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 alpha-zoogit 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/alpha-zoo)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/alpha-zoo"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/alpha-zoo/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/alpha-zoo"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/alpha-zoo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00079 | $0.01036 |
| Opus 5 | $0.00039 | $0.00518 |
| Sonnet 5 | $0.00016 | $0.00207 |
| Haiku 4.5 | $0.00008 | $0.00104 |
Grade A, and why
alpha-zoo 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:
- alpha-zoo — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Alpha Zoo
Purpose
When the user asks about prebuilt cross-sectional alphas — Kakushadze 101, GTJA 191, Qlib 158, Fama-French / Carhart — or wants to bench a whole zoo on an investable universe (CSI 300, S&P 500, BTC-USDT, ...), this skill orients you. The zoo is the curated library; the bench is the evaluator.
Tools Available
| Tool | When to use |
|---|---|
alpha_zoo |
Browse the library. action=list_alphas to enumerate (filterable by zoo / theme / universe), action=get_alpha for one alpha's metadata, action=health for registry load status. |
alpha_bench |
Run IC / IR on one alpha or a whole zoo over a universe + period. Emits an HTML report. |
factor_analysis |
Ad-hoc factor evaluation from a user-supplied factor CSV + return CSV. Use this when the user has their own factor (not in the zoo). |
Decision Tree
- "list all momentum alphas" →
alpha_zoowithaction=list_alphas, theme=momentum. - "show me gtja191_alpha_001" →
alpha_zoowithaction=get_alpha, alpha_id=gtja191_alpha_001. - "bench all of GTJA 191 on CSI 300 from 2020 to 2024" →
alpha_benchwithzoo=gtja191, universe=csi300, period=2020-2024. - "is the registry healthy" →
alpha_zoowithaction=health— surfacesloaded,failed, and per-error reasons. - User uploads
my_factor.csv→factor_analysis(zoo tools are for prebuilt alphas only).
Zoo Inventory
| Zoo | Description | Approx. count |
|---|---|---|
kakushadze101 |
Formulaic alphas from Kakushadze's 2015 paper. Mix of momentum, reversal, volume, and microstructure. | ~101 |
gtja191 |
Guotai Junan 191 alphas — A-share focused cross-sectional factors. | ~191 |
qlib158 |
Microsoft Qlib's 158 alpha factors — features tuned for ML pipelines. | ~158 |
classical |
Fama-French 3/5-factor + Carhart momentum. | <10 |
Counts are nominal; check alpha_zoo action=health for the live count currently loaded.
Constraints
- No per-stock per-date factor values are surfaced to the agent. IC results are aggregate stats (mean / std / IR / positive-ratio); the HTML report shows top-N by IR plus formulas, never the underlying panel.
- Lookahead is banned in the operator set.
delta(df, d)requiresd >= 1; the negative-shiftRef(df, -n)form does not exist. Seedocs/alpha-zoo/spec.mdfor the full operator catalogue. - Universe loaders may not be wired for every market yet. When
alpha_benchreturnsuniverse loader for X not yet implemented, that's the W2 scaffold — the universe is recognised but the data pull lands in W4. - Do not expose absolute filesystem paths in agent output. The bench tool writes to
~/.vibe-trading/reports/by default; refer to it by that shorthand, not by the resolved absolute path. alpha_zoois read-only.alpha_benchwrites a single HTML file per run — no scratch state elsewhere.
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 · 58 lines · 79 tokens per session scan A 38a2168fe94d
alpha-zoo is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,177 stars, last pushed yesterday), licensed MIT. It adds 79 tokens to every session and 1,036 once invoked, about $0.0004 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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