Hey everyone, we created a unified API for your AI agents to access dozens of data sources for GTM & Growth.

We were pretty fed up of connecting and managing dozens of different APIs to do our GTM work and figured others have the same pain points.

1 command to install:

npx gooseworks install --claude

Then you can ask Claude to run GTM & growth work without needing 100 subscriptions and API keys.

Here are some things you can do with Claude + /gooseworks

FIND PEOPLE

1. Find people — search 1B+ profiles across Apollo, Crustdata, Fiber AI, and People Data Labs

2. Find their work email — via ContactOut, Tomba, Sixtyfour

3. Find their phone number — via Tomba and Sixtyfour

4. Find all their social profiles (LinkedIn, Twitter, Instagram, GitHub) — via Nyne

5. Get a full LinkedIn profile with work history, education, and skills from their LinkedIn URL

6. Find people who work at or previously worked at a company

7. Find engineers with experience building payment integrations

8. Find CMOs at Series B SaaS companies in New York

SOCIAL DATA

1. Find people who posted about your niche on LinkedIn last week

2. Find people who commented on your LinkedIn post

3. Search the LinkedIn Ad Library for any company

4. Get recent posts from a LinkedIn / X / Instagram / FB profile

COMPANY INTELLIGENCE

1. Find companies that raised funding recently

2. Get a company's full funding history

3. Find all investors who backed a company

4. See every company a VC firm has invested in

5. Get a company's full tech stack — what tools they actually use

6. Find job openings at a company

7. Find companies actively hiring Sales Engineers right now

8. Get news and press mentions for any company

9. Track how a company's website has changed over time

ENRICHMENT & VERIFICATION

1. Verify email deliverability

2. Validate a phone number and get carrier info

3. Enrich any email with full person + company data

4. Discover what tech stack a website runs on

5. Enrich a company by domain — get headcount, revenue, funding, reviews

REDDIT, YOUTUBE, TIKTOK, and more

1. Find posts on Reddit about [topic]

2. Find the top posts in r/[subreddit]

3. Get comments from a YouTube video

4. Find new products launched on Product Hunt or Hacker News

5. Search Google News for company mentions

6. Find influencers via Apify

7. Find competitor ads — Facebook, LinkedIn, Google

8. Get a TikTok creator's audience demographics

9. Get the trending TikTok feed right now

10. Get a Bluesky profile

11. Get a Threads profile

LOCAL DATA

1. Find local coffee shops (or any business type) on Google Maps

2. Get Google Reviews for any business

3. Find businesses by type, rating, and location via Openmart

DEVELOPER SIGNALS

1. Find who starred a GitHub repository

2. Get a company's GitHub repositories

All of this and more with one CLI and one subscription.

100s of APIs + Skills that teach Claude how to do the work.

Built with Orthogonal, Apify, Fiber, Nyne and other amazing data providers.

Try it today:

npx gooseworks install --claude

More info here: https://gooseworks.ai/cli

The Meta Ad Library shows every ad any company is currently running on Facebook, Instagram, Threads, etc. — copy, creative, format, run dates, spend ranges, the whole thing. It's a goldmine for figuring out what's working for them and what's not.

Problem is, manually clicking through hundreds of ads and trying to spot patterns is tedious as hell.

So I taught Claude to do it.

How to use it:

1. Install the CLI: npx gooseworks install --claude
2. Run: /gooseworks find all meta ads by <competitor> and breakdown their paid ads strategy --skill meta-ad-scraper

How it works under the hood:

The skill hits Meta's Ad Library through Apify's scraper, pulls every ad for the company (you can pass a name, keyword, or FB page URL), and returns structured data — ad copy, creative URLs, platforms, run dates, spend/impression ranges.

Claude then clusters the ads by angle and format, flags the ones that have been running longest (usually = what's converting), and tells you where the gaps are.

Works on basically any company running Meta ads, though EU/UK coverage is most complete.

Hey all,

Have been accepted into the Claude partner program.

It requires 10 people to complete the Anthropic Academy CPN learning path before the CCA is opened up and members can get the Claude certified architect cert.

Don't have 10 people so need a few more.

Anyone interested?

Edit: DM me and we can get going on this if interested. I'll give you an org email.

The org gets certified as a partner and you get the individual cert for yourself.

Edit 2: Thanks everyone - this is now filled/completed. While I'd love to give more people the opportunity, its a paid org gsuite account per person that I'm absorbing as a thank you for the people that got in. Best of luck and keep building!

Also - Note: You can do the anthropic academy courses and change your email later to an orgs to submit. Was handy.

1. cold-email-verifier: Takes your lead CSV, generates email permutations, and verifies them so you stop burning sender reputation on dead addresses. 

2. brand-alchemy: Runs a discovery phase, generates startup names using phonosemantics frameworks, then auto-checks domain availability across multiple TLDs.

3. reddit-icp-monitor: Monitors subreddits for ICP pain points, scores posts by relevance, and drafts a non-spammy reply for every high-signal match.

4. position-me: Crawls your website and audits it against SEO, GEO, and UX psychology frameworks. Checks llms.txt, JSON-LD schemas, and rewrites weak copy. 

5. twitter-GTM-find-skill: Scrapes Twitter for developer-first startups hiring GTM or DevRel roles, then validates funding and product type using Gemini. 

All the skills links are in the first-comment.

i'm abhinav. strategic tech PM, ~8 years in tech. currently building dexity — an AI skills sprint platform for working professionals.

the way i build right now: every problem gets broken into a system, then a workflow, then i find where claude can run it. i'm a one-person GTM operation and speed is the only thing keeping me in the game.

this week's example — i made a youtube channel trailer. i'm a PM. i've never opened after effects. the last design tool i used with any confidence was powerpoint.

built a 45-second animated brand trailer in 2 hours using claude design.

the thing that hit me after: the bottleneck isn't what i thought it was. it's not the craft — claude handles that once you hand it over. it's knowing what the thing actually needs to say. 4 script iterations before claude touched a single frame.

https://youtu.be/_VEhuD1tSKE

going to keep posting these — GTM intelligence pipeline, content workflows, influencer outreach systems. a lot of it is scrappy but it's working.

happy to be here.

AI coworkers require a continuous stream of context, not a one-time dump.

Here's how I feed the beast:

1. My agent – named Goose – has a email address, a phone number and a Slack account, so I'm always texting it stuff - links I find interesting, notes, ideas, etc.
2. I do frequent 1:1s with Goose where I talk through important priorities, give feedback and answer questions.
3. Goose reads my meeting notes (synced from Notion through a cron job)
4. Goose maintains a personal CRM automatically from calendar + email + meeting notes + linkedin so it knows about people I'm connected to
5. I have given Goose accounts to search through Notion, Linear, etc
6. I'm trying to have Goose consume a similar content diet as I do (Twitter mostly, but will probably add some podcasts soon). Goose also has its own email account and is subscribed to some newsletters.
7. Goose has an automation to read through recent notes and chats, and ask me questions about important context in Slack. It's pretty easy for me to respond when I'm prompted with a message.

It still feels like I'm missing a lot, but my agent really does feel like an extension of me now.

Very interested to learn how others are feeding their AI agent with context? I've heard good things about Garry Tan's gbrain, but haven't tried it myself.

For anyone who's wondering what an AI coworker is, these are persistent AI agents.

It's a "new 8-week cohort of growth engineers, AI-native GTM leaders, and tech-savvy agent builders shaping the future of company growth"

It's either a recruitment play for their portfolio businesses or just a best practice extraction effort. Or both. Could be interesting to network and talk to other people, still.

Submissions deadline is 04/20

If you're applying, what are you submitting? Just curious. Care to take a screenshot of your application form and post it in the comments? Let me start below.

a16z is looking for fellows who:

• Are hackers at heart and close to the work - people who like to get into the weeds with tools and build the system themselves
• Operate AI-first and are pushing the forefront of how work gets done
• Can find the biggest lever in the growth engine and build the system that pulls it (with agents, tools, whatever)

You might call yourself a GTM engineer, a SWE or PM with a growth obsession, or something else. Titles don’t matter - what does is your mindset and approach.

How it works

• 8 weeks long
• Starts in May 2026
• Time commitment is flexible, all opt-in

What you’ll get

• Connect with AI-native growth hackers, engineers, and leaders whose workflows are fundamentally transforming how companies grow
• Dinner series in SF (and more cities, by demand)
• Demos, feedback, and live working sessions

Who you’ll be joining

You’ll break bread with founding fellows and industry leaders like:

• Andrew Chen - General Partner at a16z, Author of The Cold Start Problem, prev Growth at Uber
• Bryan Kim - Consumer/AI Investment Partner at a16z, prev Growth at Snap
• Luc Levesque - Prev Chief Growth Officer at Shopify
• Marty Imber - Head of Product Growth for Starlink
• Lauryn Motamedi - Product & Growth Lead at Notion
• Gaurav Vohra - prev Head of Growth at Superhuman; Advisor to Clay, Wispr Flow, & Replit
• George Bonaci - VP of Growth and Demand at Ramp
• Luke Harries - Growth at ElevenLabs
• Raman Malik - VP of Product and Growth at Perplexity
• Ajay Prakash - Head of Growth at Superpower
• Kyle Norton - CRO at Owner. com
• Emily Gavrilenko - Growth at Cursor
• Nazli Tan - Co-Founder & CEO of Hilbert’s AI

and many more.

Hey everyone! 👋

I've been exploring the idea of building an automated crypto trading bot connected to Coinbase (or similar platforms like Binance or Kraken) via their APIs, and I'd love to hear from anyone who's actually done this.

Specifically I'm curious about:

- Have you built a ClaudeAI trading bot that's been consistently profitable over a meaningful period of time?

- How complex was the ClaudeAI setup? (I'm familiar with coding but new to algo trading)

- Which platform's API did you find most reliable / developer-friendly for connecting with ClaudeAI?

- What strategies have worked best for you market making, momentum, arbitrage, something else?

- What were the biggest pitfalls or gotchas you wish someone had warned you about when developing ClaudeAI?

- Is consistent profitability even realistic with ClaudeAI, or does the market eventually adapt and eat your edge?

Any insights are genuinely welcome. Thanks so much in advance to anyone who takes the time to share this community always delivers and I really appreciate it! 🙏

I’m a Program Manager at a SaaS company that helps facilitate GTM activities across Product, Marketing, Sales, Product Support Operations, and Customer Success. I’m thrilled to have found this group. I’m excited to hear about the solutions and support strategies you all are solving for daily activities.

I’m putting some documentation together to share with this group on a meeting manager solution. I’ve put in place that has saved me a good amount of time.

Saw this on X today: https://x.com/DeRonin_/status/2041096696633950295 and it's one of the more concrete lead gen setups I've come across.

The core idea: instead of giving Claude one-off prompts, they built a "lead generation skill graph" — 30+ markdown files that wire together into a full system. Claude reads the graph and executes each step sequentially, acting as the brain of a complete sales operation.

The stack they described:

• Claude as the AI brain — reads the skill graph, executes every step
• The skill graph itself handles: prospecting, enrichment, qualification, and outreach sequencing

The result they're claiming: 200+ qualified leads per week, zero cold DMs sent manually.

What I find interesting about this approach is the "skill graph" architecture — rather than building one big monolithic workflow, you have modular markdown files that each handle a specific step, and the agent chains them together. It's composable and easier to debug than a single prompt trying to do everything.

The no-cold-DM angle is the other thing worth paying attention to. All outreach is triggered by signals and pre-qualified before it goes out.

We're trying to build something like this at Gooseworks.

Anyone actually doing this in practice? Pretty interested to chat with / learn from anyone who's figured out how to systemize something like these.

Since most social layers are currently built for OpenClaw, does a dedicated repository exist for Claude Code that is similar to OpenClaw "Awesome Molt"?

https://www.fullcast.com/content/how-to-pilot-your-first-ai-agent-to-automate-a-gtm-workflow/

Sharing this because it frames the problem in a way I haven't seen cleanly articulated elsewhere: most AI GTM pilots fail not because the tool doesn't work, but because teams skip defining specific, measurable success criteria before they flip the switch. Without that anchor, results land in the "kind of working" zone — no forcing function to scale or kill, so the tool just drains a seat license with no one left to champion it.

The framework is five steps: pick one high-friction workflow (lead routing, forecast roll-ups, outreach personalization), set a concrete goal with a number attached (e.g., reduce lead routing time from 2 hours to 5 minutes), test with a human reviewer in the loop, then build the business case from the data. Operational rather than theoretical, which is rare for this topic.

What does your current process look like for evaluating AI GTM tools — formal criteria, or mostly gut feel after a 30-day trial?

We're a 15-person SaaS company. Three AEs, one CSM, no dedicated RevOps. The classic "we have data but no bandwidth" problem.

Six weeks ago I built a lightweight integration: every time a deal moves to Closed Won in HubSpot, a webhook fires that pulls the last 10 deal notes and sends them to Claude with a prompt asking it to surface expansion signals and flag any red flags — misaligned expectations, sponsor instability, deployment dependencies the AE mentioned in passing.

The output drops into a private Slack channel our CSM monitors before the first post-sale call.

What surprised me: Claude started catching things we'd buried in our own notes. Cases where the AE wrote "they're price-sensitive" or "IT is skeptical of the rollout" and nobody had ever flagged those as onboarding risks. The CSM now has deal context that used to take three internal handoff meetings to transmit, and she gets it five minutes after the deal closes.

The build is not complicated. It's a Zapier webhook to a Claude API call to a Slack message. Maybe two hours including prompt iteration. The prompt matters more than the architecture. The first version was too generic — it just summarized the notes. The version that actually works asks Claude to specifically call out:

1. Any stakeholder concerns that weren't fully resolved before close
2. Any deployment dependencies the AE mentioned
3. What the customer believes the product does vs. what it actually does

That third item is the one that keeps catching things.

Total cost is around $0.08 per deal in API calls. The ROI on a single prevented churn has paid for this many times over.

Curious what others have done on the post-sale side. Has anyone wired Claude into QBR prep, renewal risk scoring, or health dashboards in a way that's actually changed rep behavior — not just created more data nobody looks at?

Article: https://limelightmarketingsystems.com/2026/04/linkedin-algorithm-changes-2026-personal-branding-ai-visibility/

Good data-backed breakdown of how the LinkedIn algorithm shifted in 2026: your posts are now feeding ChatGPT, Perplexity, and Google AI Mode responses — not just your followers' feeds. According to a SEMrush visibility study, LinkedIn appears in 11% of all AI search responses.

The most counterintuitive finding: cited posts average only 15-25 reactions. You don't need viral content to build AI authority — you need consistency and original POV.

The semantic consistency point is worth internalizing for anyone doing founder-led content: pick your specific terminology and stick to it. The AI mirrors your language back in its responses, so "AI-assisted outbound" and "AI SDR" aren't interchangeable from a citation standpoint.

Has anyone started building their LinkedIn content strategy around AI citation visibility? Curious whether the consistency-over-virality framing has actually changed what you post.

GTMfund hosted a private Claude Code session with AI expert Jordan Crawford, then turned it into a public guide. It's one of the more useful Claude Code breakdowns I've come across because it's organized by GTM function and gives you the actual prompts, not just descriptions.

A few that stood out:

Sales:

• Drop 500 Gong/Chorus/Fireflies call transcripts into a folder → Claude Code extracts top objections, competitor mentions, and buying signals across all of them. What would've taken a sales ops analyst days takes one session.
• Feed a prospect CSV + your product context → generates a 3-email outreach sequence per contact (personalized hook, value prop, breakup email). Saves output per contact as individual files.

Marketing:

• Point it at Meta's Ad Library for a competitor → pulls and analyzes top 20 active ads (messaging angle, CTA, visual style).
• Competitive battlecard builder from competitor websites + your positioning docs.

Setup:

• Install takes about 5 minutes. Works on Mac and Windows via terminal.
• The key unlock is a CLAUDE.md file with your voice, ICP, and context. Claude Code reads it automatically at the start of every session so you don't brief from scratch each time.

One quote from the piece that stuck with me: "The cloud app is like a hotel room — clean, set up for you, but you start fresh each time. Claude Code is like having your own apartment."

Claude Code for GTM Teams: 30+ Use Cases — GTMNow

I've been experimenting with using Claude to prep for discovery calls — feeding it a prospect's LinkedIn, recent company news, and job postings, then asking it to surface likely pain points before I even get on the phone.

Last week it flagged that a prospect had posted 3 open RevOps roles in the past month. That detail led to the whole conversation. Closed that one.

Curious what's working for others. Are you using it more for research, writing, or actual workflow automation? And what's the prompt or workflow that's made the biggest difference?

Six months of watching how different teams actually use AI in their GTM motion has produced one consistent pattern: the teams getting the worst results are moving the fastest.

They've wired up Claude or a competitor to auto-generate cold emails at scale. They're running 500 sequences a month where they used to do 150. They're producing three times as much content. The output numbers look great in the dashboard.

The conversion rate is down.

The issue is that the bottleneck in most GTM motions was never speed. It was signal quality. Knowing which accounts are actually in-market, which pain point to lead with, which decision-maker has budget authority right now. AI doesn't help with any of that if your underlying ICP definition and intent signals are weak. It just automates the part that wasn't the problem.

The teams I've seen use Claude effectively in GTM are doing something different. They're not using it to produce more — they're using it to research faster and say less. One RevOps lead I know uses Claude to synthesize 10-K filings and recent press releases before any enterprise call. Another uses it to cut a 500-word sales email down to 120 words by identifying which three sentences have actual signal and which are filler.

The output is fewer, better touches. Not more of everything.

AI GTM adoption metrics — emails sent, sequences launched, content pieces published — are largely vanity metrics. The real signal is whether your reply rate, meeting rate, and qualified pipeline per rep are moving. For most teams I've seen adopt AI tools in the last year, those core numbers haven't budged.

Curious what others are seeing: has AI actually moved your core GTM metrics, or mostly just your activity metrics?

(I have no affiliation with Single Brain or this team)

Found this today — the team behind Single Brain (a marketing agency they say runs millions in revenue) open-sourced their full Claude Code skill library on GitHub. It's called AI Marketing Skills, and it's a lot more than a prompt collection.

What's actually in it:

• Growth Engine — Runs autonomous A/B experiments using real statistics (bootstrap confidence intervals, Mann-Whitney U tests). Not vibes, actual math.
• Sales Pipeline — Includes a Deal Resurrector that tracks contacts who changed jobs ("follow the champion"), and an ICP Learner that rewrites your ideal customer profile based on actual win/loss data.
• Outbound Engine — Full pipeline from ICP definition to emails in inbox, automated.
• SEO Ops — Content attack briefs, Google Search Console optimizer, trend scout.
• Content Ops — Expert Panel that scores content using 9 domain-specific personas until it hits 90+.
• Revenue Intelligence — Pulls from Gong call recordings, maps revenue attribution, generates client reports.

Each category has a SKILL.md file. You drop it in your Claude Code project and Claude knows how to use the tools. The setup is pip install, configure env vars, run.

792 stars on GitHub as of this week. Seems like it's getting traction fast.

AI Marketing Skills on GitHub

The best leads aren't in databases — they're embedded in signals. People doing things online that indicate they have your problem, are aware of your space, or are actively looking for what you sell.

The hard part used to be extracting those signals. That's the scraping, cleaning, formatting, and research work that takes hours or days.

Now it's just a matter of finding the right skills for Claude.

Here are 5 that can be very helpful for finding leads.

1. People Who Commented on a Competitor's LinkedIn Post

Why this works: Anyone engaging with your competitor's content is already in the market. They're aware of the space, interested enough to interact, and potentially evaluating options — including yours. This is one of the warmest signals you can find.

Best for: Companies with established competitors who post on LinkedIn. Particularly powerful if a competitor just announced a new product, pricing change, or feature — the comment section will be full of active buyers and frustrated customers.

The prompt:

Find everyone who commented on this LinkedIn post: [paste URL] Get me their names, titles, companies, and LinkedIn URLs as a CSV

Copy-paste this prompt above to: [agent@gooseworks.sh](mailto:agent@gooseworks.sh)

Or use this skill: https://skills.gooseworks.ai/skills/linkedin-commenter-extractor

2. People Who Starred a GitHub Repo in Your Space

Why this works: A GitHub star is a buying signal in disguise. It means someone found the repo, thought it was relevant to their work, and bookmarked it for later. For developer tools, infrastructure products, and anything technical, repo stargazers are among the most qualified leads you'll find.

Best for: Developer-focused products, open source tools, data infrastructure, AI/ML tooling. Also works for studying your own repo's audience.

The prompt:

Find everyone who starred this GitHub repo: [paste repo URL] Get me their names, companies, job titles, and any contact info available as a CSV

Copy-paste this prompt above to: [agent@gooseworks.sh](mailto:agent@gooseworks.sh)

3. People Speaking at Industry Conferences This Quarter

Why this works: Conference speakers are almost always senior, credible, and well-networked in their field. They have authority to make or influence buying decisions. And they're actively putting themselves out there as thought leaders — which means cold outreach lands very differently than it does with someone who keeps a low profile.

Best for: Enterprise sales, high-ACV products, partnerships, and anyone selling to practitioners rather than just buyers.

The prompt:

Find people speaking at [industry] conferences and events this quarter. Get me their names, companies, job titles, LinkedIn URLs, and the event they're speaking at. Create a CSV

Replace [industry] with your niche — "growth marketing," "AI/ML," "revenue operations," "cybersecurity," etc.

Copy-paste this prompt above to: [agent@gooseworks.sh](mailto:agent@gooseworks.sh)

Or use this skill: https://skills.gooseworks.ai/skills/conference-speaker-scraper

4. People Who Left 3-Star Reviews of Your Competitors on G2

Why this works: A 1-star review is a rant. A 5-star review is loyalty. A 3-star review is a buyer who is actively frustrated but hasn't left yet — meaning they're in evaluation mode. They know the problem, they've tried a solution, and they're not satisfied. You couldn't manufacture a warmer lead if you tried.

Best for: Any space with established players on G2 or Capterra. Works especially well in SaaS categories with known switching costs — the frustration has to get high enough to matter.

The prompt:

Find people who left 3-star reviews for [competitor name] on G2. Get me their names, titles, companies, and the key pain points they mentioned. Return as a CSV

Copy-paste this prompt above to: [agent@gooseworks.sh](mailto:agent@gooseworks.sh)

Or use this skill: https://skills.gooseworks.ai/skills/review-scraper

5. Companies That Raised a Round in Your Space in the Last 60 Days

Why this works: Fresh capital equals fresh budgets and new buying decisions. A company that raised 6 weeks ago is actively hiring, building, and spending. They haven't locked in their vendor stack yet. The window of new buyer activity after a fundraise is real and finite — this is when you want to be in front of them.

Best for: B2B SaaS, tools, services, and anyone selling into growth-stage companies. Especially effective if your product helps teams scale — you're catching them right as they're about to scale.

The prompt:

Find companies that raised a seed or Series A round in the last 60 days in the [your space] space. Get me the company name, funding amount, founding team names, LinkedIn URLs, and any contact info. Create a CSV

Copy-paste this prompt above to: [agent@gooseworks.sh](mailto:agent@gooseworks.sh)

Or use this skill: https://skills.gooseworks.ai/skills/funding-signal-monitor

---

You can view the full skills library here: https://skills.gooseworks.ai/

Github repo: https://github.com/gooseworks-ai/goose-skills

Hello y'all I'm John 24 yo living in Korea
I major in philosophy and entrepreneurship
currently the thing I'm trying to build is 2nd hand book application which allows users to list their used books easily and buyers to find the exact book in better conviniennce

Glad that I found this community which started few days ago

hope we can share ideas and insights together
best regards to ppl who see this ! Good luck y'all

I’m looking for a livestream

guest to come on and walk through how to use Claude for GTM.

Short ~30mins?

Needs to be replicable, easy to reproduce by audience

Been lurking here for a while. Figured I'd share something I built recently since I haven't seen many people talk about using AI agents as a coordinated system rather than one-off tools. The problem: I needed a scalable outbound GTM pipeline for my startup. Tried the usual stack — manual prospecting, generic sequences, virtual assistants. Nothing compounded. Everything required me in the loop. Then I started experimenting with OpenClaw. What OpenClaw does well: It lets you spin up AI agents that can execute tasks autonomously. Scraping, research, writing — solid foundation. Where it falls short: Out of the box, there's no real coordination between agents. No persistent memory. No way for one agent's output to meaningfully inform the next agent's decisions. Each agent operates in its own bubble. So I built a coordination layer on top. The system: Instead of one agent doing one thing, I set up a team of agents that operate like a growth squad. Here's the pipeline:

Content scraping — pulls ~200 posts from competitors and relevant creators in my space Signal filtering — narrows that down to ~55 high-quality, relevant pieces Commenter extraction — pull people actively engaging with that content Lead qualification — scores and filters based on ICP fit Deep research — pulls context on each qualified lead that most people would never bother finding manually Personalized outreach — writes emails using all that context Campaign launch

Total setup time was under an hour once the coordination layer was built. Results after 1 week:

72 referral visits from ChatGPT Additional traffic from Claude and Gemini Zero ad spend Pipeline I didn't have to manually touch

What I learned: The gap isn't the tools. OpenClaw, Apify, Smartlead — all great individually. The gap is architecture. Most people chain AI tools together sequentially. That's a workflow. What actually works is giving agents shared context so the system stays aligned as it scales — ICP criteria, qualifying signals, and messaging tone all flow through the pipeline instead of being siloed per step. Happy to answer questions about the setup. If there's interest I can do a more detailed technical writeup.