# Learning & R&D > Daily research agents, deep-dive briefs, competitive intel, and the agent-as-analyst patterns. The operator who out-learns the competition compounds faster than any other lever in the business. The bottleneck isn't access to information — there's too much. The bottleneck is filtering and absorbing. That's an agent's job, not yours. A few principles: You learn for the business, not for fun. Filter everything against your current actual questions. Compound what you learn into skills, not into notes. A note you took three months ago doesn't ship anything. A skill does. Cron the input, not the output. The agent reads daily. You read weekly. Don't drown yourself in raw feed. One niche-deep beats five-niches-shallow. Pick the 2-3 areas where the agent goes hard, and let everything else stay surface-level. ## 1. Daily Research Agent ### Tip 1.1 — Morning niche brief **What it does:** Every morning your agent reads the day's signal from your niche — RSS feeds, X, LinkedIn, podcasts that dropped, papers that got attention — ranks it by relevance to your specific current questions, and sends you a 5-minute morning brief. Why it wins: Most operators consume content in the doomscroll mode: random, unfiltered, addictive, unproductive. A morning brief replaces 60-90 minutes of scrolling with 5 minutes of triage. The agent does the ranking work. Tools: RSS feeds (per Marketing Tip 1.5), platform scrapers, podcast feed parsers, your "current questions" doc. How to wire it: 1. Maintain a current-questions.md doc: 5-10 specific questions you're actively trying to answer (e.g., "what's the best way to handle multi-agent orchestration in production?", "which content formats are working for B2B SaaS in 2026?"). 2. Sources: RSS feeds, top accounts to follow on X / LinkedIn, niche newsletters, podcast feeds, key Substacks. 3. Cron 5am: agent pulls everything published in last 24h. 4. Scores each item: relevance to current questions (0-5), novelty (0-3), credibility (0-2). 5. Top 5-10 make the brief. For each: 2-line summary + why-it-matters + a "your follow-up action" if the agent has one. 6. Send by 7am. You read over coffee. Example prompt to your agent: Every day at 5am: read all sources in learning-sources.json (RSS, X accounts, LinkedIn accounts, podcasts, Substacks). Pull everything from the last 24h. Score against current-questions.md : relevance 0-5, novelty 0-3, credibility 0-2. Pick the top 5-10. For each: 2-line summary, 1-line why-it-matters, 1-line follow-up action if any (download paper, listen to podcast clip, save tweet, ping someone). Send me on Telegram by 7am. Watch out for: Without current-questions.md , the agent reverts to "interesting" which is useless. Update questions weekly. Some signal is in places you can't easily scrape (private Slacks, Skool, Discord). Add a "manually drop in this folder" rail for those. The brief itself can bloat. Cap at 10 items. If 11+ are scoring high, the threshold is too low. Skill file: linkedin-post-writer (RSS pattern), search, _auto-competitor-intel ### Tip 1.2 — Paper / blog / podcast deep-read pipeline **What it does:** When you flag an item from the morning brief for deeper read, the agent runs the full deep-read: transcribes (if audio/video), extracts key claims, fact-checks 2-3 critical ones, distills the operator-relevant takeaways, and adds the result to your notes. Why it wins: "I'll read it later" is where 95% of saved content dies. The agent reads it now, gives you the structured digest, and you decide if it's worth your time on the full thing. Tools: Whisper for audio/video transcription (the free open-source model is fine), your reader of choice (Readwise, Reflect, plain Markdown), your note system. How to wire it: 1. From the morning brief, you tag items "deep" or save to a deep-read folder. 2. Per item, agent runs: fetch full text or transcribe audio/video. 3. Reads end-to-end. Extracts: thesis, 3-5 key claims, supporting evidence quality (your call), counter-arguments missing, operator-relevant takeaways for your niche. 4. Saves the digest to notes/deep-reads/YYYY-MM-DD-.md and indexes for retrieval. 5. Daily summary of new deep-reads waiting for you. Example prompt to your agent: When I tag an item deep in my morning brief, queue it for deep-read. Fetch full text or transcribe audio/video with Whisper. Extract: thesis, 3-5 key claims with quoted support, evidence quality assessment, counter-arguments the piece skips, operatorrelevant takeaways for my niche. Save to notes/deep-reads/YYYY-MM-DD-.md. Update my central notes index. Daily evening digest of new deep-reads waiting for me. Watch out for: Long pieces produce long summaries. Force a hard cap: 500 words total per digest. Always cite. Every claim should link back to the original passage so you can verify. Don't index garbage. Cull the deep-read folder monthly — anything you haven't referenced in 60 days probably wasn't worth it. Skill file: _auto-memory-search, content-repurposer (transcript pattern) ## 2. Weekly Digest ### Tip 2.1 — "What changed this week in " **What it does:** Every Sunday, your agent synthesizes the week's daily briefs into a single themed digest: the 3-5 shifts that happened in your niche this week, the data behind them, the implications for your business, and the 1-2 things you should consider doing differently next week. Why it wins: Day-to-day signal looks random. Week-over-week patterns are where the real intelligence lives. The agent has the full 7-day view; you don't. Tools: Outputs from Tip 1.1 (daily briefs), your current-questions.md , your note index. How to wire it: 1. Sunday 6pm cron: agent pulls last 7 daily briefs. 2. Identifies themes — what topics came up multiple times, what shifted in tone, what's new vs last week. 3. Cross-references with your current-questions.md to weight relevance. 4. Drafts the digest: 3-5 themes, 2-3 sentences each + a data point, plus 1-2 "action implications." 5. Saves to weekly-digests/YYYY-WW.md and sends Sunday evening. **Example prompt to your agent:** ``` Every Sunday at 6pm: read this week's 7 daily briefs and the items I tagged deep (Tip 1.2). Identify 3-5 themes — multi-mention topics, shifts in tone, new vs last week. Weight by my current-questions.md . Draft a digest: each theme gets 2-3 sentences + a concrete data point + 1 sentence on the business implication for me. End with 1-2 "consider doing differently" action items for next week. Save to weeklydigests/YYYY-WW.md . Send me Sunday 7pm. Watch out for: Themes drift if you don't update your current questions. Force the agent to highlight when it can't find theme overlap — that's a sign the questions are stale. "Trends" can be confirmation bias. Force the agent to surface counter-evidence too. Weekly digests can replace too much daily reading. Keep both. Skill file: _auto-memory-search, content-engine ``` ## 3. Competitor Product Change Monitoring ### Tip 3.1 — Competitor changelog watcher **What it does:** Your agent monitors competitors' public surfaces — changelog pages, status pages, pricing pages, key product pages, their blog, their release notes — for changes. Anything that moves gets diffed and reported. Why it wins: Most operators "watch competitors" by occasionally browsing. They miss the pricing change, the new feature drop, the quiet repositioning. The agent diffs daily and tells you what shifted. Tools: Web fetcher with diff capability, optionally agent-browser for JS-heavy pages, a competitors.json config. How to wire it: 1. Per competitor, list the surfaces to watch: changelog URL, pricing page URL, key feature pages, blog feed. 2. Daily cron: agent fetches each URL and stores the rendered text. 3. Diffs against yesterday's version. 4. For any meaningful change (not just CSS noise — actual content), agent summarizes: what changed, your read on why, implications for you. 5. Weekly digest of all changes plus a "what they might be telegraphing strategically" synthesis. **Example prompt to your agent:** ``` Build competitor-monitor skill. Watch surfaces in competitors.json (per competitor: changelog URL, pricing, key features, blog). Daily at 3pm: fetch each, diff against yesterday, filter out CSS-only / boilerplate noise. For each meaningful diff, summarize: what changed, your read on why, what it implies for me. Save to competitor-watch//YYYY-MM-DD.md . Weekly Friday: cross-competitor synthesis — what's the field collectively telegraphing. Watch out for: JS-heavy pages need a real browser. agent-browser works but is slower. "Pricing change" is the highest-signal event. Surface those immediately, not just in the weekly digest. Some changes are A/B tests. Don't over-react to a one-day change that reverts. Skill file: agent-browser, _auto-competitor-intel, site-analytics ``` ### Tip 3.2 — Competitor team & hiring signal **What it does:** Your agent monitors competitors' LinkedIn careers pages and team rosters. New hires (especially senior) signal direction. Hiring slowdowns signal trouble. Specific role openings signal where they're investing. Why it wins: Hiring is a leading indicator of strategy. Three senior MLE postings = they're building a model team. A new VP of Sales = they're going upmarket. Most operators don't see this because they don't look. The agent does. Tools: LinkedIn scraper for company pages, their public careers page, your competitor config. How to wire it: 1. Per competitor: company LinkedIn URL, careers page URL. 2. Weekly cron: pull current job openings and headcount-by-function from LinkedIn. 3. Diff against last week. Note: new roles posted, roles filled (no longer listed), senior arrivals announced on LinkedIn. 4. Per change, agent writes a 2-line read. 5. Monthly: a "where competitor X is investing" synthesis. Example prompt to your agent: Weekly Monday morning: per competitor in competitors.json , pull current job openings (LinkedIn + careers page) and recent senior hires (from LinkedIn employee feed). Diff vs last week. Note new postings, filled roles, senior arrivals. Write a 2-line read per change. Monthly: synthesize across all competitors — who's investing where, who's growing, who looks stuck. Watch out for: Ghost job postings. Some companies leave roles up for months. Track time-to-fill to spot fakes. Mass layoff announcements are louder than the slow grind. Don't miss the slow grind. A new VP arriving doesn't always mean what you think. Watch what they actually ship over 60 days. Skill file: linkedin-scraper, _auto-competitor-intel ## 4. Skill-Building Loop The most under-used pattern in this whole cookbook: when you learn something new and useful, have your agent codify it AS a skill so it doesn't get lost. This is how you compound learning into capability instead of into notes that decay. ### Tip 4.1 — Build-your-own-skill from a learning session **What it does:** You spend an hour deeply learning something — a new technique, a new tool, a new framework. Instead of writing a Notion doc nobody reads, you debrief with the agent and it writes the learning up as an executable skill: when to use it, the steps, the gotchas, the example commands. Why it wins: Three months from now you won't remember the technique. The agent will. And when the relevant trigger fires, the agent loads the skill automatically. Learning that compounds. Tools: Your agent's skill creator capability, your skills directory. How to wire it: 1. After a learning session (paper read, tool experimented with, technique tried), prompt the agent to interview you: what did you learn, what was the trigger / problem, what's the step-by-step, what are the gotchas, when should this fire next time. 2. Agent drafts the skill in your skill format. 3. You edit. Save to skills. 4. Next time the trigger condition fires (in any agent session), the skill loads. Example prompt to your agent: Interview me about [thing I just learned]. Ask: what problem does it solve, what's the trigger to use it, the step-by-step, the gotchas, what tools are needed, an example that works. Then draft a skill in my standard format at skills//SKILL.md . Use a clear "use when" trigger description. I'll edit. Once saved, this skill loads whenever the trigger matches. Watch out for: Don't skill-ify everything. If you learned a one-off fact, that's a memory, not a skill. A skill should have a clear trigger ("use when scoping a new SEO project"). Vague triggers mean it never loads. Re-validate skills quarterly. Some go stale as tools / techniques evolve. Skill file: voice-skill-template (template pattern), script-polish (build-your-own-skill pattern) ### Tip 4.2 — The "explain this back to me" pattern for retention **What it does:** After every deep-read (Tip 1.2) or learning session (Tip 4.1), the agent runs an "explain this back to me" pass: it generates 3-5 Socratic-style questions about the material and quizzes you. Your weak answers identify gaps; the agent re-explains those parts. Why it wins: Reading without testing produces shallow retention. Active recall is what makes learning stick. The agent runs the recall loop without you having to set up a flashcard system. Tools: Your notes / digests / skills. How to wire it: 1. After a deep-read or skill is saved, agent generates 3-5 questions covering the material. 2. The next morning (or whenever you have 5 min), the agent quizzes you on Telegram. 3. You answer in plain text. Agent grades, surfaces your weak spots, re-explains them with the original source quoted. 4. Items you're shaky on come back 1 week, 1 month, 3 months later. Example prompt to your agent: After any deep-read or new skill is saved, generate 3-5 Socratic-style questions. The next morning, quiz me on Telegram (one item per day, max 5 questions). Grade my answers — partial credit ok. For weak answers, re-explain with the original quoted. Schedule items I'm shaky on for spaced repetition: 1 week, 1 month, 3 months. Track my "mastery" per topic. Watch out for: Don't ask trivial-recall questions. Force "explain why," "when wouldn't you use this," "what's the failure mode." The first week feels slow. Stick with it 30 days before judging. Drop topics you're confident on. Don't quiz yourself forever on basics. Skill file: _auto-memory-search ### How it all stacks The R&D stack runs daily, weekly, and on-demand. Daily gets you the signal. Weekly synthesizes the patterns. Skill-building converts learning into permanent capability. Competitor monitoring keeps you honest about what's actually moving in the market. Install order: 1. Morning brief (Tip 1.1). Lowest-cost install, daily value from day one. 2. Deep-read pipeline (Tip 1.2). Pair with 1.1 once you have items worth deep-reading. 3. Skill-build loop (Tip 4.1). This is the one most people skip and most regret skipping. Install early. 4. Weekly digest (Tip 2.1). Once you have 2-3 weeks of daily briefs to draw from. 5. Competitor product monitor (Tip 3.1). As soon as you have 3-5 named competitors worth watching. 6. Competitor hiring monitor (Tip 3.2). Once you're past 10 competitors or you're in a market where talent moves matter. 7. Spaced repetition quiz (Tip 4.2). Last to install — only useful once you have a real corpus to recall against. The R&D stack is the slowest to pay off and the highest-ceiling. The operators who install this in year one and let it run are unrecognizable by year three. Patience. ### Wild Real-World Examples This section is different. The rest of the cookbook is recipes — Marketing, Sales, Ops, each with steps you can follow this afternoon. This section is the hall of fame. End-to-end agent loops real people have built, where the shape of the loop is more interesting than any single step. Read these to recalibrate what "AI agent" can mean. Most operators still think of agents as chatbots or as that thing that drafts an email when you ask. The loops below find leads in satellite photos, redesign menus before a restaurant has ever heard of you, and DM a prospect a screenshot of the website you already built for them. They run while their owners sleep. Walk away with the principle, not the literal blueprint. If your niche isn't pools or restaurants, that's fine — every one of these loops has a transferable shape. A note on attribution: where the original builder is known, they're credited and linked. Where the loop has been built by several people in parallel and no single origin is clean, that's flagged. If you know who built one of these first and we miscredited it, let us know. ## 1. The Booking Accelerator Built and running daily inside a consultancy practice. Tactical recipe lives in the Sales section; the story is here. ### The loop A prospect books a call on your booking link. The webhook fires the second they hit confirm. Your agent reads the booking — name, email, time slot — and immediately spawns two parallel research jobs. One sweeps every inbox and channel you own for any prior thread with that person. The other runs a full deep-research X-ray: company site, LinkedIn, news, funding, tech stack, team, pain points, automation surface area. Both jobs return inside a couple of minutes. The agent synthesizes them into a call-prep dossier, then decides what to build. Default move: a targeted lead list scoped to the prospect's exact ICP, because if they're booking you they want to know you understand their business. Could be a competitor intel report. Could be a custom-built demo asset. Whatever's the highest-value thing it can build for this specific prospect in the next ten minutes. Then it writes the email — in your voice, signed by the agent, openly disclosing that it's the agent. Subject line: "See you on [date] :)". The pitch isn't "look how clever my agent is." The pitch is "the second you booked, my agent pulled up your company, found three things relevant to you, and built this for you. Here it is. I put it together so our call is useful from minute one." Drops the asset into the draft, you click send. By the time you're sitting at the call, the prospect has already opened the document, already seen real research about their company, already had a "oh, this is different" moment. The call starts in second gear instead of first. ### Why it's insane Most operators do the call-prep at 11pm the night before, or twenty minutes before the call while the kettle boils. Some don't do it at all. This loop does it the moment the booking lands and ships the prep to the prospect, which means it's not just preparing you — it's preselling them. The same fifteen minutes of work that you used to do for yourself is now frontloaded into the relationship and visible to the buyer. The reframe: every call now starts with the buyer thinking "they're more prepared than anyone I've talked to" before they've spoken a word. That's a 10x edge over every competitor in your inbox, automated. ### Tools they used Booking-tool webhook ( BOOKING_CREATED or equivalent), Claude Code as the harness, parallel subagent pattern for the email and research jobs, internal deep-research skill, internal pre-call-research skill, email-voice skill trained on your sent messages. The agent has access to read your inbox(es), your CRM, and a deliverables folder. The email API of your choice handles the send. ### The principle you can lift Whenever a high-intent event fires — booking, signup, paid trial start, demo request, downloaded asset — a clock starts in the buyer's head. They are most attentive to your follow-up in the first ten minutes after the trigger and least attentive thereafter. Most operators reply hours or days later. Build the loop that ships the most valuable artifact you could ship in those first ten minutes, automatically, while the prospect is still warm. The principle applies to any business with a high-intent inbound: SaaS demo requests, agency consultations, real estate showings, custom-quote requests, even Calendly bookings for therapy practices. ### Cross-link Tactical recipe lives in Sales Section 6 — Tip 6.1 (Booking Accelerator). The directly-liftable skill file is `booking-accelerator`. ## 2. The Site-Mockup Cold DM A loop running in production across several agencies and solo operators. Variants of this have been built by independent consultancies for client work and by several creators in the Liam Ottley AAA community. No single "origin" tweet — the pattern emerged when imageinput models became cheap enough. ### The loop Your agent picks a geography — a suburb, a city, a postcode bounding box — and a category. Independent cafés. Auto detailers. Med spas. Trades that quote on the phone. Whatever you sell to. It queries Google Places for every business in that geo × category combo, then filters for businesses with no website. Most local-services scanners stop there and hand you a list. This loop does not stop there. For each surviving lead, the agent runs three skills in sequence. First, research-thebusiness : scrapes whatever's findable — their Google profile, their Facebook page if they have one, their reviews, any local news mentions — and builds a one-paragraph read on what they do, who they serve, and what their brand feels like. Second, find-a-greatcompetitor-site : searches for a high-performing site in the same vertical, ideally aspirational rather than direct local competition, that has the structure and design you want to reference. Third, design-a-better-version : feeds the lead's research plus the competitor reference into an image-generation model with a prompt to build a home-page mockup tailored to the lead — their colors, their copy direction, their offer, their voice. Just the home page. As an image. No actual code, because tokens get expensive fast and the mockup is the only thing that matters in the cold DM. Then the agent picks the channel — Instagram DM, Facebook DM, sometimes SMS or email if a phone or email is on the listing — and sends the screenshot with a short message in your voice. Something like "hey, I already made you a site, here's a sneak peek, want to jump on a call so I can show it to you properly?" End-to-end, the loop is: scan → research → reference → generate → DM. One agent run, hundreds of personalized prospect touches, every one of them coming with a custom artifact. ### Why it's insane Cold outreach to local businesses has a floor on response rate because everyone's already seen a thousand generic "I'll build you a website for $X" messages. They scroll past. The unfair edge here is that the lead opens the DM and the first thing they see is their own business's homepage, designed for them, looking better than what they could imagine for themselves. The agent has done the work that a human salesperson would never do for a $500 prospect — because the agent doesn't care, the agent costs cents per lead, and the agent does it on every single message. The reply rates these operators see are in the high single digits to low double digits on a cold DM channel. For context: a generic cold email response rate hovers near 1-2%. This is a different game. ### Tools they used Google Places API for the scan, agent-browser or Scrapling for the SERP fallback when the Places API misses a business. The image generation is usually gpt-image-1 (OpenAI's API) or Gemini 2.5 Flash Image with image-input support so the agent can reference the competitor screenshot when generating. Instagram and Facebook DM delivery via the unofficial APIs or a service like ManyChat. The whole orchestration sits inside Claude Code or a similar agent harness with a skill per step. ### The principle you can lift The unfair advantage of a generative agent is that the marginal cost of personalization has collapsed to roughly zero. The play is: figure out what artifact would make your prospect say "wait, did they actually build this for me?" — and have the agent build that artifact for every prospect before the first message goes out. For pool builders it's a video of their backyard with a pool in it (see entry 4). For restaurants it's a redesigned menu (see entry 3). For agencies, copywriters, designers, consultants — the question is always the same: what's the smallest, most personal artifact I could ship as the opener? ### Cross-link Full tactical recipe in Sales Section 5 (Tips 5.1–5.5) — DM cadence, deliverability protection, channel selection, follow-up flow. The scanner half that produces the lead list lives in Marketing Section 5 — Tip 5.1 (no-website scanner). ## 3. The Menu Redesign Play Restaurants version of the site-mockup loop. Several operators have built this independently — the most public versions trace back to creators in the AI Automation Agency space, with variants run by independent consultancies on client projects. No definitive "first builder." ### The loop Your agent pulls a city's restaurants from the Google Maps Places API, with a category filter that targets the level of place where a menu redesign would actually move the needle — independent restaurants, family-owned spots, small-chain locations that haven't refreshed their branding in five-plus years. It pulls the place ID, the address, the existing photos, and crucially the menu photos. Most independent restaurants have their menu uploaded as a phone snapshot or a PDF link on their profile. The agent grabs it. For each restaurant, the agent runs a parallel pass: read the menu, read the reviews, read the photo gallery, build a one-paragraph read on the place — cuisine, vibe, price point, what locals say about it. Then the redesign skill runs. The agent takes the existing menu, the brand cues from the photos and reviews, and a small reference library of beautifully designed menus from other top restaurants in similar categories, and generates a new menu — same items, same prices, but redesigned. Better typography, better hierarchy, better photography hints, better section order. Sometimes a re-categorization that nudges the higher-margin items to the visual sweet spots on the page. The output is an image, sometimes a PDF if the menu is multi-page. The agent then writes a personalized outreach — usually email if a contact's available on the profile, sometimes Instagram DM — and attaches the redesigned menu with a line like "your menu was speaking to me. I redesigned it. Look — same items, just laid out the way the food deserves. If you want the editable file or want to talk about updating the whole brand, I'm around." The redesign is the proof. The redesign is the meeting request. ### Why it's insane Restaurants are a brutally hard category for cold outreach. Owners are slammed, margins are thin, and they get hit by every web designer and marketer in their city. They've heard the pitch a thousand times. They've never had someone walk in already holding a redesigned version of their menu. The artifact itself does the convincing because no salesperson could justify the time investment per lead to hand-redesign menus on spec — but the agent can do it for the entire city, overnight, for cents. There's a secondary effect that makes this play even harder to copy: once a restaurant owner sees their menu redesigned by anybody, the original version starts looking embarrassing to them. The agent is creating dissonance in their head about the status quo. That's much stickier than a "here's why your business needs a brand refresh" pitch. ### Tools they used Google Places API to source restaurants and pull menu image URLs. Image OCR on existing menus (Gemini 2.5 or GPT-4o vision is usually enough; for difficult PDFs they fall back to a Tesseract pass). A small reference library of well-designed menus stored in a folder, used as image inputs to the generator. gpt-image-1 or Gemini Flash Image for the regeneration. Email and DM delivery same as entry 2. ### The principle you can lift Pick a vertical where the prospect's current asset is visibly suboptimal and publicly accessible. Menus. Storefront signage. Old websites. Linkedin headers. Job descriptions. Ad creative on Meta Ad Library. Have your agent harvest the current version, regenerate a better one, and DM it back. The prospect's response goes from "another cold pitch" to "they already did the work — should I let them finish?" ### Cross-link Sales section, related to the site-mockup DM tactical recipe. The scanner half lives in Marketing 5.1. ## 4. The Google Earth Pool Hunter Multiple operators have built variants of this loop in the past year. The most visible public version is Brendan Jowett's Claude Code build of the "viral AI pool outreach system" — see his post on X (@jowettbrendan) and the accompanying YouTube walkthrough, where he openly frames his version as a recreation of an earlier viral build. The original viral builder's name appears not to have been publicly preserved across the recreations we found; flagged in _open-questions.md for verification. The same concept is now productized as [PoolSend](https://poolsend.com/) ($49/mo, $2.99/postcard) for pool builders who don't want to wire it themselves. ### The loop The agent is given a target geography — a wealthy suburb, a list of postcodes, a polygon over a specific neighborhood. It walks the geography systematically using the Google Maps Static API or Google Earth's satellite imagery, pulling overhead tiles of every residential property. For each property, a vision model evaluates the image: is this a single-family home? Is the lot above a size threshold (filtering for "mansion" vs starter home)? Crucially — does the backyard contain a pool? Properties that are large and poolless become the candidate list. The agent geocodes each address, cross-references public property data where available (parcel size, property value estimate, owner occupancy), and ranks by likelihood-to-convert. Then for each top-ranked address, the agent generates the artifact: a photorealistic composite of that exact backyard with a pool installed, or in the more recent Veo-3.1 versions a short cinematic video of the same. No photos required from the prospect. Just an address. Outreach is either a physical postcard mailed via USPS with the composite printed on it (PoolSend's model — they report 4×6 postcards at $2.99 all-in including print, postage, delivery) or a DM with the video. The agent then hands the qualified list — address, composite, contact info if findable — to a pool installer or sells the lead outright. ### Why it's insane Pool installations are a $40,000-$100,000+ purchase. Pool installers are a fragmented local market spending heavily on Google Ads with high CPLs because their addressable audience is "people thinking about a pool" — which is hard to target. This loop inverts the problem: it doesn't target people thinking about a pool, it identifies every household that physically has the geographic conditions to install one and hasn't. That is the cleanest lead-list-from-firstprinciples in the entire home-services category. And the agent doesn't get tired scrolling Google Earth tiles for forty hours straight to find them. The artifact compounds the unfair advantage. Showing a homeowner a generic "pools for your area" postcard is a 0.1% response play. Showing them their own backyard with a pool in it — same trees, same fence, same shape of patio — is a different sales conversation entirely. PoolSend is monetizing this productized; the indie builds on X show that the underlying loop is buildable by one operator with Claude Code in an evening. ### Tools they used Brendan Jowett's stack (the public recreation): Claude Code as the harness, Google Maps API for satellite imagery and geocoding, an AI vision pass for identifying pools and optimal pool placement on the lot, Veo 3.1 for the cinematic video generation of the address with the installed pool. Original viral version reportedly used a similar stack with gptimage-1 image input for static composites. PoolSend's stack (inferred): high-resolution aerial imagery (likely Maxar or Nearmap rather than Google), a custom vision model trained on pool detection, an image compositing pipeline, USPS direct-mail integration, and a Stripe-based subscription billing layer. ### The principle you can lift The loop is: pick a visual gap in the physical world that signals a buying need (no pool in a big backyard, no roof solar in a sunny zip code, no EV charger at a luxury home, a faded storefront sign, an empty parking lot at peak hours, a roof that needs replacing), use a vision model to scan satellite or street-view imagery at scale, and reach out with the gap visualized as if it were already filled. Real estate, solar, EV charging, commercial roofing, sign replacement, parking-lot leasing, landscape services — every one of those verticals has the same shape of unfair advantage waiting. Critically, the qualifying signal is visible from space. There is no privacy issue, no PII, no scraping someone's inbox. The data is public. The agent is just looking at the public side of the world more carefully than any human ever could. ### Cross-link No direct tactical recipe in this cookbook yet — this might be the cleanest candidate for a dedicated Sales section addition. Marketing 5.1 (no-website scanner) is the closest existing pattern in spirit. ## 5. The Replaced-SDR Loop (11x Alice) Built by Hasan Sukkar and the [11x](https://www.11x.ai/) team. Their flagship product is Alice, an autonomous AI SDR sold as a "digital worker." Rebuilt from scratch in early 2025 on a multi-agent architecture with LangGraph. Detailed public writeup of the rebuild lives in the ZenML LLMOps Database and a LangChain Interrupt talk on YouTube. ### The loop A customer onboards by describing their ICP in natural language. Alice — actually a hierarchy of specialized sub-agents under a coordinating top-level agent — interprets that description, queries integrated data sources to source matching leads, enriches each lead with research (LinkedIn, news, company site, intent signals), drafts a personalized firsttouch email, sends it, monitors the inbox for replies, classifies the reply, drafts the next message in the sequence, books the meeting on the human SDR's calendar when the lead bites, and updates the CRM. Continuously. 24/7. Across thousands of accounts. The architecture matters here. The first version of Alice was a single ReAct-style agent and it hit a ceiling. The rebuild split the job across specialist sub-agents — sourcing, research, personalization, sequence management, reply classification — each one with its own scoped context and tools, all reporting to a coordinator. After the rebuild, in the first months of operation, they reported approximately 2 million leads sourced, 3 million messages sent, 21,000 replies — a reply rate around 2%, which is roughly the benchmark for skilled human SDRs. ### Why it's insane Sales development is one of the most expensive and highest-churn roles in B2B. A team of 10 SDRs costs $1M+/year fully loaded, takes months to ramp, and every one of them moves on within 18 months. 11x's pitch is that a single Alice instance does the work of that team for a fraction of the cost, doesn't quit, doesn't sleep, and gets better as the underlying model gets better. The reason this matters as a "wild example" rather than just a SaaS product is that they publicly documented the engineering reality of building this — the failure modes of single-agent architectures at scale, the surgery required to split it into specialists, the LLMOps work to keep it running. 11x raised over $50M on this thesis. Whether they end up being the winner in the category or just the early flag-planter is open. The loop itself is real, in production, and being copied by every B2B sales tool that wants to ship "AI SDR" by next quarter. ### Tools they used LangGraph as the multi-agent orchestration framework, OpenAI and Anthropic models for different sub-agents depending on task, custom data pipelines for lead sourcing and enrichment, native CRM integrations (Salesforce, HubSpot), email infrastructure with deliverability tooling. The full LangChain Interrupt talk has the architectural details. ### The principle you can lift When a single-agent loop hits a complexity ceiling — too many tools, too much context, too many failure modes — the answer isn't a smarter model. It's the right architecture. Split the work across specialists. Give each specialist a narrow context and a narrow toolset. Have a coordinator route. This is also the shape of the booking accelerator (entry 1) at much smaller scale, and it's the shape of every other ambitious agent in this section. If your agent is doing too many things and failing on the edges, the fix is usually three smaller agents and a coordinator, not a bigger system prompt. ### Cross-link Sales section. The Apollo-driven personalized outreach loop in Sales is the indie-operator version of what 11x sells as enterprise SaaS. ## 6. The One-Person AI Image Empire (Photo AI) Built by [Pieter Levels](https://x.com/levelsio). Solo founder, no employees, no investors, no co-founders. Photo AI generates AI portrait sessions for users — feed it a few selfies, get back hundreds of photoshoot-quality images of yourself in any setting, outfit, or scenario. Public revenue dashboard (Levels posts publicly): roughly $138K/month as of late 2025, with reported 87%+ profit margins. Detailed case study on Indie Hackers. Sister product Interior AI does an additional ~$40K/month with the same one-person model. ### The loop A user uploads selfies. An agent (under the hood, a pipeline of Stable Diffusion fine-tuning, LoRA training, inference orchestration, S3-backed storage, and a billing layer) trains a personalized model on the user's face, then generates the requested photoshoots in parallel batches. The user picks favorites, downloads, shares. Levels runs the whole thing — product, marketing, support, ops — from a laptop, posting his metrics publicly on X. The wild part isn't the product — AI portrait generators are a crowded category. The wild part is the operating model. Levels uses agent assistants to handle support tickets (drafted by an LLM, approved with one click), to monitor metrics, to run his marketing (he posts his own metrics publicly as marketing, but the post drafts and the trend analysis is agentassisted), and to keep the product shipping. The whole business is a feedback loop between him and his agents, and the agents do enough of the operational work that one human can run a $1.5M+/year business with margins higher than most VC-backed SaaS. ### Why it's insane Conventional wisdom for a $1M+ ARR SaaS: hire a CTO, hire support, hire a marketer, raise a seed. Levels's version: zero of those. The principle being demonstrated is that the floor on team size for a software business has collapsed once agents handle the operational glue. Levels is the proof that has been touted for years — he's the founder all the "one-person unicorn" essays are about. Photo AI and Interior AI together do roughly $2M ARR. He's also publicly documented the entire stack so anyone who wants to copy it can. ### Tools they used PHP and jQuery for the front-end (yes, really — pin this as evidence that stack choice matters less than people think), Replicate or similar GPU inference for the model training and image generation, S3 for asset storage, Stripe for billing, ChatGPT/Claude for support drafting and operational work, Levels's own posts on X as the primary marketing channel. Whole thing runs from a Linux server he provisions himself. ### The principle you can lift The threshold for "needs a team" is much higher than the conventional wisdom suggests, if the founder treats the agents as the team. Levels doesn't outsource. He just doesn't hire. Every operational task either gets automated, agentized, or skipped. If your business has more than five employees and revenue under $1M, the honest question is "which of these roles is a human doing because that's tradition, and which is doing something an agent couldn't do?" Most operators don't ask. Levels asked and built the answer. ### Cross-link Outside the marketing/sales/ops main sections — this is a "shape of the business" example rather than a tactical recipe. Closest analog: the SEO section's full-loop tip 4.5, where a single operator runs an SEO content engine that would otherwise need a team. ## 7. The AI-Run Retail Store (Luna at Andon Labs) Built by Axel Backlund and Lukas Petersson at Andon Labs. They signed a three-year retail lease in San Francisco and handed operational control of the storefront to an AI agent named Luna. Covered by Good Morning America and PYMNTS. ### The loop Luna runs on Anthropic's Claude Sonnet 4.6 for reasoning and Google's Gemini Flash-Lite for voice. The agent chooses inventory based on what it predicts will sell at the location, places orders with suppliers, sets prices, rewrites the employee handbook for the human store associates (yes — Luna manages humans, not the other way around), handles customer service inquiries, processes returns, and adjusts strategy based on what's selling and what isn't. The store opens, the store closes, the agent runs it. The honest result, also publicly reported: financially underwhelming so far. Luna spent ~$15K on inventory and generated ~$2K in revenue against a ~$7.5K monthly lease. The experiment also revealed a long tail of AI behaviors that needed guardrails — the agent making bizarre buying choices, hallucinating supplier pricing, getting confused about local logistics. Andon Labs is publishing the failure modes openly because that's the actual point of the experiment. ### Why it's insane This isn't a "look, an agent does retail" gimmick. It's a serious public stress test of how close current frontier models are to running a physical operation end-to-end. The answer in 2026 is: closer than five years ago, not as close as the marketing suggests, and the failure modes are the most valuable output. Most companies running agent experiments hide their failures. Andon Labs leased a building and put theirs on Good Morning America. That's a different category of public commitment to honest evaluation. The wild loop is also that humans report to the agent. The agent writes the schedule. The agent writes the handbook. The agent decides what to stock. The humans execute. This inversion of the org chart is what makes Luna a hall-of-fame example regardless of the P&L. ### Tools they used Claude Sonnet 4.6, Gemini Flash-Lite, custom integrations with point-of-sale, supplier ordering APIs, voice for in-store customer interactions, and a guardrail layer for the things the agent kept getting wrong. ### The principle you can lift If you want to know what's actually possible with current models, run the loop end-to-end with skin in the game and report the failures publicly. Most "AI in production" claims are demos. Luna is a lease. The honesty about what's broken is more useful than the typical pitch deck about what works. For operators: pick a small loop in your own business, run it end-to-end with an agent, and write down everything that breaks. The failures are the roadmap. ### Cross-link Outside the main sections — closest analog is the Ops section's full-meeting-to-todo loop, but Luna is an order of magnitude more ambitious. ## 8. The Autonomous Etsy Empire (Eight-Agent Shop) Documented by Charles Ross in a public Medium writeup. The architecture is the loop. Multiple operators have built variants — Ross's is the most cleanly documented. ### The loop Eight agents. One human operator at the top, a commander agent below, and six specialist agents under the commander, each with a name and a job. Oracle does market research — what's trending, what's underserved on Etsy, what gaps exist in product categories the operator targets. Muse designs the products — generating mockups, refining them, producing print-ready files. Scribe writes the listings — title, description, tags, SEO. Sentinel handles compliance — Etsy has tightened enforcement on AI-feeling shops, so Sentinel makes sure listings read as branded and human, photos look right, metadata is clean. Herald runs customer service — replying to messages in the shop's voice, handling returns, defusing complaints. Ledger runs the analytics — what's selling, what's not, where to cut, where to double. Evangel runs the marketing — social posts, ads, off-platform traffic. Every agent has a defined role, a constrained toolset, and a clear input/output contract with the commander. The commander coordinates and escalates to the human operator only when explicit approval is needed (new product launch, ad spend over threshold, brand voice deviation). The human's job is policy and approvals. The agents do the work. ### Why it's insane Etsy is hyper-competitive. The successful Etsy stores are run by sellers who treat it like a full-time job. This loop pulls that full-time job apart into seven specialist agents, each doing the part of the job an actual marketplace seller would do, in parallel, 24/7. The interesting wrinkle is Sentinel — Etsy has explicitly been cracking down on AI-generated shops, and the architecture's response is to dedicate a whole specialist agent to making the shop look unmistakably not AI-generated. That's a level of meta-awareness that didn't exist in the autonomous-business writeups from two years ago. The agents know what gets you banned and they actively defend against it. The shape — commander + six specialists + one human — is roughly the same shape 11x landed on for Alice (entry 5). Different industry, same architectural answer. ### Tools they used Claude (Ross specifies Claude in the writeup) as the model layer. Custom agent orchestration. Etsy's seller API for listing operations and analytics. Image generation (DALLE or similar) for product mockups. Print-on-demand integrations (Printful, Printify) so the supply chain is also automated. Plausibly LangGraph or similar for the multi-agent coordination, though the writeup is light on framework specifics. ### The principle you can lift For any business where the operator's job decomposes into a handful of distinct functions (research, build, list, market, sell, support, analyze), the multi-agent specialist pattern is the right shape. Not one big agent trying to do everything — that fails at complexity. Specialist agents reporting to a coordinator with the human as policy-maker is the architecture that's repeatedly winning in 2025-2026 builds. If you can list five-to-eight distinct roles in your business, you can build five-to-eight specialist agents. ### Cross-link Ops section, conceptually. The "running todo list" and "team meeting to action items" loops in Ops are smaller-scale versions of this same coordinator pattern. ### How to read this section Every entry above is one of three things: 1. A loop you can rebuild this quarter if you have the technical chops (1, 2, 3, 4). 2. An architecture pattern that should change how you think about your own agent stack (5, 8 — the multi-agent specialist pattern is the most important architectural lesson in this whole section). 3. A reframe on what your business has to look like (6 — solo operator, agents as the team; 7 — humans report to the agent, failures are the product). The loops will keep changing. The principles won't. The principle behind every one of them is the same: agents make the marginal cost of personalization, scanning, research, and operations collapse toward zero. Whatever you would have done for your single best prospect, you can now do for every prospect. Whatever a five-person team did, you can now do with one person and eight specialist agents. More will land in this section over time. If you build a wild loop, send it in — credit will go where it's earned. ### Skills Library Every skill in this directory is a SKILL.md (Claude Code skill format) anonymized from a production system. Names, emails, paths, and client identifiers have been replaced with , , , etc. Specific tool names have ALSO been replaced with placeholders like , , so each skill reads as a pattern, not a tool stack. A handful of OSS libraries and platform-native APIs are kept by name (yt-dlp, Groq Whisper, fal.ai, SearXNG, Scrapling, Playwright, Puppeteer, Ahrefs/Semrush/GA4/Search Console APIs, YouTube Data API, Google Maps/Earth APIs, Anthropic/OpenAI APIs) — those ARE the recipe. Hand any skill folder to your agent and tell it: "install this for me — drop it where you'll pick it up next session." That's the entire install. The agent handles the path. Before using any skill, tell your agent: "Tailor this skill to use our tools — replace the placeholders with our actual stack." The placeholder block at the top of each SKILL.md lists what needs to be filled in. Each skill is mapped to the cookbook section it supports. # Marketing Skill | What it does Skill: broll-generator • What it does: Generate animated HTML b-roll clips for video editing; creative + code agent split Skill: content-engine • What it does: Daily content orchestration across YouTube, LinkedIn, X, Reddit Skill: content-reddit • What it does: Value-first Reddit engagement — find threads, draft non-promotional replies Skill: content-repurposer • What it does: One long-form input → 5-8 platform-native outputs Skill: content-twitter • What it does: Tweet + thread frameworks, hook/CTA templates Skill: image-gen • What it does: Image generation with platform-sizing presets Skill: linkedin-copywriting • What it does: LinkedIn post copywriting rules — hook, CTA, algorithm tactics Skill: linkedin-post-writer • What it does: RSS + scraped-posts → branded LinkedIn drafts Skill: linkedin-scraper • What it does: Browser + auth-cookie pattern for monitoring target creators Skill: script-polish • What it does: Voice-polish template for video scripts (build your own voice rules) Skill: seo-blog-template • What it does: Automated SEO blog post template (keyword → outline → publish) Skill: site-analytics • What it does: Analytics + Search Console + session-recorder integration recipe Skill: thumbnail-generator • What it does: AI bg + cutout + text-overlay YouTube thumbnail QA loop Skill: youtube-analytics • What it does: YouTube Analytics API reference Skill: youtube-comment-sniper • What it does: Monitor + reply + funnel-into-community pattern Skill: youtube-descriptions • What it does: Channel-agnostic description template Skill: youtube-pipeline • What it does: End-to-end YouTube pipeline (script → record → edit → publish) Skill: youtube-shorts-repurposer • What it does: Long-form video → vertical shorts Skill: youtube-slides • What it does: React/Next.js slides synced to video recordings Skill: youtube-titles • What it does: Title research + brand-consistent generation # Sales Skill | What it does Skill: agreements • What it does: SOW + agreement drafting via your doc tool Skill: booking-accelerator • What it does: Pre-call asset generation triggered by new bookings Skill: call-confirmation-emails • What it does: 24h-before booking reminder drafts Skill: consultation-recap • What it does: Value-focused post-consultation recap email in team voice Skill: deal-accelerator • What it does: Pre-call X-ray + transcript → tailored follow-up assets Skill: email-followups • What it does: Transcript-aware, draft-only follow-up generator Skill: lead-monitor • What it does: Booking webhook + polling fallback → new-lead alerts Skill: lead-status • What it does: Parallel-subagent fan-out: email + chat + call transcripts + CRM Skill: meeting-prep • What it does: One-page pre-call brief from all data sources Skill: outreach-drafter • What it does: Research → short conversational email drafts (never sends) Skill: pdf-proposal • What it does: HTML + headless-browser → branded PDF proposal Skill: pipeline-closer • What it does: First-contact → close lifecycle tracking + stall detection Skill: pipeline-report • What it does: Parallel-per-lead aggregator → pipeline dashboard Skill: pipeline-sdr • What it does: Zero-token scanner + scheduled drafter SDR architecture Skill: post-call-autopilot • What it does: 10-min cron after transcript: classify, proposal, scorecard, notify Skill: pre-call-research • What it does: 8-phase prospect research with confidence tagging Skill: retell • What it does: Outbound AI voice calling + post-call summary Skill: scope-analyzer • What it does: Prospect requirements → scoped delivery estimate Skill: scoped-proposal • What it does: Guaranteed-scope proposal structure for enterprise deals Skill: stale-lead-blitz • What it does: Find stale leads → pull context → per-lead email drafts Skill: client-onboarding • What it does: Welcome → env setup → first-week cadence skeleton # Operations Skill | What it does Skill: agent-browser • What it does: Headed browser automation primitives Skill: attio-crm • What it does: CRM API reference for agents Skill: calendar-event • What it does: Calendar event creation pattern Skill: excalidraw • What it does: Diagrams-as-code for system design Skill: fathom-transcripts • What it does: Meeting-recorder transcript archive structure + webhook handling Skill: frontend-design • What it does: Component + design-system patterns for frontend agents Skill: google-workspace • What it does: Email/Drive/Docs/Calendar CLI reference Skill: model-switch • What it does: Pick Haiku/Sonnet/Opus by task complexity Skill: netlify • What it does: Deploy + DNS automation for static sites Skill: porkbun • What it does: Domain + DNS via your registrar's API Skill: project-planning • What it does: Greenfield project breakdown pattern Skill: prompt-guard • What it does: Prompt-injection detection wrapper Skill: revenue-tracker • What it does: Daily revenue rollup pattern Skill: search • What it does: Local SearXNG search wrapper Skill: security-audit • What it does: Repo security audit checklist Skill: slack-formatting • What it does: Messaging-tool mrkdwn reference Skill: status • What it does: "What's running / what's open" snapshot Skill: tmux-interactive • What it does: Long-running interactive sessions in tmux Skill: whatsapp • What it does: Read-only messaging-tool history via local SQLite Skill: whatsapp-web • What it does: Messaging-tool web automation for file send ### Auto-loaded helpers Prefix _auto- skills are loaded by default into the agent context. Skill | What it does Skill: _auto-competitor-intel • What it does: Background competitor monitoring Skill: _auto-headed-browser • What it does: Default headed-browser routing Skill: _auto-memory-search • What it does: Semantic memory lookup before answering # Templates Skill | What it does Skill: voice-skill-template • What it does: Build your own per-channel voice skill by reading 100+ of your own sent messages How to use # Just tell your agent: "install the lead-status skill from _skills-anonymized/ for me." # Or for the whole library: "install every skill in _skills-anonymized/ for me." # The agent reads each SKILL.md, picks the right install path, and confirms. Replace every placeholder with your own values. Skills are starting points — adapt to your stack. The fastest way: drop a skill into your agent and prompt "Tailor this skill to use our tools." # Audit Full classification (UNIVERSAL / COMPANY-SPECIFIC / BORDERLINE / DEPRECATED) for all 83 source skills lives in `../_skills-audit.md`. ### What this is A working directory of the best AI agent use cases, organized by business activity. Every entry is a real loop someone is already running — not a "capability list," not a theoretical pitch. If a tip is here, it works. The companion Skills Library ( _skills-anonymized/ ) is drop-in ready for any Claudecompatible agent: clone, drop into your skills folder, tell your agent to tailor it to your stack. Each skill file already has a "tailor me" header that explains exactly what to swap. ### How to use this Pick a section. Pick a tip. Hand it to your agent. Every tip follows the same shape: What it does — the loop in one paragraph Why it wins — the 10x angle vs. doing it manually Tools — named, opinionated picks How to wire it — concrete steps, cron specifics, trigger points Example prompt — paste-ready, will run as-is Watch out for — the real failure modes Skill file — link to the matching _skills-anonymized//SKILL.md if there is one You don't need to read this front-to-back. Skim the table of contents, find the loop closest to your bottleneck, and copy. ### The skills library — every SKILL.md is tool-agnostic on purpose. You'll see placeholders like , , . That's intentional. The first line of every skill file says: Tailor this skill before using. Tell your agent: "Tailor this skill to use our tools — replace the placeholders with our actual stack." _skills-anonymized/ That's the workflow. Your agent reads the pattern, asks what you use, edits the file, ships. ### How to pick what to build first Stop trying to do everything. Run one loop end-to-end. Get one ROI win. Then layer. The fastest-paying-back loops, in order: 1. Meeting intelligence (Ops) — transcripts + auto-extracted action items per call. One day of setup, lifetime of cleared mental load. 2. Follow-up loop (Sales) — agent watches for ghosted threads in both directions. Recovers deals you forgot about. 3. Content engine (Marketing) — one long-form input → 5-8 platform-native outputs. Kills the "what do I post today" tax. 4. Apollo personalized outreach (Sales) — agent enriches and personalizes 10-15 leads/day. Replies land because each message is real. 5. Living TODO (Ops) — agent maintains your task list from what you say + what's in transcripts + what's in Slack. No more "did I do that?" Once you have one loop humming, the next one takes half the time. The agent learns your tools, your voice, your stack. ### Contributing your own loops This guide grows. Send the Wild Examples section anything you've built that other operators would steal. Loops, not screenshots. End-to-end recipes, not vague capabilities.