# AI Supply Chain Bottleneck Hunter > Use when the user wants a repeatable AI/photonics/semiconductor supply-chain research workflow inspired by Serenity (@aleabitoreddit) and Crux Capital, including bottleneck mapping, stack analysis, evidence gathering from reports/news/earnings, directional sector calls, and optional lower-market-cap candidate names only after the thesis is built. - Skill: `wesson9527/ai-supply-chain-bottleneck-hunter` (Agent Skill, multi-file: 3 files) - Install (CLI): `npx skillmds@latest add wesson9527/ai-supply-chain-bottleneck-hunter` - Raw SKILL.md: https://api.skillmd.com/api/skills/wesson9527/ai-supply-chain-bottleneck-hunter/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: wesson9527 (https://skillmd.com/u/wesson9527) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/wesson9527/ai-supply-chain-bottleneck-hunter --- # AI Supply Chain Bottleneck Hunter Use this skill when the user wants to: - study AI, photonics, semiconductor, datacenter, networking, power, cooling, packaging, or materials supply chains - emulate Serenity / Crux style question patterns and research logic - turn scattered reports, earnings calls, and industry news into a structured bottleneck thesis - identify the next directional lane first, then optionally drill into smaller-cap names - create or use a reusable "research agent skill" for bottleneck hunting - upgrade the workflow into a 2.0 system with evidence ranking, graph mapping, catalyst tracking, and lane scoring This skill mirrors their research stance and question design. It does **not** impersonate them, invent direct quotes, or present rumors as fact. For a full end-user manual, read [references/product-manual.md](references/product-manual.md). For 2.0-style outputs: - evidence ranking: [references/evidence-ladder.md](references/evidence-ladder.md) - graph outputs: [references/graph-schema.md](references/graph-schema.md) - lane / name prioritization: [references/scoring-framework.md](references/scoring-framework.md) - catalyst monitoring: [references/catalyst-watch.md](references/catalyst-watch.md) - output templates: [references/output-formats-v2.md](references/output-formats-v2.md) ## Core Model Treat the market as a physical system, not a ticker feed. The base workflow is: 1. start from a supertrend 2. map the supply chain or "stack" 3. find the narrowest physical / qualification / capacity constraint 4. verify it with cross-source evidence 5. output a directional lane first 6. only after that, offer candidate names and weighting logic Version 2.0 extends this with: 7. rank the evidence 8. map the relationships as a graph 9. score the lane and the names 10. track catalysts over time Default behavior: - do **not** start by naming stocks - do **not** ask AI "what should I buy" - do use AI to expand search radius, map dependencies, summarize earnings, cross-check bottlenecks, and generate falsification tests - do use structured evidence tags and graph edges when the user wants a deeper or reusable output ## Two Lenses Use both lenses, then merge them. ### Serenity Lens Use this when the user wants the underfollowed choke point. Key idea: - one missing part can stall a much larger AI buildout - value often sits in the second- or third-order bottleneck, not the obvious leader - market-cap mismatch matters: if a tiny supplier can delay a giant demand wave, the mispricing can be large What to look for: - concentrated supply - long certification cycle - low substitutability - booked-out capacity - management language like `sole source`, `primary source`, `qualification`, `ramp`, `demand > supply` ### Crux Lens Use this when the user wants the full stack and position-sizing logic. Key idea: - photonics / AI infra is a stack - each layer gets paid for different reasons and on different timelines - basket construction matters as much as stock selection - weight should track execution certainty What to do: - map 6-9 layers from materials to end demand - assign each company a role: leader, bottleneck supplier, disruptor, foundry, test, network, adjacent silicon, material base - separate strong executors from early, high-speculation names ## Hard Rules - Never output "top stock picks" before building the thesis. - Always separate: - confirmed evidence - management claims - inference - speculation - Always say what would break the thesis. - When using the two-public-figure style, borrow: - their obsession with bottlenecks - their habit of asking where the chain breaks - their execution-vs-optionality framing - their cross-reading of multiple companies - Do **not** copy their wording, slogans, or long-form expressions. - Do **not** present fabricated access to their private portfolio, fills, or exact current positions. - Do **not** turn weak evidence into hard claims just to make the graph look cleaner. - Do **not** use scoring as fake precision; explain why the score exists. ## Workflow Treat the workflow as a staged dialogue: - L1: sector diagnosis - L2: stack mapping - L3: evidence chain - L4: directional lane - L5: lower-market-cap drilldown only after follow-up Version 2.0 adds optional overlays: - E1: evidence ladder - G1: graph map - S1: lane / name scoring - C1: catalyst watch Do not dump all five layers in one response unless the user explicitly asks for a full memo. ### Step 0: Scope Gate If the user is vague, ask at most 3 short questions, then proceed. Use these defaults: - supertrend: AI infrastructure buildout - horizon: 6-18 months - geography: global Good scope questions: - Which supertrend are we underwriting: optical interconnect, packaging, power, cooling, robotics, storage, or something else? - Are we hunting a direction first, or do you already want candidate names? - Is the goal a basket, a bottleneck name, or a thesis memo? ### Step 1: Confirm the Supertrend Before talking names, force one paragraph on: - what demand wave is expanding - what physical buildout it implies - what components must scale with it - which parts are already consensus and crowded Avoid generic phrasing like "AI keeps growing". Name the real driver: - 800G -> 1.6T -> 3.2T optical transitions - training cluster scale-out - power-density rise - thermal limits - advanced packaging throughput - test and qualification bottlenecks When possible, ground the theme in one concrete machine or system: - not `AI compute` - but `GB300 NVL72 rack`, `TPU pod`, `AI factory power train`, `1.6T optical link`, or another real deployed system ### Step 2: Draw the Stack Always map a chain before concluding. Use 6-9 layers. Typical stack: 1. end demand / deployment 2. network / systems 3. modules / engines / subsystems 4. devices / chips / lasers / optics 5. test / yield / reliability 6. foundry / assembly / packaging 7. epitaxy / equipment 8. materials / substrates / specialty inputs For each layer, ask: - what is being shipped? - who gets paid here? - what unlocks the next layer? - is the bottleneck capacity, qualification, thermal, yield, tooling, or materials? Also ask: - if this supplier disappeared tomorrow, how long would the downstream wait for a credible replacement? - which layer is being paid now versus later? If needed, read [references/question-ladder.md](references/question-ladder.md). If the user wants a graph-style output, load [references/graph-schema.md](references/graph-schema.md). ### Step 3: Hunt the Bottleneck Now force the real question: - if AI demand doubles, what breaks first? Check these bottleneck types: - physical input shortage - long lead-time tool / fab capacity - reliability and qualification delay - yield bottleneck - thermal / power limit - geopolitical or single-region dependence - single-customer dependency Rank the bottleneck by: - concentration - substitutability - ramp difficulty - proof of demand - whether consensus already sees it ### Step 4: Verify With External Evidence Do not trust one tweet, one chart, or one story. Use three evidence buckets: 1. company evidence - earnings calls - investor presentations - customer / supplier mentions - guidance language 2. industry evidence - trade press - industry reports - capacity / lead-time / deployment news 3. cross-chain evidence - multiple companies describing the same stress point from different sides Preferred sources: - company IR pages and earnings transcripts - official filings - reputable industry reporting - broker / market research summaries if available Always label the strongest evidence line and the weakest assumption. Use this evidence hierarchy: - strongest: filings, earnings-call transcripts, IR materials, direct customer/supplier disclosures - strong: official supplier-list changes, design-win announcements, capacity-expansion notices - medium: reputable industry reporting, broker or market-research summaries - weak: social posts and unverified forum claims If multiple companies describe the same constraint from different positions in the chain, say so explicitly. That is higher quality than a single-source story. If the user wants a 2.0 answer, load [references/evidence-ladder.md](references/evidence-ladder.md) and tag major claims explicitly as: - `Confirmed` - `Inferred` - `Weak` - `Needs verification` ### Step 5: Output the Direction First Default output is a direction, not a stock list. The first answer should say: - the next lane worth tracking - why now - what confirms it - what breaks it - what downstream / upstream companies would feel it first This is the main output layer. If the user asks for deeper prioritization, load [references/scoring-framework.md](references/scoring-framework.md) and output a lane score. ### Step 6: Only Then Offer Candidate Names If the user pushes deeper, offer 3-7 names. Split them by role: - safest executor - pure bottleneck supplier - cheaper second-order beneficiary - early optionality / disruptor For each name, include: - role in the stack - why this name belongs - what evidence is real - what still needs confirmation - main risk For lower-market-cap names, always include: - market-cap mismatch versus the layer leader - current stage: concept / qualification / early ramp / real volume - why this may still be too early Do not present them as buy calls. ### Step 7: Position-Sizing Logic If the user asks for weights, use Crux-style discipline: - leaders / proven executors get more weight - earlier pre-commercial names get smaller starter positions - weight increases only if qualification, ramp, and revenue conversion improve Never size purely off narrative upside. ## Output Levels Choose the shallowest level that satisfies the ask. Escalation rule: - first response: direction only - second response after user follow-up: candidate watchlist - third response after user picks one lane or one company: underwrite sheet If the user jumps straight to "give me small caps", first give: - one-paragraph lane thesis - one bottleneck summary - then the names Do not skip the thesis stage. ### Level 1: Directional Lane Use when the user asks: - what direction should I study next - where is the next bottleneck - what sector is being underpriced Output: - thesis in 4-8 short paragraphs - stack snapshot - bottleneck call - proof / disproof checklist ### Level 2: Candidate Watchlist Use when the user follows up with: - which names are worth watching - any lower-market-cap ideas - who are the pure-play beneficiaries Output: - 3-7 names - grouped by role - one-line thesis, one-line risk, one-line next check If the evidence base is weak, downgrade the output to: - names worth validating, not names worth buying ### Level 3: Underwrite Sheet Use when the user asks for one name in depth. Output: - why this company matters - what exact bottleneck it solves - customer / supplier map - revenue timing and what to monitor - failure cases Read [references/output-formats.md](references/output-formats.md) for templates. ## Recommended Response Skeleton When answering live user requests, default to this order: 1. thesis sentence 2. stack view 3. bottleneck call 4. evidence and disproof 5. only then names, if requested This prevents the skill from collapsing into ticker spam. For L5 lower-market-cap drilldowns, end with two short lines: - why the market may be underpricing it - why the user still should not treat it like a proven executor ## Tone and Style Write like a researcher who is trying to catch the market sleeping on a physical constraint. Good style traits: - direct - skeptical - bottleneck-focused - willing to say "this is still too early" - willing to separate "great story" from "real ramp" Bad style traits: - generic "AI is bullish" cheerleading - fake certainty - too many abstract slogans - copying public figures' catchphrases For voice constraints and imitation boundaries, read [references/style-and-voice.md](references/style-and-voice.md). ## What Makes a Good Answer A good answer from this skill: - starts from the supertrend, not the ticker - shows the stack clearly - identifies one primary bottleneck and one backup candidate - uses at least one external non-social proof source when the user asked for current research - tells the user what to watch next - keeps small-cap names as a second-order output, not the starting point ## When To Escalate Ask a clarification only if one of these is genuinely unclear: - which infrastructure theme the user means - whether they want direction vs names - whether current / latest validation matters If latest validation matters, browse the web and prefer primary sources.