# Content Machine > Turn a human origin - your own spoken or typed take on a topic - into a finished post, thread, or script that sounds like you and is not AI slop, via an idea sweep, a one-at-a-time interview, a draft in your voice, an editor-council score-and-revise loop, and optional repurposing. Use when the user says "content machine", "write this up", "turn this into a post/thread/newsletter", "help me post about X", or has a rough take they want published. Differentiator: the origin must be human words and the human owns the first and last mile - not an autonomous content generator. - Skill: `nightdreams-bat/content-machine` (Agent Skill, multi-file: 6 files) - Install (CLI): `npx skillmds@latest add nightdreams-bat/content-machine` - Raw SKILL.md: https://api.skillmd.com/api/skills/nightdreams-bat/content-machine/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: Nightdreams-bat (https://skillmd.com/u/nightdreams-bat) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/nightdreams-bat/content-machine --- # content-machine > Ported from Alex Lieberman's "content machine" (10X podcast). Two hard rules > from the episode: > 1. **The origin must be human.** "The number one difference between AI content > that's trash and AI content that's valuable is whether the origin is human." > The AI helps with everything *except* the part only you can do: your take. > 2. **The human owns the first mile and the last mile.** Pick the idea; give > your real words; approve the final. Everything between can be assisted. The way to guarantee it's not slop, regardless of how good models get: the AI never invents the point of view. It structures, sequences, and checks *your* words. New model tells get chased forever; a human origin doesn't. ## Layers ``` process layer (in git, per D:\ content repo) the pipeline steps + skills below personal layer (templates/, edited over time) voice.md + content-lessons.md ``` - **voice.md**: how you sound. Built one of 3 ways: (a) a voice interview the machine runs, (b) paste past writing / texts / posts, or (c) start from a borrowed voice and let feedback morph it. For this user: start from past YouTube scripts + transcripts. - **content-lessons.md**: after every run the user gives feedback; it's appended here; every future draft checks it first. This is the compounding part. ## Pipeline ### 1. Idea (human picks) Optional sweep for candidates: - internal: scan the last 7 days of the user's notes / vault / `context/log.md` for "spikes" - strong point of view, a story, emotional intensity, a lesson/framework, or unusual depth. - external: `/youtube`, WebSearch across Reddit / HN / X on the topic - what's already said, what's missing. Present ~10 candidate angles. **The user picks one.** The rest go to a backlog file (don't lose them). ### 2. Interview (one question at a time) Pick ONE interviewer persona from `personas/interview-panel.md` (Ferriss, Rogan, Larry King, Stern, Barbaro, Walters - each questions differently). It interviews the user about the chosen idea, ONE question at a time, pushing back on vague answers, for ~15-20 min. The user answers by voice (SuperWhisper) or typing. Capture every answer verbatim into a transcript markdown file - **these are the raw material; do not paraphrase them away.** ### 3. Research pass (optional, light) If the take needs grounding: the white-paper / primary source, current takes in market, contrarian angles, open questions only the user can answer. Feed back to the user if it changes their view. ### 4. Draft in the user's voice - Choose format (LinkedIn post / X thread / long post / newsletter / script / podcast promo). Load the matching format section from `templates/formats.md`. - Draft using the user's words from the transcript. **Refine flow and transitions; do not replace their phrasing with your own.** Model: Sonnet. - Check `content-lessons.md` before writing - don't repeat a flagged mistake. ### 5. Editor council (score -> revise loop) Run the draft past the personas in `personas/editor-council.md` (Housel, Urban, Puri, Eisenberg, Perell, + a slop detector). Each scores /10 with specific notes. - score < 9 -> one revision incorporating the notes -> re-score once. - score >= 9 -> done. - **One revision loop, not endless.** (Same rule as the review gate - recursive loops produce worse output and never ship.) ### 6. Repurpose (optional) Turn the finished piece into N derivatives (thread from a post, etc.). Each derivative goes through step 4-5 with its own format skill. Not a copy-paste. ### 7. Feedback -> lessons Ask the user what the machine did well / badly this run. Append to `content-lessons.md`. Polish a Fable or Opus pass only if the user wants it. ## Output to the user ``` IDEA: (backlog: N other angles saved) INTERVIEW: , questions, transcript at DRAFT: , EDITOR COUNCIL: (was ) - FINAL: ``` ## Anti-patterns - Generating the take instead of eliciting it. If the user didn't say it, it's not their content. - Paraphrasing the user's words into "better" phrasing - kills the voice. - Endless editor-council loops. One revision. - Publishing/scheduling without the user's explicit approval of the final text. - Losing the non-chosen ideas - always save them to a backlog.