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Linear Release Setup

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novuhq
linear-release-setup

Generate CI/CD configuration for Linear Release. Use when setting up release tracking, configuring CI pipelines for Linear, or integrating deployments with Linear releases. Supports GitHub Actions, GitLab CI, CircleCI, and other platforms.

Overview

Publishernovuhq
Repositorynovu
Skill namelinear-release-setup
Stars
40.1K
Forks
4.5K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by novuhq on GitHub. Read the source before you install it.

Installation

Install the Linear Release Setup 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/novuhq/novu/tree/next/.agents/skills/linear-release-setup
  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/novuhq/novu.git /tmp/novu
mkdir -p .claude/skills
cp -r /tmp/novu/.agents/skills/linear-release-setup .claude/skills/linear-release-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Linear Release Setup 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 Linear Release Setup 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 Linear Release Setup 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.

Linear Release Setup

The linear-release README is the source of truth for commands, flags, installation, environment variables, path filtering, and troubleshooting. Fetch it before generating any config — this skill focuses on the interactive setup workflow and the pipeline modeling decisions the README cannot make for the user.

Interactive Workflow

Step 1: Preflight

Before generating config, confirm:

  1. Pipeline exists in Linear — the user must have created a release pipeline in Linear first (Settings → Releases). Each pipeline has its own access key.
  2. Detect CI platform — look for .github/workflows/*.yml (GitHub Actions), .gitlab-ci.yml (GitLab CI), .circleci/config.yml (CircleCI), or other CI config.
  3. Detect default branch — check git symbolic-ref refs/remotes/origin/HEAD or the CI config. Don't assume main.

Step 2: Map pipelines, then ask

Start by listing every build the user ships independently — each becomes its own Linear pipeline. Pipeline-vs-stage confusion is the single most common setup mistake, so whenever a split isn't obvious, apply the test in "Stages vs Pipelines" below.

Ask, in order:

  1. CI platform — if not auto-detected.

  2. What do you ship, and to whom? Prompt explicitly about common split candidates: production vs. beta or TestFlight, nightly or dogfood builds, staging, per-platform builds (iOS, Android, web), per-service in a monorepo. For each candidate, apply the test: can these hold different commits at the same time? Yes → separate pipelines. No (same immutable build moving through gates) → one pipeline with stages.

  3. For each pipeline: continuous or scheduled?

    • Continuous — every deploy completes a release. Typical for nightlies, dogfood, and web apps that ship on merge.
    • Scheduled — releases collect changes over time and move through stages before shipping. Typical for versioned mobile and on-prem.

    The test: does the team need to track a release before it ships — naming it, seeing what's queued in it, or moving it through phases (code freeze, QA, etc.)?

    • Yes → scheduled (the release exists as an in-progress thing before it ships).
    • No → continuous (the release is created at the moment of shipping).
  4. For each scheduled pipeline, ask explicitly:

    • Branch model — just main, or main + release branches (release/*)?
    • Version source — calendar (2026.05), semver (1.2.0), or commit SHA? Derived from branch name, CI variable, file, or git tag?
    • Stages — what phases does a release move through before completion (e.g. "code freeze", "in qa")? Stages are gates on one build, not separate pipelines.
    • Automation — all manual via workflow_dispatch, or automated (e.g. cutting a release branch auto-promotes it)?
  5. Monorepo paths — if multiple pipelines share one repo, note which paths belong to each and wire up path filters in Linear pipeline settings or via --include-paths.

Step 3: Generate the CI configuration

Fetch the README first for the current commands, flags, install snippet, and command-targeting rules. For GitHub Actions, prefer the official action (linear/linear-release-action@v0); for other platforms, use the CLI binary per the README's Installation section.

Runtime requirements (Docker-based CI)

The image running the linear-release job must provide:

  • glibc. The prebuilt binary is dynamically linked against glibc and will not run on Alpine/musl images. Pick a Debian/Ubuntu base (debian:bookworm-slim, ubuntu:24.04, buildpack-deps:bookworm). Avoid alpine, any *-alpine tag, and curlimages/curl — on musl, the binary fails with an opaque "not found" error because the glibc dynamic loader is absent.
  • git. Slim images do not include it. Install it explicitly: apt-get update && apt-get install -y git.
  • curl (or wget). Needed to download the CLI binary.
GitLab CI: check existing variables

If .gitlab-ci.yml already exists, inspect any default variables: block. The linear-release job needs a full clone, so override at the job level when project defaults would prevent that:

  • GIT_STRATEGY: clone — required if the project default is none or empty (both skip cloning entirely).
  • GIT_DEPTH: 0 — set this on the linear-release job regardless. New GitLab projects default to a shallow clone of depth 20, and projects often lower it further.

Pick the matching example template, adapt it (branch patterns, stage names, paths, version format), and add it to an existing workflow or create a new one. Multiple pipelines mean multiple workflows or jobs, each calling the CLI with its own access key — one secret per pipeline (e.g. LINEAR_ACCESS_KEY_IOS, LINEAR_ACCESS_KEY_WEB).

PlatformPipeline TypeExample
GitHub ActionsContinuousgithub-actions-continuous/
GitHub ActionsScheduledgithub-actions-scheduled/
GitLab CIContinuousgitlab-ci-continuous/
GitLab CIScheduledgitlab-ci-scheduled/
CircleCIContinuouscircleci-continuous/
CircleCIScheduledcircleci-scheduled/

Each scheduled example includes a monorepo note in the header explaining how to split workflows for path filtering per platform.

Step 4: Remind about secrets

Tell the user to add the LINEAR_ACCESS_KEY secret to their CI environment:

  • GitHub Actions: Repository Settings → Secrets and variables → Actions → New repository secret
  • GitLab CI: Settings → CI/CD → Variables
  • CircleCI: Project Settings → Environment Variables

The access key is created in Linear from the pipeline's settings page. Each pipeline has its own access key.

Key Concepts

A Linear release pipeline is one independent stream of releases, with its own version history, current release, and access key. This is not a CI pipeline; it is the unit Linear uses to track releases, and your CI config calls the CLI to update it. Different products, environments, or distribution channels that ship independently are different pipelines.

Pipelines come in two types — continuous and scheduled. See the README's Pipeline Types section for the canonical description of each.

Stages vs Pipelines

A pipeline is one stream of releases. A stage is one phase inside a release on that pipeline. Confusing the two is the single most common setup mistake — work through the test below before writing any config.

The test: can two things be in-flight at the same time, holding different commits?

  • Yes → separate pipelines. TestFlight running on HEAD while production ships 1.2 from a release branch. Web staging auto-deploying from main while prod lags behind. A hotfix landing in one stream but not the other.
  • No, it's the same build moving through gates → one pipeline with stages. A release is cut at 1.2, goes through code freeze, QA, and RC soak, then ships. The build never changes; only the phase does.

Stages are process gates: "code freeze", "in qa", "in review", "rc soak". They only exist on scheduled pipelines.

Ambiguous cases — apply the test:

  • Beta / TestFlight. TestFlight soak before GA on the same build → stage on the production pipeline. A separate nightly or dogfood channel shipping distinct builds → its own pipeline.
  • Staging. Staging that auto-deploys from main (or runs hotfixes prod doesn't have) → separate pipeline. Staging that holds the exact same build as prod, just earlier in the promotion path → stage.
  • Per-service monorepo. Each service that ships independently → its own pipeline, scoped by path filters. Unambiguous; services are never stages.

Stages can also be frozen in Linear. A frozen stage makes sync (without --release-version) skip that release and land commits on the next one — a safety net for code freezes. This is a process tool, not a way to squeeze two pipelines into one.

Reference

Everything about commands, flags, environment variables, command targeting, path filtering, JSON output, and troubleshooting lives in the linear-release README. For GitHub Action inputs and how they map to CLI flags, see the action README. Always fetch these rather than relying on memory — they move ahead of this skill.

Checklist

  • Full clone / fetch-depth: 0 (GitLab: GIT_DEPTH: 0, and GIT_STRATEGY not none)
  • LINEAR_ACCESS_KEY set as a secret (one per pipeline)
  • Correct binary platform (linux-x64, darwin-arm64, or darwin-x64)
  • Docker-based CI: glibc base image (no Alpine/musl) with git and curl available
  • Triggers on the correct branches (main for continuous; main + release/* for scheduled)
  • Monorepo: path filters set (in Linear config or via --include-paths), and separate workflows if using release branches

Frequently asked questions

What does the Linear Release Setup AI skill do?

Generate CI/CD configuration for Linear Release. Use when setting up release tracking, configuring CI pipelines for Linear, or integrating deployments with Linear releases. Supports GitHub Actions, GitLab CI, CircleCI, and other platforms.

Why use Linear Release Setup on TypingMind?

Because you install it once and use it with any model. Linear Release Setup 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 Linear Release Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/novuhq/novu/tree/next/.agents/skills/linear-release-setup. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Linear Release Setup?

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 Linear Release Setup?

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

Is the Linear Release Setup AI skill free?

It is published on GitHub by novuhq. Check the repository for licensing terms. 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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