菜单
Luke Alexander's AI service business system — pick the service layer of the AI stack, sell outcomes (never "AI"), structure performance deals with setup fee + percentage of upside, and deliver the work invisibly with AI via structured context profiles.
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| name | luke-alexander-ai-business |
| description | Luke Alexander's AI service business system — pick the service layer of the AI stack, sell outcomes (never "AI"), structure performance deals with setup fee + percentage of upside, and deliver the work invisibly with AI via structured context profiles. |
| version | 2.0 |
| format | completion-engine |
| workflows | 3 |
| source | claude.ai export 2026-07-01 |
Luke Alexander trained ~5,000 sales reps, generated close to $10M personally online (Closer Cartel, IPO/growth-partner offers), and now builds Kendo AI, an enterprise AI sales-training startup. His system: build a service business at the top of the AI stack (LLMs → software → services), solve one of three evergreen problems (sales, marketing, automation) for the niche where money flows fastest, price on performance instead of retainers, and let AI do the delivery while the tech stays invisible. The operating discipline underneath is the "boring" layer — structured context profiles and memory management that make AI output 100x better.
| Workflow | Produces | Use When |
|---|---|---|
01-design-ai-service-offer | A defined AI service offer (who/what/price-logic/fulfillment) positioned on outcomes | Starting or repositioning an AI-powered service business |
02-structure-performance-deal | A setup-fee + upside-percentage deal structure with conversion-lever math and ROI proof | Pricing a client engagement or pitching a growth-partner deal |
03-build-context-profile | A structured context profile + handoff protocol for consistent elite AI delivery | Setting up AI to do the fulfillment work reliably across sessions |
3 deterministic practitioner prompts — each carries an Output Contract, Output Skeleton, and Quality Gate. When a deliverable matches one, Read it and honor its contract instead of improvising the output shape.
skills/luke-alexander-ai-business/references/prompts-v2/build-context-profile.mdskills/luke-alexander-ai-business/references/prompts-v2/design-ai-service-offer.mdskills/luke-alexander-ai-business/references/prompts-v2/structure-performance-deal.mdThe /generate front door — in-house, pay-as-you-go creative generation (Higgsfield replacement). One command for image, video, and audio: routes each request to the right model recipe (skills/generate/models/*.json), defers to binding creative_router lanes (people → fal-people, style-family → fantastic-posters; Higgsfield fully retired 2026-08-06), quotes paid video before running, honors prompt-level budgets ("total budget $3"), runs comparison batches across models, logs every prompt beside its asset, and auto-refreshes the Asset Command Center (/assets-board). Use when the user says "generate", "make an image/video/voiceover", "create ads/assets/creative", names a model (recraft, kling, seedance, nano banana, gpt image), or asks for a multi-model comparison. Source: RoboNuggets 9C4TRbucmhQ, rebuilt on our fal-first stack.
The Edit Bay — in-house conversational video studio (VOX-style explainers, documentaries, shorts, LinkedIn video). Transcript-driven cutting, B-roll ladder, motion graphics as code, captions, music, publish-ready packages — all agent-operated, free-first. Use when Farrice says "edit this video/footage", "make an explainer/documentary/short", "cut this recording", "add B-roll/captions/music", "turn this essay/post into a video", or any video production ask. Front door for three modes: talking-head, VO-only, zero-camera (his recorded VO + assembled visuals — NO voice clone, NO TTS narration, his ruling 2026-08-06). Source: Brad Bonanno agentic edit pipeline (extractions/brad-bonanno-edit-bay/), rebuilt on the house stack: WhisperX local + edit_bay.py ffmpeg assembly + HyperFrames/Remotion graphics + /generate (fal) — NO Higgsfield (retired 2026-08-06).
Architects technical explainer videos using Brad Bonanno's structural patterns — survives short attention spans, communicates complete systems (not just teasers), and functions as a channel-building artifact (one video pulls subscribers, not just views). Use when planning a long-form technical explainer (5-20 min) for YouTube, structuring an explanation of a complex system the user knows but can't teach yet, balancing depth vs accessibility for a technical audience, or building an evergreen video meant to compound subscriber growth over years rather than spike then die. Trigger proactively whenever the user says "explainer video", "technical explainer", "how do I explain X", "long-form YouTube", "evergreen video", or has a complex system that needs to land with a non-expert. For Instagram-format shareworthy content use brock-johnson-shareworthy-content; for short-form video patterns use a TikTok/Reels-specific skill.
基于 SOC 职业分类