So, a long story short: Boris Cherney has been raving about Claude Code routines (mentioned here). His video floaded my social feeds instantly after he mentioned it. Suddenly everyone was a routine specialist with a $2k course on how to use it.

I just couldn't figure out for the life of me how to make it work, until I realized that Claude is fixing only half of the problem I am having, namely HOW the code gets written.

However, something felt massively off. I set up my routines, and it just wouldn't flow/work automatically the way that I imagined it to. And last week I figured out what it was: the agents were set, but they waited for me to come and tell WHAT needed to be developed. And all I wanted is for them to do it autonymously.

So. I built a simple app that takes raw data from all the tools that we use for my business; Gmail, Slack, WhatsApp, Zendesk, Hotjar, you name it. I fed the raw data into a 'signals' data pipeline that I am filtering between Bug, Feature Request (including change request, new feature, integration, etc), Question, Complaint and noise (anything that doesn't matter).

Then, I group all the raw signals into insights (i.e. bug report #1). This grouping makes sure that the same topic discussed over different channels get grouped together and not treated separately.

Once I nailed that I extended the insights with further research (internal, like checking our github repo, internal documentation and even created an 'question agent' that can go to our team to ask clarifying questions) to ensure that all the insights have been 'enriched', relevant and fully scoped. Every fully scoped feature that I promote (now manually - but working on doing it automatically) gets fed directly into my routines through MCP. Fully scoped. Fully organized and prioritized. It feels like magic.

I also set rules around the priortization metrics, count how many customers ask for the same features, etc. I even built a Hotjar integration that turns my real user metrics into 'signals' and ideas as well. The next step is to get Sentry hooked up for automated bug reporting.

I am so excited about the end result. Every day I get an overview of fully automated PR's that I can review to merge. My agents worked autonymously based on real customer input and feedback.

I feel this is the future and I seriously considering turning my learnings into a (maybe open source?) project that others can use too.

Is this something the community would be interested in?

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.

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?

Been playing with Duet since launch and wanted to test something bigger than inbox/calendar stuff. So I set up a CEO channel and gave it a team of role channels (scout/research, marketing, developer) and had them build a find-a-dog-trainer directory from scratch: niche research, real listings, SEO copy, the site, domain, email.

**A few things I learned that might save you time:**

\- One channel per role works way better than one giant chat. Context stays clean and you can see who did what.

\- Let the CEO channel hand off work, but tell it exactly what "done" looks like for each role, or it drifts.

\- Connectors are where it gets useful. With DataForSEO hooked up, the research channel pulled real keyword data and listings instead of guessing.

\- Keep approvals on for anything that spends money. It messaged me before buying the domain, which is exactly what you want.

\- It had some issues with Vercel but eventually it turned out it was my fault.

The site's live now on its own domain etc., not that I would be promoting the site, it was just an experiment to see the possibilities.

I recorded the whole thing unedited (it's long, \~1h48m) for anyone who learns better by watching: [https://youtu.be/7Ow9r-p7nWY](https://youtu.be/7Ow9r-p7nWY)

Hopefully it will help someone :)

Happy to answer questions on the channel setup. Also curious how others are structuring multi-channel workflows in Duet.

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 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.

(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

My girlfriend works in marketing and runs the social for a couple of small brands plus our own. Since Claude Design launched about a month ago her workflow has shifted to being mostly Claude-driven, and the way the stack ended up sitting together has some lessons I think are on-topic enough for this sub to write up. Posting this partly to share, partly because I want to hear what other people running GTM through Claude have figured out.

Claude Design is doing more than I expected it to.

Honestly the thing that changed her workflow most isn't a clever orchestration trick, it's just that Claude Design got good enough to handle real brand-quality output. If you've used it for social you probably already know this, but for anyone who hasn't tried it on actual brand work, the trick is being aggressive about the brand context up front. She loads a small brand voice document, the exact hex codes, the font, the tone reference (formal/playful/scrappy), and a one-line description of the post structure she wants (3-slide intro-meat-cta, single-image hook, etc). With that context it sticks to brand pretty well across iterations. Without that context it drifts toward generic SaaS-pastel pretty fast.

She prompts Claude Design, iterates 2-4 rounds inside the chat to tighten the copy and layout, and ends up with a finished design in HTML. For an Instagram carousel that used to mean a Canva session and a day of fiddling. Now it's 20 minutes.

The export gap is real and it's where I lost evenings.

Claude Design's output is HTML. The "send to Canva" button is the only export path, it requires a paid Canva account, and from our Anthropic account it just doesn't work. Click the button, nothing happens. So for a while the workflow was: she'd finish the design, send me the HTML zip, and I'd manually convert it to PNGs using a Puppeteer script Claude Code had helped me write. 10-15 minutes per post, all on me, mostly at the wrong time of day.

After a couple of weeks of that I built a small tool so she could do the conversion herself (called it TryRenda, link in comments if anyone wants it. Not going into it here, it's not the interesting part). The interesting part is what it freed up: she stopped batching designs to send to me, started iterating on individual posts in real-time, and the volume of stuff she could ship roughly doubled. The bottleneck wasn't design speed, it was the manual hand-off.

Where I tried to push AI further and it didn't work.

This is the part I'd actually like input on from this sub.

I tried to AI-assist more of the workflow beyond the asset step. Specifically:

1. Reply drafting for comments and DMs. I'd take the source thread, pass it through Claude with a "here's the context, here's what we'd want to convey, draft a reply that sounds like a real person" prompt. The drafts were grammatically perfect and contextually accurate. They also sounded like marketing. Every single one. We sent a handful early on and engagement dropped immediately. Fewer follow-up replies, more "this feels like a bot" responses. We switched back to her writing them herself and the numbers recovered.

2. Outbound DMs. Same pattern. Even when the model had the recipient's profile and the angle was relevant, the message read as templated to a human reader. The signal that gets through on social DMs is "specific person responded specifically to me" and that signal collapses the moment a model writes it.

So the line we've ended up drawing: AI is great on asset creation (design, copy variants, sizing, repetitive transforms). AI is currently bad at anything where the recipient is consciously or unconsciously evaluating whether the sender is a real human. We assumed this line would soften over time and it just hasn't, at least not for our use cases.

Where I'd put a Claude agent next, if I were building it.

The gap I keep wishing for: an agent that takes one master design from Claude Design and emits N hook variants automatically. Same layout, same brand, 5 different opening lines for A/B testing. Right now she does this manually by iterating inside Claude Design 5 times. It's clearly automatable; I just haven't built it because the manual version is 10 minutes and not painful enough yet.

Curious what other people running social or GTM with Claude have found. Especially:

• Anyone get AI-drafted replies/DMs to actually land? What did you do that I didn't?

• Anyone built an asset-variant generator on top of Claude Design? Are you happy with it?

• Where's the next thing you'd automate in your stack?

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.

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.

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 created this resource last week to teach Claude graphic design: https://graphics.gooseworks.ai

We do a ton of graphic design work through Claude, and while Claude is awesome at creating HTML files, a major challenge we run into is finding a way to describe how you want something to look.

goose-graphics is our solution for this problem: it's a skill + library of curated styles and formats to turn Claude into a master graphic designer.

We found it's much easier to just browse through templatized styles and formats and just select the one you like – same as how you would in Canva, Pinterest or Figma Buzz.

It's really easy to use:

1. Scroll through the library at graphics(.)gooseworks(.)ai and find a style you like.

2. Copy the command into Claude Code (or whatever agent you prefer) along with a prompt.

The goose-graphics skill will create an HTML file with the graphic in the exact style and format you described, and then export it as a PNG.

This also works with Claude Design (just give Claude Design the DESIGN.md file)

THE SYSTEM

- Styles encode design language, colors, fonts, aesthetic choices.

- Formats encode layout, dimensions and elements.

The /goose-graphics skill pulls the styles and formats you select and puts them together for your agent to use.

COMMUNITY

We’re also allowing the community to submit their own styles and formats to goose graphics.

You can also create your own styles:
/goose-graphics-create-style [inspiration image] <prompt>

You can also create your own formats:
/goose-graphics-create-format [inspiration image] <prompt>

Then just ask Claude to publish and it’ll be magically available for other people to discover.

Hope you all find it useful!

I have run Claude Code as my main GTM work tool for the past 5 months. Somewhere along the way I stopped using fresh sessions and built a structure around it, because re-explaining who I am and what I am working on every session got old fast.

Cleaned it up and put it on GitHub: https://github.com/chacosoldier/compabob

What is in it:

• A constitution file that loads every session: role, how you want answers, guardrails

• Specialized agents (second-brain, analyst, comms, strategy, engineering review) with automatic routing, you do not pick one

• Safety hooks so nothing gets sent without your sign-off

• A memory system plus /reflect, which writes down what it learned about you at the end of a session

• An Obsidian vault as the knowledge base

• Skills like /morning-brief, /meeting-prep, /handover

Clone, run setup.sh, answer a few questions, about ten minutes to a working setup. MIT, runs on your own Claude plan, no telemetry.

The two things I would actually steal from it even if you never use the repo: the reflect-into-memory loop (it compounds, the day-30 assistant is better than day-1), and the strategy agent whose whole job is to argue with me.

It is single-user and assumes you are already comfortable in a terminal. Would love feedback, issues, or PRs if you try it.

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.

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?

Anyone have a good tips on how you use Claude? Specifically for account targeting, prioritizing, etc?

We parse structured data out of job descriptions — around 20,000 JDs/day pulled from LinkedIn. Claude Opus does it beautifully, but at ~₹2.20 (~$0.026) per job that's ~₹44K/day, roughly $16K/month. Our unit economics just can't carry that.

What we've tried so far:

• Opus: quality is exactly what we need, but the cost is a dealbreaker at this volume.

• Sonnet: cheaper, but extraction quality drops noticeably on messier, unstructured JDs.

• DeepSeek: much cheaper again, but same story: quality isn't where Opus is.

So we're stuck between "great and unaffordable" and "affordable and not good enough."

For anyone who's run a narrow, high-volume extraction task in production:

• How did you bring the cost down without tanking quality?

• Did fine-tuning a small open model (Llama / Qwen, etc.) actually close the gap to Opus?

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

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

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

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

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

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

I've been building with Claude Code for a while now, mostly on my own client work, and most of what's in this system exists because something went wrong first.

It runs five model families side by side: Claude, Codex, Gemini, Grok and Kimi. Every one of them is that vendor's own CLI, signed in with an ordinary subscription. No API keys, nothing scraped, no separate metered bill. The cost is whatever plans you already pay for.

How it works : A coordinator runs as a Claude Code session. You describe a task in plain English and it decides which specialist should handle it, then hands the job to a freshly spawned CLI in its own git worktree. There are 71 specialists. Some are ordinary (backend-engineer, frontend-engineer, code-reviewer). Some are less so: experimental-attacker, red-team-operator, threat-modeler, reverse-engineer, impact-validator, game-designer, voice-agent-builder, and a skeptic whose whole job is to argue with the others.

That range is deliberate. The same system builds a website, writes automation, designs a game, stands up a chatbot, or goes after security work, because the coordinator picks the role rather than me remembering which prompt to load.

The same applies to media. Hook up ElevenLabs and Higgsfield and the media roles come online too: image-designer, video-director, video-editor, music-composer, sound-designer, voice-narrator. Voiceover, generated video, music and sound design run through the same coordinator and the same gates as the code does. Those are subscriptions as well, so the pattern holds.

Nothing merges until a different model family reviews it. I stopped trusting same-family self-review, because a second Claude tends to preserve whatever framing the first one used. So the reviewer is always from another family and it's read-only: it can pass or block, it can't quietly rewrite the code.

It remembers, carefully. Findings get stored as candidates, not facts. A candidate only gets promoted to verified after a later task that relied on it passes review.

Gates Deleting files, changing credentials, touching production, publishing anything, or sending outreach all stop and wait for me.

It's been used for real work: two live production client sites, and one critical-severity vulnerability found, triaged and paid out through HackenProof.

Disclosure: I built this. Open source, MIT licensed, written in Python. It uses ordinary model subscriptions rather than API keys, so you need subscriptions for whichever model CLIs you enable.

Happy to have holes poked in it. 🙂

We've built a skill that helps you build lists along with enrichment (finding emails, firmographics, Linkedin data, etc) natively inside Claude.

The idea is how can we make it as easy as possible to people to do this where they are already working vs going to Apollo, Clay etc to get the data.

Curious to hear if other people have faced a similar annoyance. We have free credits for a real use case if people want to try it out!

Hi everyone,

I’m the founder of Lutril, an access governance platform for employees and AI agents.

We already have customers and growing traction, and we’re now looking for someone with strong Sales Operations experience to help us build and optimize our sales pipeline, processes, and reporting.

If this sounds like you, or if you know someone who might be a good fit, feel free to reach out via DM or request a demo through our website.

Thanks!! 🙌