glab▌
henricook/claude-glab-skill · updated Apr 26, 2026
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Provides guidance for using glab, the official GitLab CLI, to perform GitLab operations from the terminal.
GitLab CLI (glab) Skill
Provides guidance for using glab, the official GitLab CLI, to perform GitLab operations from the terminal.
When to Use This Skill
Invoke when the user needs to:
- Create, review, or manage merge requests
- Work with GitLab issues
- Monitor or trigger CI/CD pipelines
- Clone or manage repositories
- Perform any GitLab operation from the command line
Prerequisites
Verify glab installation before executing commands:
glab --version
If not installed, inform the user and provide platform-specific installation guidance.
Authentication Quick Start
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
Core Workflows
Creating a Merge Request
# 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"
Reviewing Merge Requests
# 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"
Managing Issues
# 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
Monitoring CI/CD
# 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
Common Patterns
Working Outside Repository Context
When not in a Git repository, specify the repository:
glab mr list -R owner/repo
glab issue list -R owner/repo
Self-Hosted GitLab
Set hostname for all commands:
export GITLAB_HOST=gitlab.example.org
# or per-command
glab repo clone gitlab.example.org/owner/repo
Automation and Scripting
Use JSON output for parsing:
glab mr list --output=json | jq '.[] | .title'
Using the API Command
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"
Best Practices
- Verify authentication before executing commands:
glab auth status - Use
--helpto explore command options:glab <command> --help - Link MRs to issues using "Closes #123" in MR description
- Lint CI config before pushing:
glab ci lint - Check repository context when commands fail:
git remote -v
Common Commands Quick Reference
Merge 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 MR
Issues:
glab issue list- List all issuesglab issue create- Create new issueglab issue close <number>- Close issue
CI/CD:
glab pipeline ci view- Watch pipelineglab ci status- Check statusglab ci lint- Validate .gitlab-ci.ymlglab ci retry- Retry failed pipeline
Repository:
glab repo clone owner/repo- Clone repositoryglab repo view- View repo detailsglab repo fork- Fork repository
Progressive Disclosure
For detailed command documentation, refer to:
- references/commands-detailed.md - Comprehensive command reference with all flags and options
- references/quick-reference.md - Condensed command cheat sheet
- references/troubleshooting.md - Detailed error scenarios and solutions
Load these references when:
- User needs specific flag or option details
- Troubleshooting authentication or connection issues
- Working with advanced features (API, schedules, variables, etc.)
Common Issues Quick Fixes
"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.
Notes
- glab auto-detects repository context from Git remote
- Most commands have
--webflag to open in browser - Use
--output=jsonfor scripting and automation - Multiple GitLab accounts can be authenticated simultaneously
- Commands respect Git configuration and current repository context
How to use glab on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add glab
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches glab from GitHub repository henricook/claude-glab-skill and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate glab. Access the skill through slash commands (e.g., /glab) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★26 reviews- ★★★★★Dhruvi Jain· Dec 4, 2024
I recommend glab for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Oshnikdeep· Nov 23, 2024
Solid pick for teams standardizing on skills: glab is focused, and the summary matches what you get after install.
- ★★★★★Ganesh Mohane· Oct 14, 2024
glab is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ava Gill· Sep 17, 2024
Registry listing for glab matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Hassan Agarwal· Sep 13, 2024
glab reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Kiara Agarwal· Sep 5, 2024
We added glab from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Kiara Khanna· Aug 24, 2024
glab reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ava Rao· Aug 8, 2024
Useful defaults in glab — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★William Ramirez· Aug 4, 2024
We added glab from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Rahul Santra· Jul 27, 2024
Useful defaults in glab — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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