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 skloxo/TideTrading --skill alpha-zoogit clone --depth 1 https://github.com/skloxo/TideTradingWrote 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/skloxo/tidetrading/alpha-zoo)<a href="https://agentmods.dev/skills/skloxo/tidetrading/alpha-zoo"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/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/skloxo/tidetrading/alpha-zoo"><img src="https://agentmods.dev/badge/skills/skloxo/tidetrading/alpha-zoo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 10d 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.
This is a copy
100% identical to alpha-zoo — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 10d ago First seen · 58 lines · 79 tokens per session scan A 38a2168fe94d
alpha-zoo is a skill published in the GitHub repository skloxo/TideTrading (10 stars, last pushed 3d ago), 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. It is 100% identical to alpha-zoo, differing in 0 lines, and is treated as a copy.
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