feat: add llmquant-ai-supply-chain skill

README.md

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 | [`llmquant-strategies`](skills/llmquant-strategies) | Hedge-fund and PM strategy playbooks. | Equity long/short, long-biased, event-driven, macro, quant, multi-strategy |
 | [`llmquant-market-intelligence`](skills/llmquant-market-intelligence) | Reusable market utilities and signal views. | Macro view, market sentiment, event probability signals |
 | [`llmquant-investor-lenses`](skills/llmquant-investor-lenses) | Investor-style reasoning overlays using LLMQuant Data evidence. | Buffett, Graham, Munger, Lynch, Fisher, Burry, Ackman, Damodaran, and more |
+| [`llmquant-ai-supply-chain`](skills/llmquant-ai-supply-chain) | AI industry supply-chain research from upstream semiconductors to downstream applications. | Semiconductor upstream map, chip design/memory map, advanced packaging tracker, network/interconnect map, hyperscaler capex tracker, model/application monitor, narrative-vs-earnings quadrant screen |
 
 ## Install
 

skills/llmquant-ai-supply-chain/SKILL.md

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+---
+name: llmquant-ai-supply-chain
+description: Router skill for LLMQuant AI supply-chain workflows. Use when the user needs semiconductor upstream, chip design, memory, advanced packaging, networking/interconnect, hyperscaler capex, AI model/application monitoring, or a narrative-vs-earnings quadrant view of AI-linked companies.
+input_data_source: LLMQuant Data
+category: ai-supply-chain
+---
+
+# LLMQuant AI Supply Chain
+
+This category routes AI-industry research workflows that treat the AI buildout as a structural supply chain: upstream materials and equipment, chip design, memory, advanced packaging, networking, hyperscaler data centers, and downstream model/application demand. It is distinct from `llmquant-equities` (single-name memos) and `llmquant-equity-derivatives` (options/hybrids) — this category stitches names together across tiers and tracks whether narrative, capex, and earnings evidence agree.
+
+## Routing Rules
+
+1. Identify the supply-chain tier, company set, geography/export-control exposure, horizon, and requested deliverable.
+2. Select the closest workflow below.
+3. Open only that workflow and any referenced local resources.
+4. Use LLMQuant Data for filings, price history, revenue/margin trends, capex disclosures, and industry context.
+5. Report filing periods, price dates, guidance-vs-actual deltas, stale notices, and missing inputs.
+
+## Workflow Index
+
+| User intent | Workflow |
+|---|---|
+| Map upstream materials and semiconductor equipment exposure (wafers, photoresist, gases, CMP, litho, etch, deposition, test). | [`workflows/semiconductor-upstream-map.md`](workflows/semiconductor-upstream-map.md) |
+| Map chip design and memory exposure (CPU/GPU/AI ASIC/FPGA plus HBM/DRAM/NAND) and HBM-attach dynamics. | [`workflows/chip-design-memory-map.md`](workflows/chip-design-memory-map.md) |
+| Track advanced packaging capacity and bottlenecks (CoWoS, SoIC, 2.5D/3D, chiplet, OSAT). | [`workflows/advanced-packaging-tracker.md`](workflows/advanced-packaging-tracker.md) |
+| Map AI networking and interconnect exposure (CPO, optical modules, switching ASICs, SerDes, InfiniBand/Ethernet). | [`workflows/ai-network-interconnect-map.md`](workflows/ai-network-interconnect-map.md) |
+| Track hyperscaler capex guidance vs. actuals, data-center buildout, power, and cooling constraints. | [`workflows/hyperscaler-capex-datacenter-tracker.md`](workflows/hyperscaler-capex-datacenter-tracker.md) |
+| Monitor large-model evolution and downstream/enterprise AI application adoption as a recurring watch. | [`workflows/ai-model-application-monitor.md`](workflows/ai-model-application-monitor.md) |
+| Classify AI supply-chain companies into a narrative-vs-earnings quadrant (core main line, awaiting verification, potential catch-up, risk zone). | [`workflows/ai-supply-chain-quadrant-screen.md`](workflows/ai-supply-chain-quadrant-screen.md) |
+
+## LLMQuant Data Contract
+
+Prefer LLMQuant Data when available. The workflows may need these data capabilities:
+- Retrieve SEC filings (10-K/10-Q/8-K) and sections covering capex guidance, segment revenue, and risk factors.
+- Retrieve equity price history, market cap, and realized volatility for supply-chain names.
+- Retrieve revenue growth, gross margin, and valuation multiples (P/E, P/S) at company and peer-group level.
+- Retrieve macro, export-control, and sector news context when it directly affects a supply-chain node.
+
+Future or optional data capabilities:
+- Structured supply-chain node/tier tagging per company (materials, equipment, wafer fab, design, memory, packaging, network, servers, downstream).
+- Consensus capex and unit-shipment estimates for cross-company comparison.
+
+Fallback:
+- If filing, price, or fundamental data is unavailable for a name, name the missing input and continue only with retrieved evidence.
+- Do not infer capacity, yields, order backlogs, or capex figures from memory; treat unresolvable claims as unverified.
+- Do not present narrative or sentiment framing as if it were reported financial data.
+
+## Output Requirements
+
+Every workflow response should include:
+
+1. Answer or recommendation
+2. Tier/node evidence table with company-level facts and dates
+3. Risks and caveats (cyclicality, concentration, export-control, single-customer exposure)
+4. Data used, including filing periods, price dates, and coverage notices

skills/llmquant-ai-supply-chain/workflows/advanced-packaging-tracker.md

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+---
+name: Advanced Packaging Tracker
+description: Track advanced packaging capacity and bottlenecks across CoWoS, SoIC, 2.5D/3D packaging, chiplet integration, and OSAT providers using LLMQuant Data.
+input_data_source: LLMQuant Data
+pack: workflows
+---
+
+# Advanced Packaging Tracker
+
+## Purpose
+
+Advanced packaging (CoWoS, SoIC, 2.5D/3D integration, chiplet, OSAT) is the most frequently cited bottleneck between chip design and finished AI accelerators. This workflow tracks capacity, expansion disclosures, and which companies are exposed to that bottleneck easing or persisting.
+
+---
+
+## Input Data Source
+
+Use **LLMQuant Data** for filings, capex disclosures, and price history. State which capabilities were used and cite returned dates or periods.
+
+---
+
+## LLMQuant Data Contract
+
+Required data capabilities:
+- SEC filing discovery and section retrieval (capex guidance, capacity-expansion commentary) for named packaging/OSAT companies.
+- Equity price history for named companies.
+
+Optional data capabilities:
+- Industry/trade-press context on packaging capacity additions and yield commentary.
+- Consensus capex estimates for packaging-exposed names.
+
+Freshness:
+- Use the latest available 10-K/10-Q/8-K unless the user specifies a period.
+- State filing dates, periods of report, and price date ranges.
+
+Fallback:
+- If capacity or yield figures are not disclosed in retrieved filings, name the gap and state that only qualitative or price-based evidence is available.
+- Do not estimate CoWoS/SoIC capacity in units without a sourced figure.
+
+---
+
+## Workflow
+
+1. Confirm scope: a specific packaging technology (CoWoS, SoIC, chiplet) or the full packaging/OSAT set.
+2. Retrieve filings and price history for named companies.
+3. Extract capex, capacity-expansion, and yield commentary as reported; separate from narrative or rumor.
+4. Identify which downstream chip-design names are most exposed to packaging capacity as a gating factor.
+5. Note geographic concentration (packaging capacity is concentrated in a small number of regions).
+
+---
+
+## Output Format
+
+1. **Answer**: current bottleneck read (easing, stable, tightening) with evidence basis.
+2. **Capacity/Capex Evidence Table**
+3. **Downstream Exposure Map** (which design/hyperscaler names are gated by this bottleneck)
+4. **Risks / Caveats**
+5. **Data Used**
+
+---
+
+## Guardrails
+
+- Do not present capacity-expansion announcements as completed, qualified capacity until filings confirm ramp.
+- Do not state precise yield percentages without a sourced figure.
+- Do not make personalized financial advice claims.

skills/llmquant-ai-supply-chain/workflows/ai-model-application-monitor.md

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+---
+name: AI Model and Application Monitor
+description: Monitor large-model evolution and downstream enterprise/consumer AI application adoption as a recurring watch, and connect adoption signals to which sectors and names see earnings impact first, using LLMQuant Data.
+input_data_source: LLMQuant Data
+pack: workflows
+---
+
+# AI Model and Application Monitor
+
+## Purpose
+
+Track two linked, fast-moving layers as a recurring watch rather than a one-off memo: (1) large-model evolution — release cadence, open- vs. closed-model economics, inference cost trends — and (2) downstream application adoption across enterprise and consumer sectors. The goal is to move past news aggregation into which non-tech sectors and names see earnings impact first from AI adoption.
+
+---
+
+## Input Data Source
+
+Use **LLMQuant Data** for filings, segment commentary, and price history. State which capabilities were used and cite returned dates or periods.
+
+---
+
+## LLMQuant Data Contract
+
+Required data capabilities:
+- SEC filing discovery and section retrieval (AI-related segment commentary, product disclosures) for named model labs, platform companies, and adopting enterprises.
+- Equity price history for named companies.
+
+Optional data capabilities:
+- Wiki or paper research search to define model architectures or benchmark terminology.
+- Industry/trade-press context on model releases and enterprise adoption case studies.
+
+Freshness:
+- Use the latest available filings and earnings-call disclosures unless the user specifies a period.
+- State filing/report dates and the observation window for any adoption claim.
+
+Fallback:
+- If adoption metrics are not disclosed by name, state that only qualitative or anecdotal evidence is available and flag it as such.
+- Do not present model benchmark scores or inference-cost figures from memory as current; require a retrieved or user-provided source.
+
+---
+
+## Workflow
+
+1. Confirm scope: model-layer focus, application/adoption focus, or both; and which sectors or names to check for earnings impact.
+2. Retrieve filings and disclosures for named model labs, platform providers, and adopting companies.
+3. Separate model-layer evidence (release cadence, pricing changes, open vs. closed positioning) from application-layer evidence (disclosed adoption, revenue attribution, headcount or cost commentary tied to AI tools).
+4. Map adoption evidence to the sector or name most likely to show earnings impact first, and state the mechanism (cost reduction, revenue line, productivity claim).
+5. Flag where adoption claims are promotional/anecdotal versus filing-disclosed and quantified.
+
+---
+
+## Output Format
+
+1. **Answer**: current state of model-layer competition and where adoption evidence is strongest.
+2. **Model-Layer Evidence Table**
+3. **Application-Layer Evidence Table**
+4. **Sector/Earnings-Impact Map**
+5. **Risks / Caveats**
+6. **Data Used**
+
+---
+
+## Guardrails
+
+- Do not present vendor marketing claims about adoption or ROI as verified filing disclosure.
+- Do not predict which model will "win" without evidence; describe competitive dynamics, not forecasts.
+- Do not make personalized financial advice claims.

skills/llmquant-ai-supply-chain/workflows/ai-network-interconnect-map.md

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+---
+name: AI Network and Interconnect Map
+description: Map AI networking and interconnect exposure across co-packaged optics, optical modules, switching ASICs, SerDes, and InfiniBand/Ethernet fabric using LLMQuant Data.
+input_data_source: LLMQuant Data
+pack: workflows
+---
+
+# AI Network and Interconnect Map
+
+## Purpose
+
+Map the networking layer that connects AI accelerators inside and across data centers: co-packaged optics (CPO), optical modules/transceivers, switching ASICs, SerDes IP, and InfiniBand/Ethernet fabric providers. This layer scales with GPU cluster size and is a distinct cycle from chip design or packaging.
+
+---
+
+## Input Data Source
+
+Use **LLMQuant Data** for filings, segment revenue, and price history. State which capabilities were used and cite returned dates or periods.
+
+---
+
+## LLMQuant Data Contract
+
+Required data capabilities:
+- SEC filing discovery and section retrieval (segment revenue, product-mix, capacity commentary) for named networking/optics companies.
+- Equity price history and realized volatility for named companies.
+
+Optional data capabilities:
+- Consensus revenue and unit-shipment estimates for optical modules and switching silicon.
+- Industry context on transceiver speed transitions (e.g. 800G to 1.6T) and CPO adoption timing.
+
+Freshness:
+- Use the latest available 10-K/10-Q unless the user specifies a period.
+- State filing dates, periods of report, and price date ranges.
+
+Fallback:
+- If shipment or speed-transition data is unavailable in retrieved filings, name the gap and rely on reported segment revenue trends instead.
+- Do not estimate module ASPs, port counts, or transition timing from memory.
+
+---
+
+## Workflow
+
+1. Confirm scope: optics/transceivers, switching ASICs, SerDes/fabric, or the full networking layer.
+2. Retrieve filings and price history for named companies.
+3. Extract segment revenue, product-mix, and speed-transition commentary as reported.
+4. Note which companies are exposed to InfiniBand vs. Ethernet fabric choices, since customer architecture decisions shift demand between them.
+5. Flag customer concentration (networking suppliers are often concentrated on a small number of hyperscaler or accelerator customers).
+
+---
+
+## Output Format
+
+1. **Answer**: current read on networking-layer demand and technology-transition positioning.
+2. **Networking Evidence Table**
+3. **Fabric/Technology Transition Notes** (InfiniBand vs. Ethernet, CPO adoption stage)
+4. **Concentration Risks**
+5. **Data Used**
+
+---
+
+## Guardrails
+
+- Do not present a technology-transition timeline as certain; describe it as company-disclosed guidance or industry expectation.
+- Do not conflate design-win announcements with recognized revenue.
+- Do not make personalized financial advice claims.

skills/llmquant-ai-supply-chain/workflows/ai-supply-chain-quadrant-screen.md

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+---
+name: AI Supply Chain Quadrant Screen
+description: Classify AI supply-chain companies into a narrative-vs-earnings quadrant (core main line, awaiting verification, potential catch-up, risk zone) using price performance, revenue growth, margin trend, and valuation multiples from LLMQuant Data.
+input_data_source: LLMQuant Data
+pack: workflows
+---
+
+# AI Supply Chain Quadrant Screen
+
+## Purpose
+
+Classify a set of AI supply-chain companies (spanning upstream materials, equipment, design, memory, packaging, networking, servers, and downstream demand) into four quadrants by crossing narrative strength against earnings validation:
+
+- **Core Main Line**: strong narrative + strong earnings evidence.
+- **Awaiting Verification**: strong narrative, earnings evidence not yet conclusive.
+- **Potential Catch-Up**: strong earnings evidence, narrative/valuation lagging.
+- **Risk Zone**: high valuation, weak or decelerating growth evidence.
+
+This is the cross-tier synthesis workflow — use the tier-specific workflows first to gather node-level evidence, then use this workflow to classify.
+
+---
+
+## Input Data Source
+
+Use **LLMQuant Data** for filings, price history, and fundamentals. State which capabilities were used and cite returned dates or periods.
+
+---
+
+## LLMQuant Data Contract
+
+Required data capabilities:
+- Equity price history (1-month and 6-month performance) for named companies.
+- Revenue growth rate, gross margin, and valuation multiples (P/E, P/S) from filings or fundamental data.
+
+Optional data capabilities:
+- Peer-group or supply-chain-node median metrics for relative comparison.
+- Consensus estimate revisions as a narrative-strength proxy.
+
+Freshness:
+- Use the latest available filing period and most recent price data unless the user specifies otherwise.
+- State filing dates, periods of report, and price date ranges for every company classified.
+
+Fallback:
+- If a required metric (growth, margin, or multiple) is unavailable for a company, state that the classification is incomplete for that name and classify only on available metrics, flagged as partial.
+- Do not assign a quadrant based on narrative alone without at least one quantitative input.
+
+---
+
+## Workflow
+
+1. Confirm the company set and supply-chain tier(s) in scope; if the user gives a theme instead of tickers, ask for or infer a representative list from prior workflow output.
+2. Retrieve price performance, revenue growth, gross margin, and valuation multiples for each company.
+3. Score narrative strength using price momentum and estimate-revision direction (if available) as a proxy; score earnings validation using revenue growth and margin trend.
+4. Place each company into one of the four quadrants and state the deciding evidence.
+5. Aggregate a node-level read (e.g. median quadrant placement per tier) if multiple companies per tier are scored.
+
+---
+
+## Output Format
+
+1. **Answer**: quadrant summary across the company set.
+2. **Quadrant Table**: company, tier, price performance, growth, margin, valuation, assigned quadrant.
+3. **Node-Level Aggregation** (if applicable).
+4. **Risks / Caveats** (data gaps, single-metric classifications, cyclicality).
+5. **Data Used**.
+
+---
+
+## Guardrails
+
+- Do not treat quadrant placement as a price target or trade recommendation.
+- Do not classify a company using narrative alone; require at least one quantitative input per axis.
+- Do not make personalized financial advice claims.