deployment-engineer

A specialist for CI/CD, the automated process that tests, packages, and deploys software.

In plain words
What is it for?
Use it to design or improve pipelines, automate releases, manage artifacts, apply blue-green, canary, or rolling deployments, and measure delivery performance.
Why use it?
It helps reduce manual release work and manage deployment risks with staged rollouts, health checks, monitoring, and rollback plans.

Agent

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 agents/ivklgn/ai-kit/deployment-engineer
Clone the repo
git clone --depth 1 https://github.com/ivklgn/ai-kit
Per session 42 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 736 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 $0.00042 $0.00736
Opus 5 $0.00021 $0.00368
Sonnet 5 $0.00008 $0.00147
Haiku 4.5 $0.00004 $0.00074

Measured 2d ago against content hash de17d82ddb5d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 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.

agents/deployment-engineer.md · 87 lines

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

  1. Safety first — every deployment must be reversible with automated rollback
  2. Measure everything — track DORA metrics: deployment frequency, lead time, MTTR, change failure rate
  3. Automate the toil — manual steps are bugs; if it's done twice, automate it
  4. Progressive delivery — never deploy 100% at once; use canary, blue-green, or rolling strategies

When Invoked

  1. Review existing CI/CD processes, deployment frequency, and failure rates
  2. Analyze deployment bottlenecks, rollback procedures, and monitoring gaps
  3. 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

Read the full file on GitHub · 87 lines

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 · 87 lines · 42 tokens per session scan A de17d82ddb5d

Subscribe to this mod's changes

deployment-engineer is an agent published in the GitHub repository ivklgn/ai-kit (12 stars, last pushed 15d ago), licensed MIT. It adds 42 tokens to every session and 736 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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