kubesearch

Name: kubesearch
Author: ionfury

github.com/ionfuryCloud & DevOps2544 installsUpdated today

About kubesearch

kubesearch is a research skill for finding real Helm configurations in homelab repositories through kubesearch.dev. It helps Kubernetes homelab operators compare relevant releases and inspect linked GitHub HelmRelease files using recency, stars, chart versions, and infrastructure goals as selection criteria.

Key features

01Search Helm chart releases
02Find repositories linked to releases
03Fetch raw HelmRelease configurations
04Filter examples by recency and stars
05Compare chart versions and infrastructure goals

Use cases

01Research a Helm chart before configuring it
02Find homelab examples for Flux or Talos
03Compare configurations from related repositories

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/ionfury/homelab --skill kubesearch

After installing, type/kubesearchin Claude Code to confirm it loaded.

Bundled filesreferences/output-format.mdtests.yaml

Details

From the project README

KubeSearch - Homelab Helm Configuration Research

Search kubesearch.dev to find real-world Helm configurations from other homelab repositories.

Workflow

Step 1 — Find chart: WebFetch https://kubesearch.dev/?search=<chart-name> → list matching releases with registry paths.

Step 2 — Get repository links: Convert registry path to URL format (replace / with -), then WebFetch https://kubesearch.dev/hr/<url-path> → list repositories with direct GitHub links to HelmRelease files.

Examples of the URL conversion:

ghcr.io/grafana-helm-charts/grafana → ghcr.io-grafana-helm-charts-grafana
charts.longhorn.io/longhorn → charts.longhorn.io-longhorn

Step 3 — Fetch configs: Convert GitHub blob URLs to raw URLs, then fetch 3-5 in parallel:

Blob: github.com/<owner>/<repo>/blob/<branch>/<path>
Raw: raw.githubusercontent.com/<owner>/<repo>/<branch>/<path>

Selection criteria: recent activity (within 6 months), higher star count, similar chart version, similar infrastructure goals (bare-metal, GitOps, Talos).

Common Homelab Repositories

Repository	Focus
`onedr0p/home-ops`	Flux GitOps, extensive automation
`bjw-s/home-ops`	App-template patterns
`buroa/k8s-gitops`	Talos + Flux
`mirceanton/home-ops`	Well-documented configs

See references/output-format.md for the standard output structure when presenting findings.

Comments

Markdown supported

Details

Author: ionfury
Repository: ionfury/homelab
Category: Cloud & DevOps
Installs: 44
GitHub stars: 25

Report

More skills

All skills

lark-apps

open.feishu.cn

732.0k

Develop and host Miaoda (Spark/Miaoda) apps: app creation, local full-stack development, cloud generation and iteration, creative design (UI mockups / interactive prototypes / wireframes / landing pages / dashboards / slide decks / visual exploration), integration of AI-related capabilities, Feishu platform capabilities, or other external capabilities; log/Trace/monitoring metric/PV/UV queries; environment variable management; app collaborators and collaboration permissions; app roles and members; and automation triggers (scheduled / record changes / Webhook / Feishu approvals). Use when users want to develop or create a system, tool, platform, or app; develop locally or in the cloud, modify, deploy, publish, launch, or get a shareable link; create a page or website with HTML and deploy it to Miaoda; design, create a mockup, prototype, wireframe, PPT, deck, or visual exploration; or mention Miaoda/Spark/Miaoda apps (links like `/app/app_xxx` or `/page/<token>`, runtime domains like *.aiforce.cloud), app databases, app file storage, open API Keys, visibility, app collaborators/developer permissions, app roles/role members, production logs, API request volume, error volume, latency, traffic, environment variables, or setting up automation tasks, scheduled triggers, or approval-triggered automation for Miaoda apps. Not for regular cloud drive file uploads (lark-drive), Feishu document editing (lark-doc), or native slide creation (lark-slides).

azure-prepare

github.com/microsoft

571.7k

Prepare azd-based Azure projects for deployment: generates azure.yaml, infrastructure (Bicep/Terraform), and Dockerfiles for the Azure Developer CLI (azd) workflow. USE ONLY when the user explicitly wants to use azd as the deployment tool, or the project already has an azure.yaml file. DO NOT USE FOR: non-azd deployments, Python App Service code-only deploys (use python-appservice-deploy), or cross-cloud migration (use azure-cloud-migrate). WHEN: prepare app for azd, create azure.yaml, set up azd infrastructure, modernize app for Azure with azd, deploy with azd, function app, timer trigger, service bus trigger, event-driven function, managed identity, generate Bicep, generate Terraform, create and deploy to Azure.

wizard

github.com/mattpocock

571.5k

Generate an interactive bash wizard that walks a human through steps only they can perform. Use when provisioning infrastructure, setting up credentials or CI secrets, walking an unfamiliar third-party dashboard, or running a one-off migration or cutover. Don't invoke this for steps the agent can perform itself.

azure-ai

github.com/microsoft

571.2k

Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.