ansible-validate
Validates Ansible playbook syntax and executes ansible-lint against project configurations.
Install
mkdir -p .claude/skills/ansible-validate && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/12055" && unzip -o skill.zip -d .claude/skills/ansible-validate && rm skill.zipInstalls to .claude/skills/ansible-validate
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Validate Ansible playbook syntax and run ansible-lintKey capabilities
- →Activate the project's virtual environment
- →Run Ansible playbook syntax checks
- →Execute ansible-lint on specified scopes
- →Report success or failure of checks
- →Suggest fixes based on project configuration
How it works
The skill activates a specified virtual environment and then runs ansible-playbook for syntax checks and ansible-lint for code quality, reporting outcomes.
Inputs & outputs
When to use ansible-validate
- →Validate playbook syntax
- →Lint Ansible roles
- →Check configuration files
- →Test inventory configuration
About this skill
Validate Ansible syntax and lint for this project.
Steps
-
Activate the project venv and run syntax check:
source ~/.ansible-venv/bin/activate && ansible-playbook -vv -i tests/inventory tests/test.yml --syntax-check -
Run ansible-lint on the target scope:
- If
$ARGUMENTSis empty, lint the full playbook:source ~/.ansible-venv/bin/activate && ansible-lint site.yml - If
$ARGUMENTSspecifies a role name (e.g.,vagrant), lint that role:source ~/.ansible-venv/bin/activate && ansible-lint local-roles/<role_name>/ - If
$ARGUMENTSspecifies a file path, lint that file:source ~/.ansible-venv/bin/activate && ansible-lint <file_path>
- If
-
Report results clearly:
- If both pass: confirm success
- If either fails: show the errors and suggest fixes based on the project's
.ansible-lintconfig (production profile, see skip_list for tolerated rules)
Notes
- The venv is at
~/.ansible-venv - The lint config is in
.ansible-lint(production profile) - External roles in
roles/are excluded from linting - All working paths are relative to the project root
When not to use it
- →When linting external roles in the 'roles/' directory
- →When the project does not use a virtual environment at ~/.ansible-venv
- →When the project does not have an .ansible-lint configuration file
Limitations
- →External roles in `roles/` are excluded from linting
- →The virtual environment path is fixed at `~/.ansible-venv`
- →The lint configuration is fixed to `.ansible-lint`
How it compares
This skill automates the setup and execution of Ansible syntax and lint checks, providing a structured approach compared to manually running commands.
Compared to similar skills
ansible-validate side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| ansible-validate (this skill) | 0 | 5mo | Review | Beginner |
| machine-learning-ops-ml-pipeline | 4 | 4mo | No flags | Advanced |
| uv | 3 | 5mo | Review | Beginner |
| vastai-core-workflow-b | 1 | 27d | Review | Advanced |
Try saying
Example prompts that trigger this skill in your AI assistant.
You might also like
machine-learning-ops-ml-pipeline
sickn33
Design and implement a complete ML pipeline for: $ARGUMENTS
uv
mitsuhiko
Use `uv` instead of pip/python/venv. Run scripts with `uv run script.py`, add deps with `uv add`, use inline script metadata for standalone scripts.
vastai-core-workflow-b
jeremylongshore
Execute Vast.ai secondary workflow: Core Workflow B. Use when implementing secondary use case, or complementing primary workflow. Trigger with phrases like "vastai secondary workflow", "secondary task with vastai".
mflux-dev-env
filipstrand
Set up and work in the mflux dev environment (arm64 expectation, uv, Makefile targets, lint/format/test).
hugging-face-jobs
patchy631
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
mlops-initialization
fmind
Guide to initialize a new MLOps project with standard tools (uv, git, VS Code) and best practices.