Florian BRUNIAUX

Create a separate Git working directory for feature development without switching your current branch. The skill checks branch names and ignore settings, prepares dependencies, and starts background checks, with options for separate dependency installation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Starting feature work in a separate working directory
• Preparing a workspace for a bug fix
• Testing dependency upgrades with a fresh installation

Prepare a branch for a pull request by running local tests, checking types for Node projects, pushing commits, and showing the CI pipeline link. Failed checks stop the workflow before the push.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running tests before opening a pull request
• Checking Node types before pushing commits
• Getting the CI link after a push

Sort open GitHub issues by category, risk, inactivity, and possible duplication. Examine selected reports in detail, identify missing information, and prepare comments, labels, or closures for user approval before taking action.

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• Sort accumulated bug reports and feature requests
• Check possible duplicates against open and closed issues
• Review inactive issues for follow-up or closure

Audit Claude Code instruction rules against actual project files to find broken settings, overly broad scopes, and loading issues. Review each rule’s usefulness with the user, then apply confirmed changes or produce an audit report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Setting up Claude Code rules for the first time
• Investigating rules that apply too often or never
• Reviewing outdated instructions

Test a web application using branch changes, selected pages, or the whole app as scope. Choose among three testing levels, document issues with evidence, fix them in separate commits, and verify results in a release-readiness report.

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• Checking changed pages before opening a pull request
• Running a quick check for major bugs before committing
• Reviewing a release candidate for visual and functional issues

Examines project code, development history, tests, and documentation, then asks three focused questions to recommend a combination of AI-assisted development methods, with practical starting steps tailored to the project.

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• Choosing development methods for a solo prototype
• Reviewing workflows for an existing SaaS product
• Selecting evaluation methods for an AI product

Run repository tests using commands suited to Python, Node, or Rust projects. Detect the project setup from its files, run the full suite or a selected target, and show passing results or failure details.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run the full test suite before pushing changes
• Test a specific file or folder
• Use test commands suited to each project

Evaluate Claude Code agents, skills, and commands with weighted quality criteria. Get scores, readiness grades, template comparisons, and practical improvement suggestions in Markdown or JSON reports to guide reviews before release.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review skills before release
• Compare agent definitions with reference templates
• Quickly check critical quality criteria

Follow a structured Claude Code curriculum with tracks for different goals. Record concrete evidence from project exercises, unlock modules when prerequisites are complete, and check scheduled reviews while keeping progress data inside the current project.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Learn a first daily workflow with Claude Code
• Practice reusable workflows in a real project
• Keep evidence of completed learning exercises

Coordinate feature development from product direction through architecture, planning, validation, and implementation. Run the whole workflow or start at a selected stage, with saved decisions, review checkpoints, and a final pull request.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Take a feature idea through planning and implementation
• Review architecture before writing an implementation plan
• Validate an existing plan before execution

Audit a collection of agent skills for valid settings, clear activation conditions, complete workflows, and required files. Get scores for each target platform, prioritized fixes, and a separate record of whether activation was actually tested.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking skill quality before publication
• Reviewing an imported skill collection
• Investigating excessive or missing automatic activation

Clean up Git worktrees, separate working folders for branches, by identifying merged branches and reviewing unmerged ones. Preview deletions, preserve protected branches, and receive a report of removed folders and reclaimed disk space.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean up worktrees after merging branches
• Preview deletions and potential disk savings
• Review unmerged worktrees individually

Measure code patterns such as casts to any or disabled lint rules, then run repeated, targeted edits. Keep changes that improve the chosen metric, revert unsuccessful changes, and track results for each improvement loop.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding code patterns to prioritize for cleanup
• Preparing measurement files for an improvement loop
• Repeating targeted edits to reduce casts to any

Analyze changed files and commits before opening a pull request, a proposed code change for review. Identify oversized or unrelated changes, suggest splits, and create a GitHub PR with a summary, description, and checklist.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a pull request for review
• Check whether large changes should be split
• Create a draft PR with labels

Audit custom subagents, the specialized assistants used by Claude Code and Codex. Check whether definitions load, identify overlapping roles, review model choices and tool access, and receive scores with prioritized fixes and optional confirmed edits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking agents when joining a project
• Reviewing newly added or imported agents
• Investigating delegation to the wrong agent

Build an MCP integration skill using a template that reads server-specific reference notes before calling tools. The Sentry example gathers unresolved errors, groups them by likely root cause, and produces a report with investigation suggestions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Design a skill that integrates with an MCP server
• Document server-specific query pitfalls
• Group Sentry errors by likely root cause

Turn repository documentation and images into a complete landing page with features, setup instructions, and FAQs. Generate static HTML, CSS, and JavaScript files, plus a GitHub Actions workflow for publishing on GitHub Pages.

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• Turning a README into a project showcase page
• Creating a homepage for an open-source project
• Standardizing landing pages across projects

Check CI, the automated checks run on code changes, for the current branch alongside recent commits. Supports GitLab CI and GitHub Actions, with optional pull request or merge request numbers and links when command-line tools are unavailable.

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• Checking CI progress after a push
• Reviewing checks for a specific pull request
• Viewing a GitLab merge request

Check your understanding of code you recently wrote, edited, or accepted from AI. Answer questions one at a time, receive explanations, and finish with a score, strengths, review topics, and suggested practice.

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• Checking understanding of code accepted from AI
• Reviewing error handling in recent code
• Explaining trade-offs through harder questions

Generate tests for a specified file or module using the project’s testing conventions. Cover expected behavior, unusual inputs, boundaries, failures, and external interactions, with independent tests and mock substitutes for external dependencies.

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• Add tests for existing functions or modules
• Check empty inputs and boundary values
• Test failures in APIs, databases, and file operations

Assess a codebase across seven areas, including exposed credentials, security, dependencies, structure, and tests. Receive category scores, findings tied to file locations, and a prioritized improvement plan with smaller tasks that can be completed quickly.

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• Assessing the overall health of an inherited project
• Reviewing exposed credentials and security risks
• Identifying dependency and test coverage gaps

Monitor a live application after deployment and compare it with a saved baseline, a record of its earlier state. Check page responses, loading times, and new errors, flag persistent changes, and produce a per-page health report.

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• Recording a healthy baseline before deployment
• Monitoring production immediately after deployment
• Checking key pages for slower responses or failures

Create a structured Markdown handoff from the current conversation, covering scope, relevant files, discoveries, completed work, status, and next steps so another agent or a later session can resume the task.

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• Recording progress before ending a session
• Handing unfinished work to another agent
• Preserving findings and relevant code for later

Trace how AI tooling developed in a Git repository by identifying configuration file origins, monthly commit activity, major changes, and relevant releases. Produce a Markdown report that explains adoption phases and turning points using Git evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding when AI configuration files were first introduced
• Reviewing monthly activity around AI tooling changes
• Tracing major integrations and workflow changes