Expert guidance for using the GitLab CLI (glab) to manage GitLab issues, merge requests, CI/CD pipelines, repositories, and other GitLab operations from the command line. Use this skill when the user needs to interact with GitLab resources or perform GitLab workflows.
npx skills add https://github.com/jjmartres/opencode --skill glab
Provides guidance for using glab, the official GitLab CLI, to perform GitLab operations from the terminal.
Invoke when the user needs to:
Verify glab installation before executing commands:
glab --version
If not installed, inform the user and provide platform-specific installation guidance.
Most glab operations require authentication:
# Interactive authentication
glab auth login
# Check authentication status
glab auth status
# For self-hosted GitLab
glab auth login --hostname gitlab.example.org
# Using environment variables
export GITLAB_TOKEN=your-token
export GITLAB_HOST=gitlab.example.org # for self-hosted
# 1. Ensure branch is pushed
git push -u origin feature-branch
# 2. Create MR
glab mr create --title "Add feature" --description "Implements X"
# With reviewers and labels
glab mr create --title "Fix bug" --reviewer=alice,bob --label="bug,urgent"
# 1. List MRs awaiting your review
glab mr list --reviewer=@me
# 2. Checkout MR locally to test
glab mr checkout <mr-number>
# 3. After testing, approve
glab mr approve <mr-number>
# 4. Add review comments
glab mr note <mr-number> -m "Please update tests"
# Create issue with labels
glab issue create --title "Bug in login" --label=bug
# Link MR to issue
glab mr create --title "Fix login" --description "Closes #<issue-number>"
# List your assigned issues
glab issue list --assignee=@me
# Watch pipeline in progress
glab pipeline ci view
# Check pipeline status
glab ci status
# View logs if failed
glab ci trace
# Retry failed pipeline
glab ci retry
# Lint CI config before pushing
glab ci lint
When not in a Git repository, specify the repository:
glab mr list -R owner/repo
glab issue list -R owner/repo
Set hostname for all commands:
export GITLAB_HOST=gitlab.example.org
# or per-command
glab repo clone gitlab.example.org/owner/repo
Use JSON output for parsing:
glab mr list --output=json | jq '.[] | .title'
The glab api command provides direct GitLab API access:
# Basic API call
glab api projects/:id/merge_requests
# IMPORTANT: Pagination uses query parameters in URL, NOT flags
# ❌ WRONG: glab api --per-page=100 projects/:id/jobs
# ✓ CORRECT: glab api "projects/:id/jobs?per_page=100"
# Auto-fetch all pages
glab api --paginate "projects/:id/pipelines/123/jobs?per_page=100"
# POST with data
glab api --method POST projects/:id/issues --field title="Bug" --field description="Details"
glab auth status--help to explore command options: glab <command> --helpglab ci lintgit remote -vMerge Requests:
glab mr list --assignee=@me - Your assigned MRsglab mr list --reviewer=@me - MRs for you to reviewglab mr create - Create new MRglab mr checkout <number> - Test MR locallyglab mr approve <number> - Approve MRglab mr merge <number> - Merge approved MRIssues:
glab issue list - List all issuesglab issue create - Create new issueglab issue close <number> - Close issueCI/CD:
glab pipeline ci view - Watch pipelineglab ci status - Check statusglab ci lint - Validate .gitlab-ci.ymlglab ci retry - Retry failed pipelineRepository:
glab repo clone owner/repo - Clone repositoryglab repo view - View repo detailsglab repo fork - Fork repositoryFor detailed command documentation, refer to:
Load these references when:
"command not found: glab" - Install glab or verify PATH
"401 Unauthorized" - Run glab auth login
"404 Project Not Found" - Verify repository name and access permissions
"not a git repository" - Navigate to repo or use -R owner/repo flag
"source branch already has a merge request" - Use glab mr list to find existing MR
For detailed troubleshooting, load references/troubleshooting.md.
--web flag to open in browser--output=json for scripting and automationAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take jjmartres/glab from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
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