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Michael Margolis's (UX Research Partner, GV) bullseye customer research system — find the comically-narrow subset of your market most likely to adopt, then validate with 5 interviews, 3 comparison prototypes, in 1 day
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| name | michael-margolis-user-research |
| description | Michael Margolis's (UX Research Partner, GV) bullseye customer research system — find the comically-narrow subset of your market most likely to adopt, then validate with 5 interviews, 3 comparison prototypes, in 1 day |
| version | 2.0 |
| format | completion-engine |
| workflows | 3 |
| source | claude.ai export 2026-07-01 |
Michael Margolis was the first UX Research Partner in venture capital (GV, since 2010) and has run 300+ hands-on research sprints across biotech, healthcare, security, fintech, and consumer — after compressing 30 years of ethnographic research technique through Walmart.com, Gmail, and the Design Sprint. His system replaces months of guessing with the "five and three in one" formula: five bullseye customers, three simple comparison prototypes, one day of team-watched interviews. The output is not a report — it's a team that has seen the truth together and knows exactly what to do next.
| # | Workflow | Produces |
|---|---|---|
| 01 | 01-define-bullseye-customer.md | Bullseye customer definition (~7 concrete attributes: inclusion, exclusion, triggers) + screener questionnaire |
| 02 | 02-build-comparison-prototypes.md | Three distinct flat prototypes, each a different value-prop "recipe" spread across the variables you need to test |
| 03 | 03-run-research-sprint-day.md | Interview guide + watch-party operating plan + debrief/takeaways system for the one-day sprint |
4 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/michael-margolis-user-research/references/prompts-v2/bullseye-customer-definition.mdskills/michael-margolis-user-research/references/prompts-v2/comparison-prototype-set.mdskills/michael-margolis-user-research/references/prompts-v2/sprint-interview-guide.mdskills/michael-margolis-user-research/references/prompts-v2/watch-party-takeaways-system.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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