AI-Trader Market Intel
HKUDS/AI-Trader
Reads AI-Trader's read-only market snapshots, grouped financial news and events board through its market-intel endpoints, for context before trading or posting.
Breaks a structural trend such as AI infrastructure into its physical supply chain and ranks lesser-known listed companies sitting on each bottleneck.
Install Supply-Chain Bottleneck Hunter
$ npx skills add HKUDS/Vibe-Trading --skill bottleneck-hunter -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install HKUDS/Vibe-Trading bottleneck-hunter --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agent/src/skills/bottleneck-hunter .claude/skills/bottleneck-hunter && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "bottleneck-hunter" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/bottleneck-hunter into .claude/skills/bottleneck-hunter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bottleneck-hunter", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/bottleneck-hunterType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add HKUDS/Vibe-Trading --skill bottleneck-hunter -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install HKUDS/Vibe-Trading bottleneck-hunter --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .agents/skills && cp -r skills-src/agent/src/skills/bottleneck-hunter .agents/skills/bottleneck-hunter && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bottleneck-hunter" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/bottleneck-hunter into .agents/skills/bottleneck-hunter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bottleneck-hunter", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add HKUDS/Vibe-Trading --skill bottleneck-hunter -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install HKUDS/Vibe-Trading bottleneck-hunter --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/agent/src/skills/bottleneck-hunter .cursor/skills/bottleneck-hunter && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bottleneck-hunter" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/bottleneck-hunter into .cursor/skills/bottleneck-hunter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bottleneck-hunter", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/HKUDS/Vibe-Trading.git --path agent/src/skills/bottleneck-hunter--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add HKUDS/Vibe-Trading --skill bottleneck-hunter -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install HKUDS/Vibe-Trading bottleneck-hunter --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/agent/src/skills/bottleneck-hunter .gemini/skills/bottleneck-hunter && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bottleneck-hunter" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/bottleneck-hunter into .gemini/skills/bottleneck-hunter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bottleneck-hunter", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install HKUDS/Vibe-Trading bottleneck-hunterInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add HKUDS/Vibe-Trading --skill bottleneck-hunter -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .github/skills && cp -r skills-src/agent/src/skills/bottleneck-hunter .github/skills/bottleneck-hunter && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bottleneck-hunter" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/bottleneck-hunter into .github/skills/bottleneck-hunter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bottleneck-hunter", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add HKUDS/Vibe-Trading --skill bottleneck-hunter -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install HKUDS/Vibe-Trading bottleneck-hunter --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/agent/src/skills/bottleneck-hunter .opencode/skills/bottleneck-hunter && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bottleneck-hunter" agent skill from https://github.com/HKUDS/Vibe-Trading/tree/main/agent/src/skills/bottleneck-hunter into .opencode/skills/bottleneck-hunter/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bottleneck-hunter", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
bottleneck-hunter- GitHub stars
- 35k
- Token cost
- ~2.7k tokens
- SKILL.md length
- 1,000 words
- Files
- 1
- Skills in repo
- 89
- Repo updated
- First seen
- Licence
- MIT
At a glance
Breaks a structural trend such as AI infrastructure into its physical supply chain and ranks lesser-known listed companies sitting on each bottleneck.
- Works in 7 steps: Super-Trend Confirmation → Physical Supply-Chain Decomposition → Bottleneck Identification — Finding… → …
- Finding lesser-known companies that benefit from a structural trend
- SKILL.md covers Core Idea, Step 1: Super-Trend Confirmation, Step 2: Physical Supply-Chain… and Step 3: Bottleneck…, plus 6 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
You name a long-running trend, for example AI infrastructure, energy transition, defense modernization, semiconductor reshoring or the space economy, and the skill first checks that it qualifies: durable growth, real hardware demand, large global capex and demand growing faster than supply. It then breaks the trend into layers of the physical supply chain, treating widely watched core components as already priced in and focusing on second and third layer links such as optical modules, lasers, InP substrates, SOI wafers, IC substrates and specialty fiberglass.
Each link is scored on six scarcity criteria, starting with how concentrated the supplier base is. The skill description adds mandatory valuation gates (PS, PE and a safety margin) run through the financial_rigor tool, a Munger-style reverse check, and a ranked bottleneck opportunity board as the output. It relies on web search to verify the trend and to look for shortage, capacity-constraint and sole-source evidence.
When your agent uses it
- Finding lesser-known companies that benefit from a structural trend
- Decomposing an industry's supply chain down to component and material level
- Scoring which supply-chain link is likely to run short first
- Building a ranked bottleneck board from a trend such as energy transition
Example prompts
- “Hunt for bottlenecks in the semiconductor reshoring supply chain and rank the companies behind them.”
- “Decompose the space economy into its physical supply chain and show where capacity runs out first.”
- “Which second-layer suppliers are hidden beneficiaries of AI infrastructure spending?”
Requirements
- Web search access
- The financial_rigor tool for the valuation gates
Workflow steps
7 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit e532650. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Supply-Chain Bottleneck Hunter loads about 2.7k tokens when it runs. Until then it costs about 174 tokens; SKILL.md has 1,000 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from HKUDS/Vibe-Trading at commit e532650, republished under its MIT licence (© HKUDS). 1,000 words, ~2,695 tokens.
Download SKILL.mdSave it as
.claude/skills/bottleneck-hunter/SKILL.md (or your agent's skills folder).- name
- bottleneck-hunter
- description
- 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.
- category
- strategy
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
| Criterion | Requirement | How to verify |
|---|---|---|
| Durability | ≥3-5 years of certain growth | Search industry forecasts, capex plans |
| Physicality | Needs real hardware/material/equipment build | Distinguish "software upgrade" from "physical expansion" |
| Scale | Global capex >$50B/year | Search top players' capex guidance |
| Acceleration | Demand growth > supply expansion | Compare 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, certificationsAI 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/permitsFor 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:
| # | Criterion | Question | Score |
|---|---|---|---|
| 1 | Supply concentration | ≤3 global suppliers? | 🔴 ≤2 / 🟡 3-5 / 🟢 >5 |
| 2 | Expansion lead time | How long to add capacity? | 🔴 >2y / 🟡 1-2y / 🟢 <1y |
| 3 | Substitutability | Can other tech/material replace it? | 🔴 irreplaceable / 🟡 partial / 🟢 easy |
| 4 | Capacity utilization | Current utilization? | 🔴 >90% / 🟡 70-90% / 🟢 <70% |
| 5 | Demand growth | Downstream demand growth? | 🔴 >50%/yr / 🟡 20-50% / 🟢 <20% |
| 6 | Customer qualification cycle | How 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
| Criterion | Requirement |
|---|---|
| Listing status | Listed (A/HK/US/JP/TW/EU) |
| Bottleneck revenue share | >30% of revenue from the bottleneck link |
| Market cap | Prefer <$10B (large caps already priced) |
| Liquidity | Average 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
| Check | Question |
|---|---|
| Customer validation | Have top customers signed/imported? (check announcements, customer filings) |
| Revenue validation | Is the bottleneck already showing in revenue growth? (last 2-3 quarters) |
| Price validation | Is the product raising price? (industry quotes, analyst reports) |
| Capacity validation | Is capacity really tight? (lead times, customer complaints) |
| Capital validation | Is there expansion capex? (company guidance) |
Use get_financial_statements / get_stock_news / web_search.
Show full SKILL.md (416 more words)Show less
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
| Rank | Company | Ticker | Mkt Cap | Revenue | PS | PE | Bottleneck link | Grade | Share | Growth | Signal | Valuation |
|---|
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 / skipSave 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
| Bias | Symptom | Counter |
|---|---|---|
| Leader-bias | Search dominated by large caps | Deliberately search small-cap suppliers, add "small cap" |
| English-bias | Miss JP/KR/TW players | Must search JP/KR/TW market suppliers |
| Narrative-bias | Drawn to "AI concept" labels | Look only at actual supply-chain position, not market labels |
| Confirmation-bias | After finding a bottleneck, only seek positive evidence | Force Step 5 reverse checks |
| Recency-bias | Rely on stale info | Prefer last 30 days of data |
Core Principles (highest priority)
- Don't ask the AI to recommend stocks — ask it to decompose supply chains. The question matters more than the answer.
- Physical first — only links that need real physical product/material/equipment.
- Layer 2 and Layer 3 — don't chase already-priced leaders.
- Cross-validate — every conclusion needs ≥2 independent sources.
- Be honest about uncertainty — if data is missing, say so; don't fill with speculation.
- Bottlenecks are temporary — every bottleneck gets resolved; the key is timing the window.
- Small cap ≠ good opportunity — a small cap can also be a bad company; it must pass financial quality.
- 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.
© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in agent/src/skills/bottleneck-hunter of HKUDS/Vibe-Trading.
Open the folder on GitHubat commit e532650
Compare with similar skills
Supply-Chain Bottleneck Hunter next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Supply-Chain Bottleneck Hunter this skillHKUDS/Vibe-Trading | 35k | — | ~2.7k | Automated safety check: Pass | MIT | |
| AI-Trader Market IntelHKUDS/AI-Trader | 23k | — | ~1.1k | Automated safety check: Pass | None | |
| Stock Deep Analysis Workflowwbh604/UZI-Skill | 7.1k | — | ~9.1k | Automated safety check: Notes | MIT | |
| Zhengxi Fund Manager Views Librarylyra81604/zhengxi-views | 1.8k | — | ~1.6k | Automated safety check: Pass | MIT | |
| Supply Chain Bottleneck Hunterxbtlin/ai-berkshire | 17k | — | ~2.6k | Automated safety check: Pass | MIT | |
| Deep Company Article Seriesxbtlin/ai-berkshire | 17k | — | ~2k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Supply-Chain Bottleneck Hunter
What does Supply-Chain Bottleneck Hunter do?
Breaks a structural trend such as AI infrastructure into its physical supply chain and ranks lesser-known listed companies sitting on each bottleneck. You name a long-running trend, for example AI infrastructure, energy transition, defense modernization, semiconductor reshoring or the space economy, and the skill first checks that it qualifies: durable growth, real hardware demand, large global capex and demand growing faster than supply. It then breaks the trend into layers of the physical supply chain, treating widely watched core components as already priced in and focusing on second and third layer links such as optical modules, lasers, InP substrates, SOI wafers, IC substrates and specialty fiberglass.
When should I use Supply-Chain Bottleneck Hunter?
Supply-Chain Bottleneck Hunter fits situations like: finding lesser-known companies that benefit from a structural trend; decomposing an industry's supply chain down to component and material level; scoring which supply-chain link is likely to run short first; building a ranked bottleneck board from a trend such as energy transition.
How do I install Supply-Chain Bottleneck Hunter in Claude Code?
Run `npx skills add HKUDS/Vibe-Trading --skill bottleneck-hunter -a claude-code`. Or copy the skill folder (agent/src/skills/bottleneck-hunter in HKUDS/Vibe-Trading) into .claude/skills/bottleneck-hunter in your project. Claude Code loads it when a task matches its description.
How do I install Supply-Chain Bottleneck Hunter in Codex?
Run `npx skills add HKUDS/Vibe-Trading --skill bottleneck-hunter -a codex`. Or copy the skill folder (agent/src/skills/bottleneck-hunter in HKUDS/Vibe-Trading) into .agents/skills/bottleneck-hunter in your project. Codex loads it when a task matches its description.
Can I use Supply-Chain Bottleneck Hunter in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HKUDS/Vibe-Trading --skill bottleneck-hunter -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bottleneck-hunter, .gemini/skills/bottleneck-hunter, .github/skills/bottleneck-hunter and .opencode/skills/bottleneck-hunter in your project.
What does Supply-Chain Bottleneck Hunter need to run?
SKILL.md names no scripts, command-line tools or credentials: Supply-Chain Bottleneck Hunter is instructions for the agent only. Our summary lists: Web search access; The financial_rigor tool for the valuation gates.
Does Supply-Chain Bottleneck Hunter access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Supply-Chain Bottleneck Hunter safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Supply-Chain Bottleneck Hunter use?
Supply-Chain Bottleneck Hunter is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Supply-Chain Bottleneck Hunter use?
About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Supply-Chain Bottleneck Hunter?
Skills that share tags, products or a category with Supply-Chain Bottleneck Hunter: AI-Trader Market Intel (HKUDS/AI-Trader, 23k stars), Stock Deep Analysis Workflow (wbh604/UZI-Skill, 7.1k stars), Zhengxi Fund Manager Views Library (lyra81604/zhengxi-views, 1.8k stars) and Supply Chain Bottleneck Hunter (xbtlin/ai-berkshire, 17k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Supply-Chain Bottleneck Hunter?
HKUDS (a GitHub organization) maintains it in HKUDS/Vibe-Trading, which has 35,043 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 8, 2026.
Source: HKUDS/Vibe-Trading on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.