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Platform-level AI engineering strategy, agent fleet management, deployment diagnostics, and model upgrade planning from the Head of Engineering at OpenAI's API Platform.
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インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
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SKILL.md を表示中
| name | Sherwin Wu: AI Engineering Leadership |
| description | Platform-level AI engineering strategy, agent fleet management, deployment diagnostics, and model upgrade planning from the Head of Engineering at OpenAI's API Platform. |
| version | 2.0 |
| format | completion-engine |
| workflows | 4 |
Sherwin Wu is Head of Engineering at OpenAI's API Platform — the infrastructure powering virtually every AI startup. Extracted from a deep-dive interview on Lenny's Podcast (17,768 words). His vantage point is unique: he sees deployment patterns, failure modes, and adoption curves across thousands of companies simultaneously. These aren't theories — they're empirical observations from building the platform the AI economy runs on.
| # | Workflow | Produces | Use When |
|---|---|---|---|
| ai | AI Strategic Foresight Roadmap | AI Strategic Opportunity & Obsolescence Report | Planning long-term AI product strategy or evaluating new market trends |
| agentic | Agentic Infrastructure Lifecycle | Agentic Operating System & Migration Blueprint | Building, managing, or upgrading a fleet of autonomous AI agents |
| ai | AI Adoption & Alignment Audit | AI Deployment Recovery & Communication Plan | AI initiatives are failing to show ROI or there is friction between engineering and product teams |
| ai | Production Hardening Sentinel | AI Production Reliability Blueprint | Moving AI from demo to production, building unattended AI pipelines, or after silent quality degradation |
7 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/sherwin-wu-ai-engineering/references/prompts-v2/01-ai-deployment-diagnostic.mdskills/sherwin-wu-ai-engineering/references/prompts-v2/02-n-order-opportunity-scanner.mdskills/sherwin-wu-ai-engineering/references/prompts-v2/03-agent-fleet-commander.mdskills/sherwin-wu-ai-engineering/references/prompts-v2/04-context-architecture-builder.mdskills/sherwin-wu-ai-engineering/references/prompts-v2/05-scaffolding-obsolescence-audit.mdskills/sherwin-wu-ai-engineering/references/prompts-v2/06-developer-to-pm-translator.mdskills/sherwin-wu-ai-engineering/references/prompts-v2/07-model-upgrade-migration-planner.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.
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