20 AI Tricks
You Can Use Today

Instead of reading every email yourself, tell Claude to scan your entire inbox, identify which emails actually need your reply, draft responses for each one, and generate a dashboard showing everything at a glance. What used to take 30–45 minutes becomes a single command.

Walking into a meeting unprepared? Claude can pull your calendar, check your email history with each attendee, find relevant documents, and produce a one-page briefing with background on who you're meeting, what you discussed last time, your open action items, and suggested talking points.

Drop a folder of scanned PDF receipts onto your desktop. From your phone, tell Claude to turn them into a categorized expense report. It reads every receipt, extracts amounts and vendors, categorizes spending, and generates an interactive HTML report with charts and breakdowns.

Create an empty folder. Tell Claude to set itself up as your personal knowledge assistant with an orchestrator agent, a local SQLite database for structured data, and a simple HTML interface to browse it. You now own 100% of your data, it works offline, and you can swap AI models anytime.

Dump a pile of random scanned PDFs, invoices, contracts, and images into a folder. Claude reads every one, creates a logical folder structure (by year, type, or topic), renames files meaningfully, and indexes everything in your database so you can search it later.

Claude Dispatch lets you fire off multiple independent tasks from your phone. Each runs as a separate parallel agent on your desktop. While you're at the gym, one agent sweeps your inbox, another researches a topic, a third preps your meeting, and a fourth generates a presentation.

Claude's native connectors cover about 38 apps. But using Zapier's MCP server, you can extend that to 8,000+ apps with 30,000+ actions. Connect to School, Airtable, HubSpot, Stripe, or any app Zapier supports — then trigger actions from Claude or even from your phone via Dispatch.

Paperclip is a free open-source tool that lets you create a company of AI agents with an org chart, budget tracking, a ticketing system, and role-based delegation. You act as the "board" — you set high-level goals and the CEO agent figures out who to hire and what to delegate.

Instead of building a company from scratch, import a proven team template. The Paperclip community has pre-built templates including a marketing agency, a game studio (48 agents!), a scientific research lab, and Gary Tan's "G-Stack" with CEO/CTO/engineers. Each comes with agents, skills, and knowledge pre-configured.

Agents can wake up on a schedule (every 4, 8, or 12 hours) and work autonomously. On each heartbeat, they read their memory, check assignments, review tasks, and continue working. Think of it like the movie Memento — they wake up capable but with no memory, so they read their "tattoos" (instruction files) to know what to do.

Skills are pre-written instruction sets that make AI agents experts in specific domains. The skills.sh marketplace has hundreds of free skills for front-end design, web guidelines, security audits, content strategy, and more. Install them with a single URL paste.

Instead of manually scrolling Reddit and Twitter for content ideas, a single command scans the last 30 days across Reddit, X/Twitter, BlueSky, YouTube, TikTok, Instagram, Hacker News, and Polymarket. It finds what people in your niche are actually debating, recommending, and getting angry about.

A multi-step pipeline that researches a topic, creates a detailed lead magnet document (1,500 words, sixth-grade reading level, narrative-driven), pushes it to Notion via MCP, and drafts three LinkedIn post variations to promote it: a contrarian take, a pain-first hook, and a results-led hook.

Claude accumulates memory of your conversations over time. Ask it to create tone-of-voice guidelines based on what it's learned about how you communicate. This becomes a reusable markdown file that every AI skill references, ensuring all content sounds like you.

When AI extracts data from documents, it guesses instead of admitting it doesn't know. Fix this by explicitly giving AI permission to leave fields blank and requiring an explanation for each blank. You only review blanks instead of everything.

AI defaults to guessing because it treats a wrong answer the same as a blank answer. Add a single line that changes the incentive: tell AI that a wrong answer costs 3x more than saying "I don't know." This dramatically reduces hallucinations. Like telling a new hire: "If you give me wrong info it costs the company 3x more than just saying you'll check."

That's it. One line. Add it to any extraction, analysis, or review prompt. Combine with Trick #15 for maximum effect.

Even after you tell AI to only extract from the document, it will start inferring on complex tasks. This safety net catches it. Require a "Source" column on every field with two possible values: Extracted (word-for-word from the document) or Inferred (derived from context). For inferred values, require a one-sentence evidence explanation. Now you only need to review the inferred fields.

The complete anti-hallucination stack: Combine Tricks #15 + #16 + #17 in every data extraction prompt. You'll go from checking everything to only checking blanks and inferred fields.

Before Claude does anything complex, switch to Plan Mode. Instead of executing immediately, it asks clarifying questions, shows you a step-by-step plan, and waits for your approval. This prevents expensive mistakes, runaway agents, and "overkill" implementations. In Claude Code, press Shift+Tab to toggle. In Cowork, add "plan first, don't execute yet" to your prompt.

Connect Claude to TradingView via an MCP server. Describe a trading strategy in plain English from your phone. Claude writes the Python backtest, runs it against years of data, tests multiple moving average types (SMA, EMA, VWMA, HMA, DEMA, TEMA, WMA), optimizes parameters, and ranks everything by profit factor — while you're having drinks with friends.

Firecrawl turns any website into clean structured data with a single API call. The business model: pick a niche, scrape valuable data, wrap an LLM around it, and sell the output as reports, alerts, or enriched datasets. Examples: SEO audits for dentists ($200/month), sneaker resale price alerts ($50/month), or lead enrichment ($500/batch at $2 cost). One API call replaces a thousand lines of custom scraping code.