A 30-minute video is too long for one conversation. This guide walks the 8-step Skill that locks the narration and one look, then gives each 3-minute segment to a fresh sub-agent, five at a time, and polishes the cut in an empty-context session.
The folder structure behind my AI agent team, the Herdr setup where Grok Build, Claude Code and Kimi Code work side by side, and the two live tasks from the video: an article published by voice and a three-model front-end test.
Eight folders of plain Markdown, a CLAUDE.md index in each, and seven prompts that make your agent interview you. Every prompt, template and command from the video, in order.
Assumes production experience
20% of companies capture 75% of the measurable AI value while running the same models as everyone else. The gap is operational: a three-layer architecture, five maturity levels, and the reliability math that makes human gates non-negotiable in any workflow automation build.
Your agent starts every session from zero because nothing about how you work is written down. An 8-layer knowledge base — brand, memory, workflows, tools, specs — plus two full production demos where only the knowledge base changes and the output changes completely.
One canonical knowledge store any MCP-aware AI tool can query: three independently replaceable tiers, eight industry templates, and per-person access you revoke with a single command. Built so the knowledge stays with the business when the person leaves.
Built a skill Wednesday night, opened a different Mac Thursday morning, and it was gone. The full-mesh Syncthing setup that keeps the config behind my workflow automation software identical on 4 machines - .stignore rules for 360,000 files, an onboarding SOP, and the mistakes I made.
Vector databases are overkill. A file-system knowledge base with multi-level CLAUDE.md routing finds any file in a 1,000-file system in three seconds. Here is the complete architecture — and why structure beats search when documents have to be findable on demand.
Five Claude Code Skills, one unattended content pipeline: persona cloning, sourcing, drafting, image generation, and scheduled publishing. A workflow automation pattern built on single-responsibility design and file-based handoffs — each step reads exactly what the last one wrote.
Debugging ate 60% of the build time across 20+ projects. The Quality Loop cut it to under an hour per Skill and dropped post-launch issues from 40% to below 10%: dual-phase detection, four-layer verification, and isolated SubAgent self-repair.
Delegated work without a receipt is not delegated, it is just moved somewhere you keep checking. Three ways to close the loop: Hooks for a quick script, Channels for two-way chat, and a Hermes MCP bridge for cross-device push, with config code for each.
A long conversation degrades; a plan held in a script does not. Split an objective into dozens of subtasks, run them in parallel, cross-verify, and return only validated conclusions. Six patterns, a decision table, and the cost controls workflow automation software needs before running unsupervised.
Subagents run well-defined tasks in isolated contexts so the main session stays clean. One dispatch rule: high intermediate noise, short final output. Start read-only, grant the fewest tools possible, run three real tasks. Copy-paste config plus the hidden costs in workflow automation software.
160,000+ Skills exist, so availability stopped being the constraint and selection took over. A SKILL.md that searches registries, the web, and GitHub in parallel, deduplicates, and ranks every candidate across six dimensions with install commands attached.
A written instruction depends on the model remembering; a checkpoint does not. Eight lifecycle moments where your script fires automatically, where the config lives, how to write one that blocks dangerous commands, why three is the ceiling, and how to debug workflow automation software hooks.
When I figure out how to get something real done with AI, you get the write-up — every step, including the parts that didn't work.