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name	description
osgrep	Run lightweight semantic grep over the current repository using the osgrep CLI (natural-language search, JSON output, hot daemon).

Note: path should be ~/.factory/skills/osgrep/SKILL.md

Install osgrep

Install / Warm Up

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#!/usr/bin/env sh
set -e

# Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

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#--------------------------------------------------------------------------
# Configuration
#--------------------------------------------------------------------------

# Use Vi mode
setw -g mode-keys vi

# Increase scrollback buffer size
set -g history-limit 10000

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$ curl --help
Usage: curl [options...] <url>
     --abstract-unix-socket <path> Connect via abstract Unix domain socket
     --alt-svc <file name> Enable alt-svc with this cache file
     --anyauth       Pick any authentication method
 -a, --append        Append to target file when uploading
     --basic         Use HTTP Basic Authentication
     --cacert <file> CA certificate to verify peer against
     --capath <dir>  CA directory to verify peer against
 -E, --cert <certificate[:password]> Client certificate file and password

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user nginx;
worker_processes auto;
worker_cpu_affinity auto;
# Change the default thread pool settings
thread_pool default threads=2 max_queue=16384;

error_log /var/log/nginx/error.log notice;
pid /var/run/nginx.pid;

events {

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How to Build & Run Dockerized ArogCD CLI

This quick guide shows you how to build a Dockerized argocd CLI. The advantage of this setup is that it's platform-independent. Also, I personally found the Linux installation annoying and buggy, and this proves to be a much more streamlined approach. Interestingly enough, the MacOS and the Windows installers were way less finnicky.

Dockerfile

Save the text below to Dockerfile.argocd in a folder called docker

FROM argoproj/argocd:v1.7.6 as builder

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.
├── books
│   ├── handlers.go
│   └── models.go
├── config
│   └── db.go
└── main.go

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{
  "customModes": [
    {
      "slug": "sparc",
      "name": "⚡️ SPARC Orchestrator",
      "roleDefinition": "You are SPARC, the orchestrator of complex workflows. You break down large objectives into delegated subtasks aligned to the SPARC methodology. You ensure secure, modular, testable, and maintainable delivery using the appropriate specialist modes.",
      "customInstructions": "Follow SPARC:\n\n1. Specification: Clarify objectives and scope. Never allow hard-coded env vars.\n2. Pseudocode: Request high-level logic with TDD anchors.\n3. Architecture: Ensure extensible system diagrams and service boundaries.\n4. Refinement: Use TDD, debugging, security, and optimization flows.\n5. Completion: Integrate, document, and monitor for continuous improvement.\n\nUse `new_task` to assign:\n- spec-pseudocode\n- architect\n- code\n- tdd\n- debug\n- security-review\n- docs-writer\n- integration\n- post-deployment-monitoring-mode\n- refinement-optimization-mode\n\nValidate:\n✅ Files < 500 lines\n✅ No hard-coded

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This is an LLM-assisted workflow for creating a product requirement document using LLM assistance for task completion. 
It keeps track of inputs for the template and works with the user to acquire them, finally generating a completed PRD 
prompt when all slots are addressed.


credit:  Ian Nuttall - https://gist.github.com/iannuttall/f3d425ad5610923a32397a687758ebf2



**System-Prompt for Facilitating Chat-Based PRD Creation**

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{
  "customModes": [
    {
      "slug": "code-my-lean-expirimental",
      "name": "Code (My Lean Expirimental)",
      "roleDefinition": "You are Roo, a highly skilled software engineer with extensive knowledge in many programming languages, frameworks, design patterns, and best practices.",
      "groups": [
        "read",
        "edit",
        "browser",