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Azure Pipelines Validator

Community
akin-ozer
azure-pipelines-validator

Validate, lint, audit, or review azure-pipelines.yml — syntax, security, best practices.

Overview

Publisherakin-ozer
Repositorycc-devops-skills
Skill nameazure-pipelines-validator
Stars
326
Forks
39
Bundled files
19
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.

  • 19 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 Validator 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-validator
  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-validator .claude/skills/azure-pipelines-validator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure Pipelines Validator 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 Validator 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 Validator 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 Validator

Use this skill to validate Azure DevOps pipeline YAML (azure-pipelines.yml / azure-pipelines.yaml) with local scripts first, then escalate to docs only when local output is not enough.

Trigger Phrases

Use this skill when the user asks things like:

  • "Validate my azure-pipelines.yml."
  • "Why is this Azure pipeline YAML failing?"
  • "Run a security scan on this Azure DevOps pipeline."
  • "Check this pipeline for best-practice issues."
  • "Review this pipeline in CI before merge."

Do not use this skill for pipeline generation from scratch. Use azure-pipelines-generator for that.

Deterministic Path Setup (No Ambiguity)

Run from any directory using explicit absolute paths:

bash
REPO_ROOT="$(git rev-parse --show-toplevel 2>/dev/null)"
SKILL_DIR="$REPO_ROOT/devops-skills-plugin/skills/azure-pipelines-validator"
PIPELINE_FILE="$REPO_ROOT/azure-pipelines.yml"

If REPO_ROOT is empty, stop and ask for the repository root path. Do not guess paths.

Validate one file:

bash
bash "$SKILL_DIR/scripts/validate_azure_pipelines.sh" "$PIPELINE_FILE"

Auto-detect from current directory (up to depth 3):

bash
bash "$SKILL_DIR/scripts/validate_azure_pipelines.sh"

If auto-detect returns multiple files, rerun with one explicit file path.

Local-First Execution Model

  1. Preflight
  • Confirm bash and python3 are available.
  • Confirm target file exists.
  1. Run local validator
  • Default full pass:
bash
bash "$SKILL_DIR/scripts/validate_azure_pipelines.sh" "$PIPELINE_FILE"
  • Syntax only:
bash
bash "$SKILL_DIR/scripts/validate_azure_pipelines.sh" "$PIPELINE_FILE" --syntax-only
  • Best practices only:
bash
bash "$SKILL_DIR/scripts/validate_azure_pipelines.sh" "$PIPELINE_FILE" --best-practices
  • Security only:
bash
bash "$SKILL_DIR/scripts/validate_azure_pipelines.sh" "$PIPELINE_FILE" --security-only
  • Strict mode (warnings fail):
bash
bash "$SKILL_DIR/scripts/validate_azure_pipelines.sh" "$PIPELINE_FILE" --strict
  1. Interpret exit behavior
  • 0: pass (or non-blocking checks only)
  • 1: validation failed (blocking issues)
  • 2: invalid invocation (missing/ambiguous file or bad args)
  1. Return findings in the report format below.

Expected Report Format (Severity Buckets)

Always return results in this structure:

text
Validation Report: <path>

Summary:
- Blocking: <count>        # Syntax errors + Security critical/high
- Warning: <count>         # Security medium/low + best-practice warnings
- Info: <count>            # Suggestions
- Skipped: <count>         # Explicitly name skipped checks

Findings:
- [Blocking][syntax][<rule-id>] line <n> - <message>
- [Blocking][security-high][<rule-id>] line <n> - <message>
- [Warning][security-medium][<rule-id>] line <n> - <message>
- [Warning][best-practice][<rule-id>] line <n> - <message>
- [Info][best-practice][<rule-id>] line <n> - <message>

Remediation:
- <short, concrete fix per finding>

Execution Notes:
- Commands run: <exact commands>
- Environment/fallback notes: <tool missing, skipped checks, offline constraints>

Escalation Policy (Docs Only When Needed)

Run local checks first. Escalate only when at least one condition is true:

  • Local finding depends on current upstream behavior (task versions, deprecations, new inputs).
  • User asks for "latest/current/recent" Azure Pipelines task or schema details.
  • Local scripts cannot determine validity for a specific task/resource syntax.

Escalation order:

  1. Context7 docs tooling first.
text
mcp__context7__resolve-library-id(...)
mcp__context7__query-docs(...)
  1. Official docs second (learn.microsoft.com / Microsoft Azure DevOps docs).
  2. General web search only if the first two are insufficient.

When escalating, cite the source URL and state what local check could not answer.

Fallback Behavior

Use this matrix when tools are unavailable:

  • Condition: yamllint unavailable.

  • Action: Continue with syntax/best-practice/security checks.

  • Report note: "YAML lint skipped because yamllint is unavailable."

  • Condition: python3 unavailable or venv/dependency setup fails.

  • Action: Mark scripted validation blocked; perform manual YAML review only if requested.

  • Report note: "Local scripted validation blocked by missing Python runtime/dependencies."

  • Condition: No network while dependencies/docs are needed.

  • Action: Run whatever local checks are still possible; defer doc/version verification.

  • Report note: "External verification deferred due offline environment."

  • Condition: Multiple auto-detected pipeline files.

  • Action: Do not pick arbitrarily; require explicit target file path.

  • Report note: "Validation paused until a single target file is specified."

Rule Buckets (What the Scripts Check)

Syntax examples:

  • yaml-syntax
  • yaml-invalid-root
  • invalid-hierarchy
  • task-invalid-format
  • pool-invalid
  • deployment-missing-strategy

Best-practice examples:

  • missing-displayname
  • task-version-zero
  • task-missing-version
  • pool-latest-image
  • missing-cache
  • missing-deployment-condition

Security examples:

  • hardcoded-password
  • hardcoded-secret
  • curl-pipe-shell
  • eval-command
  • insecure-ssl
  • container-latest-tag
  • variable-not-secret

Use script output rule IDs directly in the report.

References and Examples

  • Syntax reference: docs/azure-pipelines-reference.md
  • Example pipelines: examples/

Quick local test:

bash
bash "$SKILL_DIR/scripts/validate_azure_pipelines.sh" "$SKILL_DIR/examples/basic-pipeline.yml"

Done Criteria

This skill execution is done when all conditions are true:

  • Trigger match is explicit and plain-language examples are provided near the top.
  • Validation command(s) were run with unambiguous paths.
  • Report uses severity buckets (Blocking, Warning, Info, Skipped).
  • Fallback behavior is explicitly reported for unavailable tools/environment constraints.
  • External docs were consulted only when local checks were insufficient.

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 Validator AI skill do?

Validate, lint, audit, or review azure-pipelines.yml — syntax, security, best practices.

Why use Azure Pipelines Validator on TypingMind?

Because you install it once and use it with any model. Azure Pipelines Validator 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 Validator 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-validator. 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 Validator?

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 Validator?

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

Is the Azure Pipelines Validator 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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