No-Code Agentist

Grok Bot turns one prompt into a full autonomous workflow 16 skills + 1 runtime, built to research, build, verify, and ship without manually switching between tools the playbook: > research: scan markets, search sources, extract signal, rank opportunities > build: write code, run browser workflows, structure data, create artifacts > verify: fact-check, test outputs, challenge assumptions > ship: merge approved work and return the final result each skill is one reusable module: → clear trigger → step-by-step instructions → structured output → automatic handoff to the next agent no micromanagement: Grok Bot can load the right skills, run them in parallel, preserve context between agents, verify the work, and keep going until the artifact is finished the runtime: → parallel agent execution → checkpoints and recovery → specialist handoffs → proof capture before delivery one prompt in a complete agent pipeline out

Zero video-editing skills → AI edited my entire 15-minute video. 🤯 I gave Claude a raw video and a prompt. That’s it. It automatically: → Found glitches and awkward parts → Cut out bad sections → Split and joined clips → Added transitions → Added animations → Removed parts that didn't work well → Found relevant stock images from the internet → Added those images directly into the video → Did green-screen editing → Put everything together The original video was ~15 minutes. Final video: ~12 min 17 sec. And I never touched a video editor. No timeline. No manually searching for images. No downloading stock footage. No dragging clips around. No manually adding transitions. I just gave it instructions and let the agent do the work. the interesting part is that Claude isn't just editing the footage - it can search for relevant visual assets and decide where they should be used. And you can take this much further with more advanced editing workflows. The biggest limitation right now? Your prompt. Give it vague instructions → you can get messy results. Give it a good editing brief → things get surprisingly powerful. “Here’s my raw video. Make it better.” We're getting closer to that workflow. 🎬 What I used: • Claude Opus 5.5 — medium effort • Palmier Pro • Wikimedia image-search MCP • Pixabay MCP server • Claude Code / MCP I connected the MCP servers to Claude so it could search for visual assets while editing. No traditional video editor was opened during the process.

Past workflows no longer apply in the age of AI. Here are five things I do different to boost my productivity: 1) Run prompts on an always-on machine Ensure you can access this machine remotely. This is ideal for long-running tasks, scheduling, and remote work. 2) Use automated / scheduled workflows I highly recommend the following scheduled workflows: - Client feedback (e.g., database) -> Human approval -> Agent -> Testing -> Release. - Error reports (e.g., GitHub issues or Sentry) -> Agent -> Testing -> Release. 3) Use Astra to evaluate Opus outcomes Models tend to favor their own output, so always use a different model as a judge. 4) Use T3Code The desktop app is excellent, and the mobile app makes it a seamless choice, especially when paired with an always-on machine. 5) Rarely read the actual code Since the introduction of Fable I'm no longer reading code. This generation of models write code that is good enough. What do you do different?

Your content gets likes but you cannot name which post closed your last deal. I am going to show you how to connect specific pieces of content to closed revenue in under 20 minutes a month. Last year I published consistently and still froze when a client asked a simple question. What revenue did content generate. I had traffic screenshots and engagement charts. I had no answer on pipeline, deals, or profit per article. And that is where most solo operators get stuck. They track the wrong layer. Views live at the top. Revenue lives at the bottom. The bridge is boring and practical. Every time a deal closes, you tag which articles that buyer saw. Title, platform, date, revenue, deals influenced. One spreadsheet. One consistent split rule when three pieces touch the same deal. That gap between correlation and proof is the whole game. Because once you have that sheet, you stop debating opinions. You can see blog drove five deals while X drove awareness with no pipeline. You can calculate average revenue per piece. You can spot that one case study quietly influenced more cash than ten viral posts combined. This is why most people never get the result. They publish more instead of scoring better. I now score every draft out of 100 before it ships. Voice, specificity, fresh language, length fit, engagement. Anything under 75 gets revised, not published. Quality goes up. Reporting gets honest. Budget decisions get easy. That is why I built the Content-to-CFO agent. The no-code skill file content-to-cfo-skill.md walks you through it with the Revenue Attribution Mapper prompt from 06_revenue_attribution_mapper.txt, and developers can run python content_to_cfo.py attribute --input content_deals.csv from content_to_cfo.py in cmd_attribute with a 75 point publishing gate. This Content-to-CFO agent is part of the AI Agency OS, a white-label library, from AI agents, skills, automation workflows, and prompt packs. Everything you need to bring in new customers, prove your content is making money, and find the leads your clients did not know they had.