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Fix the release path: profile the build, make it cacheable and reproducible, and ship through progressive delivery you can roll back.
Complete skill package instant downloadverity-release-engineering-build-systems.md
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Questions before you buy? A person answers, usually the same day: hello@kissmyskills.com
// the file itself
The first lines of the file your AI will read, exactly as delivered. Nothing rewritten for the page.
You are Verity, a Release Engineering and Build Systems Engineer who measures p50 and p95 before touching a build, proves hermeticity before buying a cache, and ships SBOMs and signed artifacts. You have been activated to make releases mechanical.
// what's inside
Platform and developer-productivity teams whose CI takes too long and whose releases are a manual event.
// two ways to get it
$7 one payment, yours forever
$9/mo cancel anytime, or $59 a year
Subscribers can still buy single files and keep them after they cancel.
// ask your assistant
Go to kissmyskills.com/pages/for-ai-agents and set up the "Verity - Build & Release Engineer AI Skill" skill (handle: verity-release-engineering-build-systems) for me
Paste it into Claude, ChatGPT, Claude Code, Cursor or Codex. Your AI walks you through connecting KissMySkills (about 3 minutes, 7-day free trial) and loads the skill. What your AI will read →
Drop Verity into Claude and get a release engineer who profiles the build critical path before anyone spends a year migrating to a tool that will not fix it.
Verity owns how code becomes a shippable artifact: build systems and their real tradeoffs across Bazel, Gradle, Maven, npm workspaces, Nx and Turborepo, monorepo versus polyrepo, incremental and remote build caching, hermetic and reproducible builds, build time treated as a first-class metric, dependency management and lockfile hygiene, artifact repositories and promotion between environments, versioning and release trains, semantic versioning and changelog automation, feature flags and trunk-based development, progressive delivery through canary, blue-green and ring deployments, rollback and forward-fix policy, supply-chain integrity with SBOMs and signed artifacts, and flaky tests treated as a release-blocking defect rather than background noise.
What you get
How to install
Download the .skill package → open Claude → paste SKILL.md into your Project Instructions or system prompt → describe your requirement → Verity builds the answer. Includes a full worked example so you see exactly what you get.
After checkout, the download link lands in your inbox. Save the file anywhere on your device.
Claude, ChatGPT, Gemini, Grok, or Copilot - whichever one you already use.
Drop it into the system prompt, Project instructions, or custom instructions field.
Your AI is now configured as a specialist. Ask it anything inside its domain.
No technical knowledge required.
With Unlimited there is nothing to download. Connect your AI once, then just ask: "Load Verity from KissMySkills."
Fix the release path: profile the build, make it cacheable and reproducible, and ship through progressive delivery you can roll back. Load it once into Claude Projects and you get a configured Release Engineering & Build Systems Engineer without re-explaining context at the start of every session. Measure the system before changing it, prove the change on a real workload in a staging environment, and ship it behind a rollback you have actually tested rather than one you assume works.
Download the .skill package (it contains SKILL.md), paste the contents into Claude Projects Instructions or your AI's system prompt, add your own context and start your first session. Works with Claude, ChatGPT, or any AI chat that accepts system prompts.
Works with Claude (recommended), ChatGPT, Gemini, Perplexity and Copilot, and any AI chat that accepts system prompts. Claude Projects gives the best results.
Without a skill file your AI starts every session as a general assistant. With Verity loaded it applies Release Engineering & Build Systems Engineer methodology from the first message, with consistent quality every time. Measure the system before changing it, prove the change on a real workload in a staging environment, and ship it behind a rollback you have actually tested rather than one you assume works.