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Release Verification

Community
nahid-sparktales
release-verification

Prove a deployment is healthy rather than merely finished — the revision actually serving asked of the running system, every instance moved, smoke paths walked against the real environment, error rate and latency compared to a pre-deploy baseline. Use after any deploy to a shared environment, before a release is called good, or when checking someone else's claim that it went fine. Not for planning the rollout (deployment) or executing a revert (rollback); it never accepts a green pipeline, a 200 from a health endpoint, or a passing test suite as proof.

Overview

Publishernahid-sparktales
Repositoryagent-dispatcher
Skill namerelease-verification
Stars
58
Forks
5
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nahid-sparktales on GitHub. Read the source before you install it.

Installation

Install the Release Verification AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/devops/release-verification
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nahid-sparktales/agent-dispatcher.git /tmp/agent-dispatcher
mkdir -p .claude/skills
cp -r /tmp/agent-dispatcher/skills/devops/release-verification .claude/skills/release-verification
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Release Verification in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Release Verification on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Release Verification is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Release verification

"The deploy finished" is a fact about a pipeline. It says nothing about which revision is serving, whether all of it is serving, or whether the thing serving works. Those are three separate questions and this procedure answers them in that order.

When this fires

A deploy has completed to any shared environment and someone is about to call the release good — your deploy or somebody else's. It fires again after every rollback, hotfix and re-run. It does not fire for a build, and it is not a substitute for tests.

Procedure

  1. Write down what healthy means before you look. Which signals, what threshold, over what window, compared to what baseline. Deciding after seeing the numbers is how a bad release gets talked into being fine.

  2. Capture the pre-deploy baseline, or declare it missing. Error rate, latency at the percentile that matters, throughput, saturation — from before the change. Without it you can report absolute numbers but you cannot say the release did not make anything worse. Say that in those words.

  3. Ask the running system which revision it is serving. A version or build-info surface the application itself exposes, the image digest reported by the running instances, a revision header — something answered by the process handling traffic. The deploy tool's own success message is the claim under test, not evidence for it. If nothing exposes the revision, that is a finding worth fixing, and today's verification is weaker for it.

  4. Confirm every instance moved. Replicas, regions, edge locations, workers. Sampling one response cannot distinguish a complete rollout from a half-finished one — a mixed fleet answers correctly most of the time. Count what is running each revision.

  5. Read health and readiness for what they actually prove. A handler returning a static 200 proves the process is up. It does not prove the database is reachable, the cache is warm, the queue is consumed or credentials in this environment are valid. Check the dependency the release touched directly.

  6. Walk the smoke paths against the deployed environment. The few journeys whose failure means the release is bad: sign in, the primary read, one authenticated write, and any path this change touched. Real requests to the real deployment — not localhost, not a mock, not the test suite that already passed on this artifact. For a rendered surface, see browser-verification.

  7. Check the asynchronous surface, which fails later and more quietly than requests: background jobs draining, scheduled work firing, queue depth flat rather than climbing, webhook deliveries succeeding, dead-letter counts unchanged. A release that broke only the consumers looks perfect from the front door.

  8. Compare the signals to the baseline over a stated window. Error rate, latency, saturation and traffic volume, by revision where your telemetry can split it. Name the window. Two minutes of clean traffic at 3am is evidence about two minutes of traffic at 3am.

  9. Check what the release depended on, in the deployed environment: migrations recorded as applied there (see database-migration-verification), feature flags at their intended values, caches and CDN serving the new assets rather than stale ones, third-party integrations authenticating with this environment's credentials.

  10. Report with the verbs kept apart — built, deployed, serving, smoke-tested, verified, and watched for how long. This procedure exists because those get collapsed into "it's live".

What this refuses to conclude

  • Without asking the running system: that the intended revision is serving. A pipeline's exit code is not a statement about production.
  • Without counting instances: that the rollout is complete.
  • Without a baseline: that the release did not degrade anything.
  • Without walking the smoke paths: that the application works — a healthy process can serve errors perfectly reliably.
  • Without checking jobs and queues: anything about asynchronous work.
  • Without a stated observation window: that it is stable. Verified at low traffic is not verified at peak, and the report says which one it was.
  • Against a different environment: anything. A pass on staging is evidence about staging.

Checklist

  • Health defined — signals, thresholds, window — before looking
  • Baseline captured, or its absence stated as a limit
  • Serving revision read from the running system and matched to the intended artifact
  • Instance / replica / region count reconciled, not sampled once
  • Dependency the release touched checked directly, not via a static health handler
  • Smoke paths walked against the deployed environment, including one write path
  • Jobs, queues, schedules and webhooks checked
  • Signals compared to baseline over a named window
  • Migrations, flags, caches and integrations confirmed in this environment
  • Everything unchecked listed by name

Failure handling

  • The serving revision is not the one deployed — stop verifying and report it. Everything after that point is evidence about the wrong build.
  • A signal is degraded — capture the evidence before anything changes it: the failing request, the error text, the timestamp, the revision. Then hand the decision to rollback; this skill reports, it does not revert, and reverting production is itself a production change needing its own confirmation.
  • A smoke path fails intermittently — report it as intermittent with what you tried. An intermittent failure called fixed is worse than one called intermittent.
  • Telemetry is unavailable or cannot split by revision — say which conclusions that costs you, name what you did check, and do not round the result up to verified.
  • Read-only or no access to the environment — the release is unverified. Report it that way. Degrading honestly beats a false pass.

Evidence to report

The environment and the artifact id, named together. How you asked for the serving revision and what it answered. The instance counts per revision. Each smoke path and what it actually returned, not that it "worked". The signals, with the baseline beside them and the window stated. Queue and job state. Then the explicit list of what was not checked and for how long it was watched. A summary that says "verified" without these is a claim, not verification.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Release Verification AI skill do?

Prove a deployment is healthy rather than merely finished — the revision actually serving asked of the running system, every instance moved, smoke paths walked against the real environment, error rate and latency compared to a pre-deploy baseline. Use after any deploy to a shared environment, before a release is called good, or when checking someone else's claim that it went fine. Not for planning the rollout (deployment) or executing a revert (rollback); it never accepts a green pipeline, a 200 from a health endpoint, or a passing test suite as proof.

Why use Release Verification on TypingMind?

Because you install it once and use it with any model. Release Verification is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Release Verification in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nahid-sparktales/agent-dispatcher/tree/main/skills/devops/release-verification. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Release Verification?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Release Verification?

As many as you like. As long as a model supports skills, you can use Release Verification with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Release Verification AI skill free?

Yes. It is published on GitHub by nahid-sparktales under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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