Vibe-Trading is a personal trading agent that gives an AI system tools for market analysis, algorithmic trading, backtesting, and related workflows. It is for users who want an agent to research and evaluate trading strategies or manage simulated and other trading activities. The catalogue contains skills that expose these trading capabilities to compatible agents.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HKUDS/Vibe-Trading --skill bottleneck-huntergit clone --depth 1 https://github.com/HKUDS/Vibe-TradingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hkuds/vibe-trading/bottleneck-hunter)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/bottleneck-hunter"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/bottleneck-hunter/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hkuds/vibe-trading/bottleneck-hunter"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/bottleneck-hunter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00145 | $0.02683 |
| Opus 5 | $0.00072 | $0.01341 |
| Sonnet 5 | $0.00029 | $0.00537 |
| Haiku 4.5 | $0.00015 | $0.00268 |
Grade A, and why
bottleneck-hunter scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- bottleneck-hunter — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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, 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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 191 lines · 145 tokens per session scan A 2b8063b29992
bottleneck-hunter is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,177 stars, last pushed today), licensed MIT. It adds 145 tokens to every session and 2,683 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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