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Azure Pipelines Generator

Community
akin-ozer
azure-pipelines-generator

Generate/create/scaffold azure-pipelines.yml, stages, jobs, steps, or reusable templates.

Overview

Publisherakin-ozer
Repositorycc-devops-skills
Skill nameazure-pipelines-generator
Stars
326
Forks
39
Bundled files
14
LicenseApache-2.0
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.

  • 14 bundled files

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

  • Open source

    Published by akin-ozer on GitHub. Read the source before you install it.

Installation

Install the Azure Pipelines Generator 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/akin-ozer/cc-devops-skills/tree/main/devops-skills-plugin/skills/azure-pipelines-generator
  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/akin-ozer/cc-devops-skills.git /tmp/cc-devops-skills
mkdir -p .claude/skills
cp -r /tmp/cc-devops-skills/devops-skills-plugin/skills/azure-pipelines-generator .claude/skills/azure-pipelines-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure Pipelines Generator 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 Azure Pipelines Generator 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 Azure Pipelines Generator 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.

Azure Pipelines Generator

Generate production-ready Azure DevOps pipeline YAML with deterministic steps, explicit fallbacks, and clear completion criteria.

Trigger Guidance

Use this skill when the user asks to generate or redesign Azure Pipelines YAML, for example:

  • "Create azure-pipelines.yml for my Node service."
  • "Build a multi-stage Azure DevOps pipeline with staging and production."
  • "Generate Azure pipeline templates for reuse across repos."
  • "Convert this CI flow to Azure Pipelines."

Do not use this skill for validation-only requests. For validation-only work, use azure-pipelines-validator.

Execution Model

Normative keywords:

  • MUST: required
  • SHOULD: default unless user asks otherwise
  • MAY: optional

Deterministic sequence:

  1. Classify request mode.
  2. Capture minimum required inputs.
  3. Load minimum references (progressive disclosure).
  4. Generate YAML using the quality checklist.
  5. Validate (validator skill, script fallback, or manual fallback).
  6. Return output in the required report format.

If a step cannot run due to environment limits, use the fallback in that step and continue.

1) Classify Request Mode

Choose exactly one primary mode:

  • Basic CI: build/test/lint for one stack.
  • Multi-stage CI/CD: build -> test -> deploy with environment tracking.
  • Docker: image build/push and optional deploy.
  • Kubernetes: image build/push plus Kubernetes deployment.
  • Language-specific: .NET, Node.js, Python, Go, Java focused.
  • Template-based: reusable templates plus thin root pipeline.
  • Snippet-only: partial YAML requested, not full pipeline.

Mode-to-example mapping:

  • Basic CI -> examples/basic-ci.yml
  • Multi-stage CI/CD -> examples/multi-stage-cicd.yml
  • .NET -> examples/dotnet-cicd.yml
  • Python -> examples/python-cicd.yml
  • Go -> examples/go-cicd.yml
  • Kubernetes -> examples/kubernetes-deploy.yml
  • Template-based -> examples/template-usage.yml + examples/templates/*.yml

2) Capture Required Inputs

Collect these before generation:

  • App stack and package manager
  • Build/test commands and report expectations
  • Deployment target (none, Azure service, Docker registry, Kubernetes)
  • Environment flow (dev/staging/prod) and branch gates
  • Service connections, variable groups, secret handling
  • Template requirement (yes/no)

Safe defaults when missing:

  • CI branches: main, develop
  • Production deploy branch: main only
  • Agent image: pinned image (for example ubuntu-22.04)
  • Deploy image tag: immutable ($(Build.BuildId)), never deploy latest

If key details are missing, state assumptions explicitly in final output.

3) Load References (Progressive Disclosure)

Read local references first.

Always read:

  • docs/yaml-schema.md
  • docs/best-practices.md

Read conditionally:

  • docs/tasks-reference.md when selecting tasks/inputs
  • docs/templates-guide.md only for template-based mode

Then read only the closest example(s) from the mode mapping above.

Fallback behavior for missing references:

  • Missing example: use nearest mode example and note substitution.
  • Missing doc section: continue with known conventions and mark uncertainty.
  • Snippet-only request: read only the minimum needed for safe output.

The final response MUST include:

  • References used
  • References skipped or missing
  • Impact

4) External Docs Escalation (Only When Needed)

Escalate beyond local docs only when:

  • required task info is not in local docs
  • task version compatibility is unclear
  • troubleshooting a task-specific failure

Use this order:

  1. Context7 (mcp__context7__resolve-library-id -> mcp__context7__query-docs)
  2. Official docs search (Microsoft Learn first)

If network/tools are unavailable, proceed with best-known local guidance and add a residual-risk note.

5) Pipeline Generation Checklist

Apply all items below unless user asks for a narrow snippet.

Security:

  • Never hardcode secrets.
  • Use service connections and variable groups/secrets.
  • Use immutable deploy image tags.

Versioning:

  • Pin vmImage to explicit version, not *-latest.
  • Pin task major versions (Task@N).
  • @0 is allowed only when that task uses major 0.

Reliability:

  • Use explicit dependsOn.
  • Add timeoutInMinutes for long-running jobs.
  • Use branch-gated deployment condition rules.
  • Use deployment jobs with environment for deploy stages.

Performance:

  • Use Cache@2 where it improves dependency install time.
  • Use shallow checkout when full history is not required.
  • Publish only required artifacts.

Testing/observability:

  • Run lint/tests in CI.
  • Publish test results with condition: succeededOrFailed().
  • Publish coverage when available.

Maintainability:

  • Add displayName for stages/jobs/key steps.
  • Use templates when logic repeats.
  • Add short comments only for non-obvious logic.

6) Validation Workflow

Default path (MUST for full pipeline generation):

  1. Generate or update YAML.
  2. Validate with azure-pipelines-validator.
  3. Fix findings.
  4. Re-run validation until no blocking issues remain.

Script fallback if validator skill is unavailable but local validator scripts exist:

bash
bash devops-skills-plugin/skills/azure-pipelines-validator/scripts/validate_azure_pipelines.sh <pipeline-file>

Manual fallback when neither skill nor script can run:

  1. YAML structure/indentation sanity
  2. Hierarchy sanity (stages -> jobs -> steps)
  3. Task format sanity (Task@Major)
  4. Secret exposure scan (no plaintext credentials/tokens)
  5. Deployment safety scan (environment usage, immutable deploy tags)

When fallback is used, final response MUST include:

  • Validation status: Manual fallback
  • Checks performed
  • Residual risk

Validation MAY be skipped only for:

  • snippet-only YAML
  • documentation-only examples
  • explicit user request to skip validation

7) Output Contract

Final response MUST include:

  1. Pipeline YAML (or template set)
  2. Required setup:
    • service connections
    • variable groups/secrets
    • environments and approvals/checks
  3. Validation result:
    • validator status, script status, or manual fallback status
  4. Assumptions
  5. References used/skipped and impact
  6. Optional next improvements

8) Canonical Example Flows

Example A: Full multi-stage generation

  1. Select mode: Multi-stage CI/CD.
  2. Capture stack/deploy/service-connection inputs.
  3. Read docs/yaml-schema.md, docs/best-practices.md, and examples/multi-stage-cicd.yml.
  4. Add requested customizations (stages, branch gates, environments, tasks).
  5. Validate with azure-pipelines-validator; fix and re-run.
  6. Return YAML + setup + validation + assumptions + references.

Example B: Quick snippet generation

  1. Select mode: Snippet-only.
  2. Read only the minimum required reference section.
  3. Generate focused YAML snippet with safe defaults.
  4. Skip full validation and state Validation status: Skipped (snippet-only).
  5. Return snippet + assumptions + references.

9) Definition of Done

The execution is complete only when all applicable checks pass:

  • Request mode is explicitly chosen.
  • Assumptions are explicit for missing inputs.
  • YAML follows checklist requirements (security, versioning, reliability, performance, maintainability).
  • Validation path is documented (validator, script fallback, or manual fallback).
  • Final response follows the output contract, including references and impact.

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 Azure Pipelines Generator AI skill do?

Generate/create/scaffold azure-pipelines.yml, stages, jobs, steps, or reusable templates.

Why use Azure Pipelines Generator on TypingMind?

Because you install it once and use it with any model. Azure Pipelines Generator 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 Azure Pipelines Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/akin-ozer/cc-devops-skills/tree/main/devops-skills-plugin/skills/azure-pipelines-generator. 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 Azure Pipelines Generator?

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 Azure Pipelines Generator?

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

Is the Azure Pipelines Generator AI skill free?

Yes. It is published on GitHub by akin-ozer under the Apache-2.0 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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