Share your best AI workflow. We could show it to 2M+ people.

Yesterday, I built a social media workflow in Awish for a business that wanted to post more consistently without managing three platforms manually. The idea was simple: let Awish understand the business first, then use that context to plan different content for Instagram, LinkedIn, and X instead of reposting the same thing everywhere. I built the entire workflow by describing what I wanted in the Awish chat. Step-by-step: 1. I opened Awish and wrote: “Analyze this business from its website. Based on what the company does, its audience, products, and positioning, create separate content plans for Instagram, LinkedIn, and X. Generate the posts, publish them automatically at the times I choose, analyze how each post performs, and send me the important insights on WhatsApp. I also want to be able to manage the workflow from WhatsApp.” 2. Awish analyzed the website, understood the business, and planned the complete content workflow. 3. I connected the company’s Instagram, LinkedIn, X, and WhatsApp accounts and approved the plan. 4. Awish created a different content strategy for each platform instead of copying the same post everywhere: more visual content for Instagram, business-focused posts for LinkedIn, and shorter, conversation-driven posts for X. 5. I chose the publishing schedule, and Awish started creating and publishing the posts automatically at those times. 6. After publishing, Awish tracked performance and analyzed which topics, formats, and messages were getting the strongest response on each platform. 7. Awish sent me short WhatsApp updates with the important insights, such as which post performed best, what topic was gaining traction, and what it recommended changing next. 8. I could also manage the automation directly from WhatsApp by asking what was scheduled, changing a publishing time, requesting another post, pausing publishing, or asking for the latest performance summary without opening Awish. The useful part for me is that this is not just automatic posting. The workflow starts by understanding the business, treats each social platform differently, learns from what gets published, and keeps the whole process manageable from WhatsApp.

I make content—posts, articles, and videos—and it brings people to my site. But most visitors would skim for 30 seconds and leave, and nobody used my contact form. So I built an AI version of myself. I trained it on my own content, including my posts, articles, notes, knowledge, and the way I explain things. It learned not only what I know, but also how I talk and think. Now it lives on my site and chats with visitors in my voice. As it chats, it identifies who’s a real lead and sends those people to me with the full story. Visitors can ask questions, get answers in my voice, and follow up as deeply as they want. Step-by-step: 1. I collected my content, posts, articles, notes, knowledge, and explanations. 2. I trained an AI version of myself on that material, including how I talk and think. 3. I created a link and placed the AI on my site. 4. Visitors ask questions and receive answers in my voice. 5. The AI identifies real leads and sends them to me with the full conversation and context.

Content Repurposing System: one transcript into 4 platform-ready posts in 18 seconds. THE PROBLEM Every video cost me two hours turning it into posts for Twitter/X, LinkedIn, Skool and Instagram. The writing wasn't hard. The context switching was. Four platforms, four tones, the same idea rewritten four times. Built during the Skool x Hostinger n8n hackathon, Dec 2025. Still my daily workflow. STACK: n8n on a Hostinger VPS, OpenAI, Google Sheets. 13 nodes. HOW TO BUILD IT Manual Trigger. Swap for a Form or Drive trigger if you want it hands-off. Set node "Set Transcript", one string field: transcript. Leave a real sample transcript in the default value so anyone can hit execute and see output immediately. IF node "Check Transcript", two conditions with AND: transcript is not empty, and {{ $json.transcript.length }} > 50. False branch goes to a Stop and Error node. Four minutes of work. It's why I've never burned 5 API calls on a blank field. OpenAI node "Analyze Content", model gpt-5.4-mini, Simplify Output OFF: You are a content analyst. Analyze this video transcript and extract: Main topic/theme 3-5 key insights or takeaways Target audience Tone (educational, motivational, technical, etc.) Any specific examples, statistics, or stories mentioned Transcript: {{ $json.transcript }} Provide your analysis in a structured format. I don't send the transcript to four writers. I send it to one analyst first, and all four writers read that analysis. This lifted quality more than any prompt tweak: the posts share one reading of the material instead of each model guessing. The stronger model goes here for the same reason. Wrong analysis, four wrong posts. 5-8. Four generators, all gpt-4o-mini, Simplify Output OFF, all wired from Analyze Content's single output. Each pulls the same two inputs: Content Analysis: {{ $('Analyze Content').item.json.choices[0].message.content }} Original Transcript: {{ $('Set Transcript').item.json.transcript }} Then its own rules. Twitter (temp 0.8): hard hook, under 280 chars, one insight, no hashtags. LinkedIn (0.7): 150-250 words, 2-3 line paragraphs, ends on a question, no hashtags. Skool (0.8): 100-200 words, always a numbered list of actionable takeaways, ends by inviting replies. Instagram (0.8): 125-175 words, 5-8 hashtags, plus a detailed "Visual suggestion:" for a designer or image model. LinkedIn needed a tone block after v1 read like a press release: talk like you're with a colleague over coffee, use I and you, never "leverage", "in today's landscape", "fast-paced". Naming banned words works. "Write conversationally" does nothing. Merge node "Collect All Posts", 4 inputs, one generator per index. Aggregate node, mode All Item Data. Puts all four posts on one row instead of four. Code node "Format Output". Reads each generator by node name, each in its own try/catch, so one failure still writes a row. Builds a readable timestamp, a 100-character transcript_preview, and status: 'Generated'. Google Sheets, Append Row, Map Automatically. THE SHEET Seven columns, headers in row 1, named to match the Code node exactly: timestamp, transcript_preview, twitter_post, linkedin_post, skool_post, instagram_post, status. Status is a dropdown: Generated > Reviewed > Scheduled > Published. The system drafts, I decide. FOUR THINGS THAT COST ME HOURS Turn Simplify Output OFF on every OpenAI node. Every expression reads choices[0].message.content, which only exists in the raw response. Leave Simplify on and you get four empty columns with no error explaining why. No title row above your headers. I had a merged title in row 1, headers in row 2. Map Automatically stopped seeing my columns and silently built duplicates beside them. Extend data validation down the whole column (G2:G1000, not G2). I set the dropdown on one cell and every appended row arrived as plain text. Kill markdown in the prompt, not after. I wasted an evening regex-stripping ** in the Code node. The fix was upstream: tell Skool and Instagram plain text only, CAPITALS or "quotes" for emphasis. Zero artefacts since. Post-processing cleanup means your prompt is underspecified. RESULT Two hours per piece became 18 seconds of runtime plus 5-10 minutes of review. I tested 20 transcripts across five content types (tutorial, interview, news, explainer, motivational). Most were publishable with light edits. The failure mode never changed: rambling transcript, vague analysis, four vague posts. Which is exactly why the analyst node gets the better model. Budget 30 minutes to rebuild. The prompts are the product. Copy the structure, then rewrite the platform rules in your own voice. That's what decides whether it sounds like you or like everyone else.

I turned tedious Google Business Profile spammer tracking into a Claude Skill. It organizes the research and puts all findings into an XLS file that can be submitted through the Google Business Redressal form and the GBP forum for escalation by a Google Product Expert for Google review. Step-by-step: 1. I created a list of suspected spam profiles with each business name and Google Maps URL. 2. I asked Claude to examine each profile, determine its website, and identify any relationships between the domains. 3. I asked Claude to identify other businesses operating at the listed addresses. 4. I asked Claude to determine whether each profile is listed with the country’s primary Google data provider or providers. 5. I asked Claude to determine whether each profile is listed in a state or government registry. 6. I asked Claude to analyze commonalities across the profiles’ reviews, descriptions, and photos. 7. I asked Claude to identify patterns indicating whether the profiles are fake or real and whether they are related in some way. 8. I had Claude compile all findings into an XLS file for submission through the Google Business Redressal form and the GBP forum.

I use Claude Cowork and Meta MCP to map my existing ad creative data and find winning combinations. Once the data is mapped, I can usually identify one concept that wins more often than the others, has absorbed more spend, and still maintains an efficient CPA. That is the winning concept, and it is where I should focus 60% to 80% of my production time. Step-by-step: 1. I connect Claude Cowork to Meta MCP and Google Drive. 2. I place the brief for every ad I published in the past 365 days into one Google Drive folder. Each brief includes the script, format, offer, and persona it was written for. 3. I prompt Claude to work through the Drive folder and build a spreadsheet tagging every ad by persona, angle, offer, and format. 4. I prompt Claude to pull each ad’s spend and CPA through Meta MCP and add those metrics to the spreadsheet. 5. I ask Claude to turn the spreadsheet into a flowchart in which persona branches into angle, angle branches into offer, and offer branches into format. Each branch includes its spend and CPA. After the data is mapped, I use the flowchart to identify the winning concept and prioritize it in production.

I’ve been using the Awish app I built less as an automation builder and more as a way to solve specific business problems people bring me. One hotel client, Aurelia Bay Hotel, had an interesting marketing problem. They could see travel trends across search, Instagram, YouTube, website traffic, and ads, but connecting those signals early enough to spot a real campaign or guest-experience opportunity was difficult. I built an automation for them with Awish and gave it to them to test. Step-by-step: 1. I opened the Awish chat and wrote: “For Aurelia Bay Hotel, monitor Google Trends, YouTube, Instagram, Google Analytics, and Google Ads every day. Find rising interests around Dubai travel, staycations, family trips, wellness, dining, events, and hotel experiences. Cross-check those trends with social engagement, website behavior, and campaign performance. Rank the strongest opportunities, send them to the marketing team in Microsoft Teams for approval, and create a campaign or guest-experience opportunity brief in SharePoint for anything they approve.” 2. Awish understood what I wanted, planned the complete workflow, and selected the apps required for each step. 3. I reviewed the plan, connected the client’s accounts, and approved it. 4. Awish added Microsoft Teams as the human approval step and SharePoint for the final opportunity briefs. 5. Once everything was connected, Awish built the workflow and started running it daily. 6. It now cross-checks trend growth, social engagement, website behavior, and ad performance. When it finds a strong opportunity, the marketing team gets the evidence in Teams. If they approve it, Awish creates the full campaign or guest-experience brief in SharePoint. The useful part is that a trend isn’t treated as an opportunity just because it is growing on one platform. Awish checks whether the same signal is appearing across the business before recommending action. Do you have any time-consuming, repetitive tasks or problematic aspects in your job? I would be happy to develop an automation solution for you as well.

We're always looking for A/B test ideas for our landing pages and new ways to improve conversion rates while creating a better user experience. Recently, I tried an interesting AI workflow after coming across a public CRO skill on Twitter. I installed it in Claude Code and then fed Claude screenshots of our landing page along with behavioral data from Microsoft Clarity, including scroll depth, heatmaps, and which buttons people were clicking most. Using the CRO scorecard framework and the Clarity data, Claude generated a detailed analysis of the page and recommended five A/B tests we should prioritize, along with the reasoning behind each one. The insights were genuinely useful, and we have already taken action by launching one of the recommended tests. Step-by-step: 1. I installed a public CRO skill in Claude Code and used its scorecard as the evaluation framework. 2. I supplied screenshots of the landing page along with Microsoft Clarity scroll depth, heatmaps, and click data. 3. I asked Claude to analyze the design and behavior together rather than treating screenshots and analytics separately. 4. I had it recommend five A/B tests and explain the evidence and reasoning behind each one. 5. I prioritized the list and launched one of the recommended tests.

I built an autonomous, end-to-end AI Sales Development Representative (SDR) engine entirely in n8n. On a scheduled trigger, the agent calculates targeting parameters, reads long-term CRM memory to avoid duplicate outreach, searches for and qualifies prospective leads, scrapes company websites for buying signals, drafts tailored outreach emails, writes structured relational data to PostgreSQL, and reports execution summaries through Telegram—with zero manual intervention. The system is currently deployed in production for a B2B agricultural export business, generating qualified international wholesale leads on a recurring schedule. Most AI automations rely on simple linear scripts or break down when handling complex agentic tool workflows. This system addresses three common failure points: - High API costs: Re-sending large system prompts and tool schemas on every agent iteration drains tokens. - Context blindness: Agents without memory of previous contacts can send duplicate outreach. - Database crashes: Agents may hallucinate ENUM values or fail to insert nested one-to-many arrays into relational tables. The workflow uses a Cloudflare-proxied Claude Sonnet 4.6 model with prompt caching, persistent CRM memory reads, and a fault-tolerant parallel database-write architecture. The stack includes n8n as the orchestrator; Claude Sonnet 4.6 through a Cloudflare Worker proxy as the LLM core with ephemeral prompt caching; PostgreSQL for CRM contacts, intelligence, and outreach tables with custom ENUMs; SerpAPI for prospect discovery; Firecrawl for website content extraction; and Telegram for execution reporting. The workflow exposes these tools to the n8n agent: - `read_relationship_memory`: Read-only SQL access to historical contact and outreach data, preventing duplicate prospecting. - `Lead_Finder`: Searches for and identifies target prospects by country and sector. - `Scrape_Website_Content`: Extracts website content, buyer-intent signals, and objections from discovered domains. - `write_relationship_memory`: Writes leads, intelligence facts, and drafted emails to Postgres in one resilient call. Step-by-step: 1. A Schedule Trigger feeds a JavaScript “Country Calculator” node that resolves the day’s targeting parameters—region and industry focus—using ISO week rotation. This cycles outreach across markets automatically. 2. The AI Agent connects to an OpenAI Chat Model node whose Base URL points to a custom Cloudflare Worker. The worker translates OpenAI-formatted requests into Anthropic’s Messages API, enabling Claude Sonnet 4.6 while injecting ephemeral cache-control headers into the system prompt and tool definitions to reduce repeat-token costs. 3. Before researching, the agent calls `read_relationship_memory` to check relationship status and outreach history, preventing duplicate contact attempts. 4. `Lead_Finder` searches target sectors in the day’s region and returns seven filtered candidates. `Scrape_Website_Content` then visits each domain, extracts clean page text, and surfaces offerings, value propositions, and likely objections. 5. The workflow writes nested one-to-many data—multiple facts and one outreach log per contact—without item duplication or ENUM crashes. The tool schema requires a strict JSON array with exact ENUM string choices spelled out in the description. 6. A sub-workflow triggered by “When Executed by Another Workflow” splits the array, then flattens nested `contact.*` fields to root keys using JavaScript. 7. An upsert query, `ON CONFLICT (email) DO UPDATE`, writes the contact, increments `email_count` for repeats, and returns `contact_id`. 8. A “Re-attach Context” node merges `contact_id` back with the original intelligence array and outreach payload because n8n strips extra data through single-row database nodes. 9. Two parallel branches run: one inserts the outreach log with `ON CONFLICT DO NOTHING`, while the other splits and inserts each intelligence fact with defensive ENUM sanitization. This eliminates crashes and duplicate rows during retries. 10. The agent’s final output triggers a Telegram message summarizing the discovered leads, extracted facts, and drafted emails, sent directly to the operator’s phone. The result is a production-grade, self-healing AI outbound pipeline running with zero manual intervention. It maintains CRM data integrity, avoids duplicate outreach, and uses prompt caching to keep LLM costs low at scale.

I open Pulse and click Pull AM Brief or Pull PM Brief, search for a topic, or paste in an article link. That triggers a call to Claude, which scans for the five most relevant marketing, AI, and CPG stories that day. I choose one story and expand it into a full seven-slide carousel. Claude writes all the copy according to our brand’s specific editorial rules. I can then generate AI imagery for each slide. The final slide gets a custom scene generated with our actual logo built into it, so it feels native to the story instead of looking like a generic closing card. Once the carousel is built, I run it through a scoring system that evaluates factors such as how surprising the opening is and how likely people are to share it. If a slide scores low, the system automatically rewrites it until it passes. Finally, it generates the caption, a call to action, and a LinkedIn post, and I download everything ready to publish. Step-by-step: 1. I open Pulse and select Pull AM Brief, Pull PM Brief, a topic search, or an article link. 2. Claude scans for the five most relevant marketing, AI, and CPG stories of the day. 3. I select one story and expand it into a seven-slide carousel. 4. Claude writes the carousel copy according to our brand’s editorial rules. 5. I generate AI imagery for each slide, including a custom final-slide scene with our actual logo built into it. 6. I run the carousel through the scoring system, which evaluates the opening’s surprise and the likelihood that people will share it. 7. The system rewrites any low-scoring slide until it passes. 8. I generate the caption, a call to action, and a LinkedIn post, then download everything ready to publish.

As a business owner, I used to check performance data across Google Analytics 4, Google Search Console, and Google Merchant Center by logging into three separate portals, navigating complex submenus, and exporting raw CSVs. To solve this, I built an automated workflow that connects my AI coding assistant, Google Antigravity, directly to all three Google platforms through a Google Cloud Service Account. Instead of manually clicking through dashboards, I can ask natural-language questions in plain English, such as "Did yesterday's email send generate traffic or phone bookings?" or "Are any products disapproved in Google Merchant Center?" I then receive instant, cross-platform analysis in seconds. Step-by-step: 1. I created a Google Cloud Service Account by going to Google Cloud Console (console.cloud.google.com) > IAM & Admin > Service Accounts > Create Service Account. I named it `ai-marketing-assistant`, copied the generated service account email address, such as `ai-marketing-assistant@<project-id>.iam.gserviceaccount.com`, and downloaded the JSON private key file to my local project folder. 2. In Google Cloud Console > APIs & Services > Library, I enabled the three required APIs: - Google Analytics Data API - Google Search Console API - Content API for Shopping (Google Merchant Center) 3. In Google Analytics > Admin > Property Access Management, I added the Service Account email address with "Viewer" permissions and copied my numerical GA4 Property ID. 4. In Google Search Console > Settings > Users and permissions > Add User, I added the Service Account email address with "Full" or "Restricted" access. 5. In Google Merchant Center > Settings & Tools > Account Access > Add User, I added the Service Account email address with "Standard" or "Admin" access and ensured that "Content API access" was enabled. 6. In my local AI environment, such as Google Antigravity or a custom Python environment, I authenticated using standard Google client libraries (`google-oauth2`, `googleapiclient`, `google-analytics-data`) connected to the downloaded JSON key file. 7. Anytime I launch a marketing campaign, want an SEO audit, or need to check product feed health, I ask my AI assistant a plain-English question. 8. The AI executes live API queries against GA4, Search Console, and Merchant Center, cross-references website traffic with bookings, and delivers an instant report.

I connected Claude Code to Apify's API to scrape high-performing content from IG/TikTok for ad creative inspiration, and to ElevenLabs' API to automatically transcribe videos — so I can analyze not just visuals, but the exact hooks, pacing, and language top creators are using. From there, I developed a scriptwriting system that takes rough thoughts on a hook and angle and turns them into ad scripts, drawing inspiration from winners and applying proven copywriting principles. After generating, it also grades itself against a 12-point rubric. If it doesn't score at least 90%, it rewrites until it does. I then feed it back in and say, "This is the final version." It analyzes the changes I made and updates its understanding of my style. Step-by-step: 1. I connected Claude Code to Apify’s API for collecting high-performing Instagram and TikTok content. 2. I connected ElevenLabs’ API so the workflow could automatically transcribe each video. 3. I analyzed the winning examples for their hooks, pacing, visual patterns, and exact language. 4. I fed the system a rough hook and angle and had it draft ad scripts using those proven patterns. 5. I created a 12-point grading rubric and required a rewrite whenever a script scored below 90%. 6. After making my own final edits, I fed the approved version back into the system so it could learn my style.

I kept struggling to stay consistent with social media for my own projects. Writing captions, choosing hashtags, designing graphics, and scheduling content across platforms was taking hours every week—time I would rather spend building. So I built AutoKonnekt. You give it one sentence or a website URL, and it generates a full month of on-brand social posts, including captions, hashtags, and AI-generated images. It then schedules them across Instagram, Facebook, LinkedIn, TikTok, Pinterest, X, and Threads. The hardest part was getting the AI to sound like a specific brand’s voice instead of producing generic marketing copy. That took a lot of iteration on the prompting side. It’s live now with a free plan if anyone wants to try it: https://autokonnekt.com I’d love to hear what the community thinks. For those of you managing social media manually, which part takes the most time? I’m trying to figure out what to build next. Step-by-step: 1. I enter one sentence describing the project or provide a website URL. 2. AutoKonnekt generates a month of on-brand social posts with captions, hashtags, and AI-generated images. 3. I use the generated content across Instagram, Facebook, LinkedIn, TikTok, Pinterest, X, and Threads. 4. AutoKonnekt schedules the posts across those platforms. 5. I iterate on the prompting to make the content sound like the specific brand instead of generic marketing copy.

I have been testing Perplexity Computer, and it really makes you rethink a lot of workflows. It has been particularly useful for surfacing interesting and viral social content across X and Reddit, with internet search abilities that seem much more capable for quick canvassing of different platforms. I have also used it for clipping and captioning video content for social, with Computer able to find, transcribe, give recommendations on the most viral quotes/sections, and caption in just minutes — a wild process that would normally take me (with little video experience) much longer and have to leverage a ton of different apps to make happen. Step-by-step: 1. I used Perplexity Computer to canvass X and Reddit for interesting social content with signs of viral momentum. 2. I narrowed the results to the posts and videos worth developing further. 3. I had Computer find and transcribe the source video content. 4. I asked it to recommend the strongest quotes and sections for short-form clips. 5. I used the same workflow to clip and caption the selected sections in minutes.

It starts with one scheduled job that runs at 6 a.m., before I open anything. Every automated task in my business writes a single line to a shared table when it finishes. That log records which job ran, when it ran, and whether it succeeded. It is the foundation of the whole system because I am checking for silence instead of waiting for something to alert me. Step-by-step: 1. The 6 a.m. job reads the log and lists anything that was supposed to run in the last 24 hours but never checked in. 2. It pulls each client’s spend and traffic directly from the ad and analytics platforms. 3. It calculates where spend should be for that day of the month: monthly budget divided by the number of days in the month, multiplied by the number of days elapsed. Anything more than about 15 percent off pace is flagged. Accounts that return an authentication error are also flagged, which helps catch a dead access token before the client does. 4. It sends all of that to Claude along with a short brief on each client. Claude writes one email with broken items at the top in plain English, followed by a single line for each account that is fine. 5. The same table feeds the live dashboards clients use, so a monthly report is a link I already have rather than something I need to assemble. To rebuild this today without much code, I would start with the smallest version: Step-by-step: 1. Create a spreadsheet where each automation logs a row. 2. Add one scheduled script that emails me whatever is missing. 3. Use a Claude prompt to turn that raw list into the email. 4. Add the spend checks once the heartbeat is working. Alerting on silence is the part that does the real work.

An AI agent scrapes book marketplaces for recent releases, crawls the web for the corresponding authors’ email addresses, and generates customized marketing pitches based on the authors’ own book descriptions. Just kidding—I’m an author, not an agency. I receive about half a dozen such email pitches every day. I added a spam rule that looks for the text "your book" and sends those messages to spam because my email host obviously isn’t using a smart enough AI-based spam filter. Step-by-step: 1. An AI agent scrapes book marketplaces for recent releases. 2. It crawls the web for the corresponding authors’ email addresses. 3. It generates customized marketing pitches based on the authors’ own book descriptions. 4. I receive about half a dozen of these pitches every day. 5. I use a spam rule that looks for the text "your book" and sends the messages to spam.

Claude Cowork has been a huge help in creating growth forecasts for our paid media channels. I just plug in our metrics, tell it what goal I'm trying to achieve, and it's able to create an Excel sheet for me with exactly what I need. This saves me a bunch of time because I can create multiple scenarios just by chatting with Cowork. Step-by-step: 1. I gathered the paid-media metrics and the business goal the forecast needed to model. 2. I gave the inputs to Claude Cowork and asked it to build the forecast directly in Excel. 3. I reviewed the structure, assumptions, and outputs of the generated sheet. 4. I changed the assumptions conversationally to create additional scenarios. 5. I compared the scenarios without rebuilding the spreadsheet model by hand each time.