The Claude Code skills I use every day as a founder

Seven AI skills replaced seven hires. Total cost: $5K-12K/year.

No senior engineer for code review. No copywriter for brand consistency. No SEO specialist for content optimization. No analyst for portfolio tracking. Instead, Claude Code skills — reusable AI-powered workflows that run in the terminal with a slash command. Together, they replace the work that used to require dedicated headcount.

Key takeaways:

Seven Claude Code skills replace seven hires: code review, brand voice, content optimization, video summaries, flag theory analysis, portfolio tracking, idea evaluation
Total cost: $5K-12K/year. All open source except portfolio analysis.
The stack compounds — skills compose together (write → tone-of-voice → ai-rank)
Each skill saves 30 minutes to several hours per use, used daily across every function
Install: git clone into ~/.claude/skills/ and run with a slash command

These aren’t demo projects. They’re production tools I’ve been using daily while running a 7-figure bootstrapped SaaS with a team of five. Most are open source. Here’s what each one does and why it exists.

/review-staged — Code review without a senior engineer

What it does: Runs two independent AI reviewers in parallel on your staged git changes — one focused on security and bugs, the other on architecture and code quality. A code simplification pass identifies unnecessary complexity. Findings are cross-referenced to filter false positives, then presented as a consolidated report with a PASS/FAIL/DISCUSS verdict.

Why it exists: We’re two co-founders writing all the code. No senior engineer reviews our work. Before this skill, code review meant reviewing each other’s PRs — which we still do, but now the AI catches the obvious issues first. Security vulnerabilities, performance problems, dead code — the skill flags them before a human ever looks.

The reality: It’s not a replacement for human code review. It’s a first pass that elevates the quality of what reaches human review. The cross-referencing between the two agents is key — it eliminates the false positives that make single-agent reviews noisy and annoying.

There’s a deeper reason this skill matters. An agent will introduce the same code smell a hundred times and never notice. It doesn’t learn from the last run. It doesn’t remember that you already have a utility for date formatting, so it writes another one. Each mistake is small. But small mistakes at machine speed produce legacy code in weeks, not years. /review-staged is the gate that catches this drift before it compounds. Mario Zechner wrote about this problem well — it’s becoming the central challenge of working with agents.

Open source: github.com/entpnomad/review-staged

/tone-of-voice — Brand consistency across everything

What it does: Defines and enforces a specific writing voice, tone, and vocabulary across all content — blog posts, social media, emails, landing pages, GitHub READMEs. It knows which words to use, which to avoid, how to open a paragraph, and when bold is earning its place.

Why it exists: I write in four languages (EN, ES, FR, IT). Content goes out across multiple channels. Without a system, the voice drifts. Some posts sound corporate. Others sound casual. The voice skill is the system — it’s the editorial standard that Claude applies automatically.

How I actually use it: Every blog post on this site goes through /tone-of-voice during writing. Not as an afterthought — as part of the drafting process. The skill knows the rules: contrarian, specific, experience-first, anti-fluff. It enforces them line by line.

Open source: github.com/entpnomad/tone-of-voice

/ai-rank — Content optimization for LLMs and agents

What it does: Two frameworks in one. The LLM framework optimizes content for answer engines (ChatGPT, Claude, Perplexity) — answer-first intros, intent-matched headings, quotable blocks. The AGENT framework optimizes for autonomous AI agents — structured data, machine-readable facts, discovery files.

Why it exists: The next generation of content discovery is machine-driven. If an LLM can’t extract your key claims, it won’t cite you. If an AI agent can’t parse your page, it won’t recommend you. This skill audits and rewrites content for both audiences simultaneously.

How I actually use it: Every post on this site runs through /ai-rank before publishing. The Answer Engine Optimization post explains the mechanics behind it. It generates schema suggestions, writes FAQ sections with question-format headings, and outputs an “extraction preview” — what an LLM would actually quote from your page. If the preview is empty, the page is invisible.

Open source: github.com/entpnomad/ai-rank

/youtube-summary — Signal from noise

What it does: Fetches the transcript from a YouTube video and produces a comprehensive summary — key arguments, specific data points, actionable insights, and timestamps for the parts worth watching.

Why it exists: A 2-hour podcast has 15 minutes of signal. I don’t have time to watch the full thing, but I need to know what was said. This skill extracts the signal and throws away the noise.

How I actually use it: Competitive intelligence, market research, and staying current. When YC publishes a new episode or a competitor does a podcast, I run the transcript through this skill and get the key points in 5 minutes. The Make Something Agents Want post was partly informed by a YouTube summary of the YC Lightcone episode.

Open source: github.com/entpnomad/youtube-summary

/flag-theory — International setup analysis

What it does: Analyzes your international setup across the 7 flags — citizenship, tax residency, business structure, banking, physical assets, digital security, and digital assets. Produces a scored optimization report with risk ratings.

Why it exists: My international structure isn’t set-it-and-forget-it. Countries change their rules. CRS 2.0 just reshuffled the deck. Exit taxes change. CBI programs open and close. When I’m evaluating a change — a new residency, a restructured company, a different banking jurisdiction — this skill runs the analysis before I spend money on a lawyer.

How I actually use it: Before any structural change, I run the analysis. It scores each flag, identifies weaknesses, and highlights risks. I used it extensively when planning my Dominican citizenship and evaluating the Golden Visa program.

Open source: github.com/entpnomad/flag-theory

/portfolio-analysis — Investment tracking across asset classes

What it does: Tracks investments across all asset classes — brokerage accounts, crypto, precious metals, cash positions, alternatives — and produces allocation reports and analysis.

Why it exists: Spreadsheets don’t scale when you have assets across multiple jurisdictions, currencies, and asset classes. This skill consolidates everything into a single view and runs analysis on allocation, concentration risk, and rebalancing needs.

How I actually use it: Regularly. No details, no numbers — just the fact that it exists and eliminates the overhead of manually tracking a multi-asset, multi-jurisdiction portfolio.

Private: This one stays private. The logic is too specific to my setup to be useful as a generic tool.

/bootstrapper-toolkit — Business idea evaluation

What it does: 20 AI-powered skills for evaluating business ideas with bootstrapper rigor. 10 research agents run in parallel — analyzing competitors across 16 dimensions, sizing markets bottom-up, stress-testing unit economics, scoring founder-business fit.

Why it exists: I used to run this analysis manually. It took days per idea. The evaluation framework is based on every mistake I’ve made and every question I wish I’d asked before building things nobody wanted.

How I actually use it: When a new idea surfaces — mine or someone else’s — I run /analyze-idea. In 30 minutes it goes deeper than most paid consultants. The scoring is opinionated: distribution and problem validation are weighted heaviest because those are what actually kill bootstrapped businesses.

Open source: github.com/entpnomad/bootstrapper-toolkit

The stack, not the tool

Individual AI tools are useful. A stack of AI tools that work together is the actual force multiplier.

The skills aren’t isolated. They compose:

Write a blog post → /tone-of-voice for voice → /ai-rank for optimization
Evaluate an idea → /bootstrapper-toolkit for analysis → /flag-theory for international structure
Ship a feature → code it with Claude → /review-staged for quality check
Research a topic → /youtube-summary for video intel → web research → synthesize

Each skill saves 30 minutes to several hours per use. Used daily across every function of the business, they collectively replace 3-5 full-time hires in output. Not in judgment. Not in relationships. Not in institutional knowledge. But in output — and for a bootstrapped company, output is what you’re buying when you hire.

How to start

All the open-source skills install the same way:

git clone https://github.com/entpnomad/[skill-name].git ~/.claude/skills/[skill-name]

Then run the slash command in Claude Code. That’s it. No configuration. No API keys (beyond Claude Code itself). No setup wizard.

Start with /review-staged if you write code. Start with /tone-of-voice if you write content. Start with /ai-rank if you care about being found by AI. Build from there.

FAQ

What is a Claude Code skill?

A reusable AI-powered workflow that runs inside Claude Code (Anthropic’s CLI tool). Skills are markdown files that define a specific task, methodology, and output format. You invoke them with a slash command — /review-staged, /tone-of-voice, etc.

Do I need Claude Code to use these?

Yes. The skills are designed for Claude Code. They won’t work in ChatGPT, the Claude web interface, or other tools. Claude Code is Anthropic’s terminal-based AI coding assistant.

Can I customize these for my own project?

Absolutely — that’s the point. Fork the repo, edit the SKILL.md file, and adapt the instructions to your specific needs. The tone-of-voice skill, for example, is entirely customizable to your brand voice.

How much does this cost?

The skills are free and open source. Claude Code requires a Claude API subscription. The total AI tooling cost for our team is $5K-12K/year — less than one month of one employee’s salary.

Every skill mentioned in this post is on GitHub. Clone them, break them, rebuild them. The best AI workflow is the one you adapted to your own process.