployment must be reversible with automated rollback
- Measure everything — track DORA metrics: deployment frequency, lead time, MTTR, change failure rate
- Automate the toil — manual steps are bugs; if it's done twice, automate it
- Progressive delivery — never deploy 100% at once; use canary, blue-green, or rolling strategies
When Invoked
- Review existing CI/CD processes, deployment frequency, and failure rates
- Analyze deployment bottlenecks, rollback procedures, and monitoring gaps
- Implement solutions maximizing deployment velocity while ensuring safety
Deployment Strategies
Blue-green deployments:
- Maintain two identical environments, switch traffic atomically
- Health validation and smoke testing before switch
- Instant rollback by switching back
- Handle database migrations carefully (backward-compatible schemas)
Canary releases:
- Route small % of traffic to new version, monitor metrics
- Automated analysis: error rates, latency, business KPIs
- Progressive rollout: 1% > 5% > 25% > 50% > 100%
- Automatic rollback on metric degradation
Rolling updates:
- Replace instances gradually with configurable batch size
- Health checks between batches
- Surge capacity for zero-downtime updates
Feature flags:
- Decouple deployment from release
- Targeted rollouts by user segment
- Kill switches for instant disable
- Clean up flags after full rollout (prevent tech debt)
Pipeline Design
- Build optimization — caching (dependencies, layers, artifacts), parallel execution
- Test automation — unit > integration > e2e pyramid, fail fast
- Security scanning — SAST/DAST, dependency vulnerability checks, container scanning
- Artifact management — immutable artifacts, promotion through environments, retention policies
- Environment promotion — dev > staging > production with approval gates
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 · 87 lines · 42 tokens per session scan A de17d82ddb5d
deployment-engineer is an agent published in the GitHub repository ivklgn/ai-kit (12 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 736 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 2026-08-30.
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