gauntlet-setup — community gauntlet-setup, flokay, community, ide skills, Claude Code, Cursor, Windsurf

v1.0.0
GitHub

About this Skill

Perfect for Development Agents needing automated project setup and configuration for agent-gauntlet. Flokay is a Claude plugin that provides a set of Agent Skills and an OpenSpec schema for spec-driven development.

pacaplan pacaplan
[6]
[0]
Updated: 3/3/2026

Agent Capability Analysis

The gauntlet-setup skill by pacaplan is an open-source community AI agent skill for Claude Code and other IDE workflows, helping agents execute tasks with better context, repeatability, and domain-specific guidance.

Ideal Agent Persona

Perfect for Development Agents needing automated project setup and configuration for agent-gauntlet.

Core Value

Empowers agents to configure checks and reviews for agent-gauntlet by scanning project tooling and utilizing YAML schemas, providing a robust spec-driven development experience with OpenSpec schema and Claude plugin integration.

Capabilities Granted for gauntlet-setup

Configuring project setup for agent-gauntlet
Automating check and review configurations
Validating YAML configurations against OpenSpec schema

! Prerequisites & Limits

  • Requires .gauntlet/config.yml file to exist
  • Needs agent-gauntlet init to be run first if config file does not exist
  • Specific to agent-gauntlet and Claude plugin ecosystem
Labs Demo

Browser Sandbox Environment

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Boot Container Sandbox

gauntlet-setup

Install gauntlet-setup, an AI agent skill for AI agent workflows and automation. Works with Claude Code, Cursor, and Windsurf with one-command setup.

SKILL.md
Readonly

/gauntlet-setup

Scan the project to discover tooling and configure checks and reviews for agent-gauntlet.

Before starting, read references/check-catalog.md for check category details, YAML schemas, and example configurations.

Step 1: Check config exists

Read .gauntlet/config.yml. If the file does not exist, tell the user to run agent-gauntlet init first and STOP — do not proceed with any further steps.

Step 2: Check existing config

Read the entry_points field from .gauntlet/config.yml.

If entry_points is empty ([]): This is a fresh setup. Proceed to Step 3 (detect project structure).

If entry_points is populated: Show the user a summary of the current configuration:

  • List each entry point with its path, checks, and reviews

  • Then ask the user which action to take:

    1. Add checks — Scan for tools not already configured. Proceed to Step 3, but filter out any checks that already appear in entry_points.
    2. Add custom — User describes what they want to add. Skip to Step 7.
    3. Reconfigure — Start fresh. Back up existing files first:
      • Rename each .gauntlet/checks/*.yml file to .yml.bak (overwrite any previous .bak files)
      • Rename each custom .gauntlet/reviews/*.md file to .md.bak (overwrite any previous .bak files)
      • Do NOT rename .gauntlet/reviews/*.yml files (these are built-in review configs)
      • Clear entry_points to [] in config.yml
      • Proceed to Step 3

Step 3: Detect project structure

Scan for signals to classify the project as monorepo, split project, or single project.

Monorepo signals

  • package.json with a workspaces field
  • pnpm-workspace.yaml
  • lerna.json, nx.json, turbo.json
  • Cargo.toml with a [workspace] section
  • Multiple subdirectories under packages/, apps/, or services/ each containing their own project manifest (package.json, go.mod, Cargo.toml, pyproject.toml)

Split project signals

  • frontend/ + backend/ (or client/ + server/, web/ + api/) directories each containing source code and/or their own project manifest
  • Multiple apps or libraries of the same language under a common parent directory (e.g., apps/web/, apps/api/, apps/worker/ each with their own source and config) — suggests a wildcard entry point like apps/*

Single project signals

  • src/ or lib/ as sole source directory, or source files at project root
  • No monorepo or split project signals found

If monorepo or split project: Read references/project-structure.md for detailed multi-project entry point guidance, then follow it for Steps 4 through 8. The rest of this file covers the single-project flow.

If single project: Tell the user what you detected and continue below.

Step 4: Determine entry point path

Infer the source directory:

  • If src/ exists and contains source code, suggest src
  • If lib/ exists and contains source code, suggest lib
  • Otherwise suggest . (project root — safer default since it captures all changes)

Skip this step if adding checks to an existing entry point that already has a path.

Step 5: Scan for tooling

Scan the project for tooling signals across the 6 check categories listed in references/check-catalog.md.

For the "add checks" path: Filter out checks already configured in entry_points.

If no tools discovered: Offer the custom flow (skip to Step 7). Still include code-quality review.

Step 6: Present findings and confirm

Show a table of discovered checks:

Category        | Tool            | Command                              | Confidence
----------------|-----------------|--------------------------------------|-----------
Build           | npm             | npm run build                        | High
Lint            | ESLint          | npx eslint .                         | High
Typecheck       | TypeScript      | npx tsc --noEmit                     | High
Test            | Jest            | npx jest                             | High
Security (deps) | npm audit       | npm audit --audit-level=moderate     | Medium
Security (code) | Semgrep         | semgrep scan --config auto --error . | Medium

Confidence levels:

  • High — Tool config file found AND/OR explicit script in package.json/Makefile
  • Medium — Tool found in devDependencies or inferred from CI workflow but no dedicated config
  • Low — Only indirect evidence (e.g., test directory exists but no runner config found)

If a category has no discovered tool, show (not found) with for command and confidence.

Ask the user:

  1. Which checks to enable (default: all)
  2. Whether any commands need adjustment

If the user declines ALL checks, still include code-quality review and offer the custom flow (Step 7).

After confirmation, proceed to Step 8 (create files).

Step 7: Add custom

Ask the user: check (shell command) or review (AI code review)?

For checks: Ask for command, name, and optional settings (timeout, parallel, run_in_ci, run_locally).

For reviews: Built-in (code-quality) or custom prompt? Ask for name and write the review content.

Step 8: Create files and update config

Checks — Create .gauntlet/checks/<name>.yml with command, parallel: true, run_in_ci: true, run_locally: true. Add optional fields only when specified. See references/check-catalog.md for schema.

Custom reviews — Create .gauntlet/reviews/<name>.md with YAML frontmatter (num_reviews: 1) and review prompt.

Built-in reviews — Create .gauntlet/reviews/<name>.yml with builtin: code-quality and num_reviews: 1.

Update entry_points in .gauntlet/config.yml:

yaml
1entry_points: 2 - path: "<source_dir>" 3 checks: 4 - <check-name-1> 5 - <check-name-2> 6 reviews: 7 - code-quality

Always include code-quality in reviews for fresh setups. For "add checks" / "add custom": append to the appropriate entry point's lists, or add a new entry point if needed.

Step 9: "Add something else?"

Ask the user. If yes, loop to Step 7. If no, proceed.

Step 10: Validate

Run agent-gauntlet validate. If it fails, apply one corrective attempt and re-validate. If it still fails, STOP and ask the user.

Step 11: Commit configuration

Commit all gauntlet configuration and skills so the setup is preserved in version control:

  1. Stage all new/modified files: .gauntlet/, .claude/skills/gauntlet-*/, .claude/settings.local.json, .gitignore
  2. Create a commit: git commit -m "chore: configure agent-gauntlet checks and reviews"

If there are no changes to commit (everything already committed), skip this step silently.

Step 12: Suggest next steps

Tell the user: configuration is complete. Run /gauntlet-run to execute, or /gauntlet-setup again to add more.

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is gauntlet-setup?

Perfect for Development Agents needing automated project setup and configuration for agent-gauntlet. Flokay is a Claude plugin that provides a set of Agent Skills and an OpenSpec schema for spec-driven development.

How do I install gauntlet-setup?

Run the command: npx killer-skills add pacaplan/flokay/gauntlet-setup. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for gauntlet-setup?

Key use cases include: Configuring project setup for agent-gauntlet, Automating check and review configurations, Validating YAML configurations against OpenSpec schema.

Which IDEs are compatible with gauntlet-setup?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for gauntlet-setup?

Requires .gauntlet/config.yml file to exist. Needs agent-gauntlet init to be run first if config file does not exist. Specific to agent-gauntlet and Claude plugin ecosystem.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add pacaplan/flokay/gauntlet-setup. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use gauntlet-setup immediately in the current project.

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