Bottleneck Hunter logo

Bottleneck Hunter

OrganizationPopular
HKUDS
bottleneck-hunter

Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders.

Overview

PublisherHKUDS
RepositoryVibe-Trading
Skill namebottleneck-hunter
Stars
33.6K
Forks
5.5K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by HKUDS on GitHub. Read the source before you install it.

Installation

Install the Bottleneck Hunter AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git /tmp/Vibe-Trading
mkdir -p .claude/skills
cp -r /tmp/Vibe-Trading/agent/src/skills/bottleneck-hunter .claude/skills/bottleneck-hunter
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Bottleneck Hunter in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Bottleneck Hunter on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Bottleneck Hunter is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Supply-Chain Bottleneck Hunter

Decompose a super-trend (user-specified, e.g. "AI infrastructure", "energy transition") into its physical supply chain and hunt for bottleneck-arbitrage opportunities.

Core Idea

Don't ask "which AI stock to buy" — ask "if this trend keeps expanding, which link runs out first?"

Traditional research chases leaders and known tracks. This skill inverts: start from the choke points of the physical supply chain and find companies nobody notices but that the whole industry must wait on when they run short.

The edge: Layer-1 bottlenecks (GPU, HBM, power) are already priced in. The real alpha is in Layer 2 and Layer 3 — optical modules, lasers, InP substrates, SOI wafers, epitaxy equipment, wafer-level test, IC substrates, specialty fiberglass.

Step 1: Super-Trend Confirmation

Trend Filter Criteria

CriterionRequirementHow to verify
Durability≥3-5 years of certain growthSearch industry forecasts, capex plans
PhysicalityNeeds real hardware/material/equipment buildDistinguish "software upgrade" from "physical expansion"
ScaleGlobal capex >$50B/yearSearch top players' capex guidance
AccelerationDemand growth > supply expansionCompare demand growth vs capacity plans

Use web_search to verify. Reference super-trends: AI infrastructure, energy transition (nuclear/grid/storage), defense modernization, semiconductor reshoring, space economy.

Step 2: Physical Supply-Chain Decomposition

Don't stop at concepts — decompose to physical entities.

Layer 0 (end): final product/service
Layer 1 (core component): already closely watched  → priced in, limited alpha
Layer 2 (sub-component/material): low attention, alpha-rich
Layer 3 (upstream equipment/raw material)
Layer 4 (infrastructure): power, cooling, land, talent, certifications

AI Infrastructure Example

Layer 0: AI model training/inference services
Layer 1: GPU/accelerators, HBM, servers, data centers
Layer 2 (focus zone):
  - Network interconnect: optical modules, fiber, switch ASICs, copper cables
  - Optical comms core: lasers (EML/VCSEL/CW), modulators, photodetectors
  - Semiconductor materials: InP substrates, GaAs substrates, SOI wafers, SiC substrates
  - Advanced packaging: CoWoS interposers, HBM TSV, ABF substrate film
  - PCB/substrate: high-frequency PCB, IC substrates, specialty fiberglass
  - Test: wafer-level test (probe cards), burn-in, ATE
  - Thermal/cooling: liquid cooling, CDU, immersion fluid
  - Power connection: busbars, UPS, distribution, transformers
Layer 3: epitaxy equipment (MOCVD/MBE), lithography/etch, high-purity metals (In/Ga/Ge), specialty gases, sputtering targets, certifications (MSA/Telcordia)
Layer 4: power (nuclear/gas/transmission), cooling water, data-center land/permits

For other trends, use web_search with queries like {trend} supply chain bottleneck, {trend} shortage critical component, {trend} capacity constraint, {trend} sole source supplier.

Step 3: Bottleneck Identification — Finding "Choke Points"

For each Layer 2-3 link, evaluate 6 criteria:

#CriterionQuestionScore
1Supply concentration≤3 global suppliers?🔴 ≤2 / 🟡 3-5 / 🟢 >5
2Expansion lead timeHow long to add capacity?🔴 >2y / 🟡 1-2y / 🟢 <1y
3SubstitutabilityCan other tech/material replace it?🔴 irreplaceable / 🟡 partial / 🟢 easy
4Capacity utilizationCurrent utilization?🔴 >90% / 🟡 70-90% / 🟢 <70%
5Demand growthDownstream demand growth?🔴 >50%/yr / 🟡 20-50% / 🟢 <20%
6Customer qualification cycleHow long for a new supplier to qualify?🔴 >1y / 🟡 6-12m / 🟢 <6m

Bottleneck grade: 🔴×≥4 → S-grade (single-point failure, highest priority); 🔴×3 → A-grade (severely constrained); 🔴×1-2 → B-grade (stressed but manageable); no 🔴 → not a bottleneck, skip.

Step 4: Company Screening — From Bottleneck to Tickers

For each S/A-grade bottleneck, use web_search / screen_market to find listed companies.

Initial Screen

CriterionRequirement
Listing statusListed (A/HK/US/JP/TW/EU)
Bottleneck revenue share>30% of revenue from the bottleneck link
Market capPrefer <$10B (large caps already priced)
LiquidityAverage daily turnover >$1M

Valuation Gate (mandatory, never skip)

A real bottleneck ≠ an investment opportunity. For every company, compute PE/PB/ROE/FCF yield with financial_rigor (command=verify_valuation), and run financial_rigor (command=three_scenario) for scenario valuation:

  • Red light (any one → signal strength capped at ★★, flag "valuation stretched"): market cap >20% of TAM; PS>30x with revenue growth <100%; market cap >10× 5-year optimistic revenue forecast; stock doubled within 60 days of a follow-on offering.
  • Yellow light (needs extra justification, else downgrade): loss-making + PS>15x; PS >5× a profitable peer; PE>80x (compute PEG).
  • Green light (bonus): PS<10x with revenue growing; PE<30x with a moat (flag "margin of safety").

Sanity check (mandatory): with financial_rigor (command=three_scenario), answer — "buying at current market cap, if the most optimistic scenario fully plays out and I exit at 25× PE in 10 years, what's the annualized return?" <10%/yr → flag "no margin of safety at current price".

Step 5: Cross-Validation — Don't Trust a Single Story

Positive checks

CheckQuestion
Customer validationHave top customers signed/imported? (check announcements, customer filings)
Revenue validationIs the bottleneck already showing in revenue growth? (last 2-3 quarters)
Price validationIs the product raising price? (industry quotes, analyst reports)
Capacity validationIs capacity really tight? (lead times, customer complaints)
Capital validationIs there expansion capex? (company guidance)

Use get_financial_statements / get_stock_news / web_search.

Reverse checks (Munger inversion)

  • Why don't smart people buy this stock?
  • Can the bottleneck be bypassed? Alternative routes?
  • Can China / other players quickly replicate capacity?
  • If end-demand drops 50%, what happens to this company?
  • Has management diluted at highs before?
  • What growth assumption does the current valuation imply?

Step 6: Output — Bottleneck Opportunity Board

Ranking Table

RankCompanyTickerMkt CapRevenuePSPEBottleneck linkGradeShareGrowthSignalValuation

Market cap, revenue, PS, PE are mandatory — never skip with "TBD". If financials can't be obtained, signal strength ≤★★.

Signal strength (valuation gate directly affects):

  • ★★★★★ multi-cross-validated + customers imported + revenue confirmed + valuation green
  • ★★★★ most checks pass + valuation green/yellow (with explanation)
  • ★★★ logic holds but parts unverified + valuation yellow acceptable
  • ★★ early signal, or logic holds but valuation red
  • ★ pure concept, unverified

After drafting, run report_audit (command=extract → verify each point → command=verdict) as a quality gate to ensure no hallucinated numbers.

One-Pager Template

🎯 {Company} ({Ticker}) — {one-line bottleneck positioning}

Why it's a bottleneck: (2-3 sentences)
Why this company: (2-3 sentences)

Catalyst timeline:
- Near-term (1-3m): [earnings / capacity / customer win]
- Mid-term (3-12m): [industry trend / expansion node]

Key risks: 1.  2.

Key data: market cap / revenue / PS / PE / growth / bottleneck revenue share
Margin of safety: 10y 25× PE exit method, annualized return XX%. Conclusion: yes/no.

Cross-validation status: ✅ customer / ✅ revenue / ⚠️ valuation stretched / ❌ unverified

Conclusion: deep research / watchlist / skip

Save with write_file to the reports directory (e.g. reports/bottleneck-map/{trend}-bottleneck-{YYYYMMDD}.md).

Step 7: Inventory Update — Maintain the Bottleneck Map

On each run: ① re-check identified bottlenecks (new suppliers? capacity expanded? substitute breakthrough?); ② scan new bottlenecks (web_search last 7 days supply chain / shortage / bottleneck news); ③ update grades (upgrade/downgrade/relieve).

AI Research Bias Self-Check

BiasSymptomCounter
Leader-biasSearch dominated by large capsDeliberately search small-cap suppliers, add "small cap"
English-biasMiss JP/KR/TW playersMust search JP/KR/TW market suppliers
Narrative-biasDrawn to "AI concept" labelsLook only at actual supply-chain position, not market labels
Confirmation-biasAfter finding a bottleneck, only seek positive evidenceForce Step 5 reverse checks
Recency-biasRely on stale infoPrefer last 30 days of data

Core Principles (highest priority)

  1. Don't ask the AI to recommend stocks — ask it to decompose supply chains. The question matters more than the answer.
  2. Physical first — only links that need real physical product/material/equipment.
  3. Layer 2 and Layer 3 — don't chase already-priced leaders.
  4. Cross-validate — every conclusion needs ≥2 independent sources.
  5. Be honest about uncertainty — if data is missing, say so; don't fill with speculation.
  6. Bottlenecks are temporary — every bottleneck gets resolved; the key is timing the window.
  7. Small cap ≠ good opportunity — a small cap can also be a bad company; it must pass financial quality.
  8. A real bottleneck ≠ an investment — at PS>30x or still loss-making, the current price is not a buy. Valuation is a hard gate that cannot be overridden by bottleneck purity, signal strength, or narrative appeal. Better to miss a bottleneck stock that ran than buy a loss-making company at 100× sales.

Frequently asked questions

What does the Bottleneck Hunter AI skill do?

Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user want...

Why use Bottleneck Hunter on TypingMind?

Because you install it once and use it with any model. Bottleneck Hunter is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Bottleneck Hunter in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/bottleneck-hunter. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Bottleneck Hunter?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Bottleneck Hunter?

As many as you like. As long as a model supports skills, you can use Bottleneck Hunter with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Bottleneck Hunter AI skill free?

Yes. It is published on GitHub by HKUDS under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇