LynxPrompt

🥈Silver

LynxPrompt is an AI-powered IDE and tools rule config generator. It enables operations teams to create, browse, store, and share configuration files like AGENTS.md and CLAUDE.md via a WebUI or CLI. It connects to AI agents like Claude, streamlining the setup and management of AI workflows.

2510Updated 2mo ago

Intermediate30min to implementautomation

Saves ~30 min per use

Quick InstallView Source

git clone https://github.com/GeiserX/LynxPrompt.git

Works with:

Claude

Overview

About This Skill

LynxPrompt is a self-hostable platform that centralizes AI coding assistant configuration management for teams and individuals. It supports 30+ AI tools including Claude Code, Cursor, GitHub Copilot, and Windsurf, enabling users to generate, store, and share configuration files like AGENTS.md, CLAUDE.md, and .cursor/rules through a web UI or CLI. The interactive wizard auto-detects tech stacks from GitHub/GitLab URLs and supports template variables for monorepos. Teams can establish a private or federated marketplace to standardize AI behavior across projects, while the full REST API and CLI tool enable automation and CI/CD integration. Deploy with Docker Compose on your own infrastructure, or use the hosted instance at lynxprompt.com.

How to Use

[{"step":"Define the automation task","action":"Identify a repetitive workflow (e.g., code deployment, data processing) and list the tools, inputs, and outputs required. Use LynxPrompt’s WebUI to draft a `CLAUDE.md` file with these details.","tip":"Start with a small, well-defined task (e.g., 'Run unit tests on push to main branch') before scaling to complex workflows."},{"step":"Configure error handling and sharing","action":"Add error handling rules (e.g., retries, notifications) and define how the config will be shared (e.g., team-wide via `AGENTS.md`). Use LynxPrompt’s CLI to test sharing: `lynxprompt share --file CLAUDE.md --team all`.","tip":"Include a rollback plan for destructive actions (e.g., database migrations) to avoid irreversible mistakes."},{"step":"Test the automation","action":"Run the config via CLI: `lynxprompt run --config CLAUDE.md --input '{\"param\": \"value\"}'`. Monitor logs for errors and adjust the config as needed.","tip":"Use `--dry-run` flag to preview actions without executing them: `lynxprompt run --config CLAUDE.md --dry-run`."},{"step":"Integrate with AI agents","action":"Update `AGENTS.md` with the new automation steps so AI agents (e.g., Claude) can trigger it. Example: 'To deploy the API, run `lynxprompt run --config aws-deploy.md`.'","tip":"Document the automation in `AGENTS.md` with clear triggers (e.g., 'Deploy on PR merge to main') to make it discoverable."},{"step":"Monitor and iterate","action":"Track automation success/failure rates in LynxPrompt’s dashboard. Refine configs based on logs and team feedback.","tip":"Set up alerts for failures (e.g., via Slack or email) to ensure timely intervention."}]

Use Cases

Standardize AI coding assistant configurations across engineering teams

Build an internal marketplace of reusable AI configuration blueprints

Generate and export configs for multiple AI tools from a single source

Automate AI config deployment in CI/CD pipelines via REST API

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/GeiserX/LynxPrompt

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Generate a LynxPrompt configuration file for automating [TASK] in [PROJECT_NAME]. The config should include: 1) A clear task description, 2) Required tools (e.g., Git, Python, API keys), 3) Input/output specifications, 4) Error handling rules, and 5) A sharing policy for the team. Save the output as a CLAUDE.md file and include a comment explaining how to trigger the automation via CLI. Example: 'Generate a LynxPrompt config for deploying a Python API to AWS using Terraform. Include steps for linting, testing, and rollback on failure.'

Example Output

```markdown
# LynxPrompt Config: AWS Python API Deployment
**Task**: Deploy a Python FastAPI application to AWS ECS with Terraform, including CI/CD pipeline setup.

## Tools Required
- Terraform (v1.5+)
- AWS CLI (configured with `aws configure`)
- Docker (for containerization)
- GitHub Actions (for CI/CD)
- Python 3.10+
- `aws-ecs` Terraform provider

## Inputs
- `app_name`: Name of the FastAPI application (default: `my-api`)
- `aws_region`: AWS region (default: `us-east-1`)
- `github_repo`: GitHub repository URL (e.g., `https://github.com/acme/my-api`)

## Outputs
- ECS Cluster ARN
- Load Balancer DNS name
- CI/CD pipeline URL

## Steps
1. **Validate**: Run `terraform validate` to check syntax.
2. **Plan**: Execute `terraform plan -out=tfplan` to preview changes.
3. **Apply**: Run `terraform apply tfplan` to deploy infrastructure.
4. **Test**: Execute `curl $(terraform output -raw lb_dns)` to verify API health.
5. **Rollback**: If test fails, run `terraform destroy -auto-approve` to revert changes.

## Error Handling
- **Terraform errors**: Log to `terraform_errors.log` and notify Slack channel `#devops-alerts`.
- **AWS permissions**: Fail deployment if IAM role lacks `ecs:CreateCluster` permissions.
- **API health check**: Retry 3 times with 10s delay before marking deployment as failed.

## Sharing Policy
- Store config in `./lynxprompts/aws-deploy.md`.
- Share via LynxPrompt CLI: `lynxprompt share --file aws-deploy.md --team devops`.
- Update `AGENTS.md` with new deployment steps for AI agents.
```

**CLI Trigger**: 
```bash
lynxprompt run --config aws-deploy.md --input '{"app_name": "user-service", "aws_region": "eu-west-1"}'
```