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 strategy-discoverygit 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/strategy-discovery)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/strategy-discovery"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/strategy-discovery/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/strategy-discovery"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/strategy-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- 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 | $0.00062 | $0.04525 |
| Opus 5 | $0.00031 | $0.02263 |
| Sonnet 5 | $0.00012 | $0.00905 |
| Haiku 4.5 | $0.00006 | $0.00453 |
Grade A, and why
strategy-discovery 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy Discovery
Purpose
Strategy Discovery is the single entry point for two questions: what strategies exist, and what state are they in. It fronts the Alpha Zoo registry and the SDM strategy store with one facade and answers with computed evidence instead of labels, and it reports the freshness of that evidence on every returned row.
It supersedes the earlier closed-registry attempt. That design attached boolean scenario tags (works in bear markets: yes/no) to a curated list. This skill replaces tags with per-regime evidence rows: every claim that a strategy works in a regime must come from a computed, reproducible backtest stored as evidence, never from curation or inference.
The three query tools are read-only: they never register, mutate, or delete strategies. To add or change strategies, use the strategy-dev-manager and alpha-zoo workflows — Strategy Discovery only reports what those workflows have produced. The fourth tool, refresh_strategy_evidence, is the single write in the surface: it rebuilds ONLY the disposable evidence cache from local backtest run artifacts (see Populating & Refreshing Evidence and Composition Guarantee).
When to Use
Decision tree for routing user requests:
- User asks what strategies exist / "list available strategies" →
list_strategies(limit=..., offset=..., source=...) - User asks which strategy fits a regime or threshold ("what works in bear markets?", "anything with Sharpe above 1?") →
query_strategies(regime=..., min_sharpe=..., ...) - User asks for the evidence behind one specific strategy →
get_strategy_evidence(strategy_id=..., regime=...) - User asks to populate or refresh the evidence cache ("turn my backtest runs into evidence", "the evidence is stale, refresh it") →
refresh_strategy_evidence(manifest_path=...)— this rebuilds the disposable cache from run artifacts; it is NOT strategy creation or registration - User asks to create, backtest, or register a strategy → this is NOT this skill; route to
strategy-generate/strategy-dev-manager/alpha-zoo
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.
- 9d ago First seen · 213 lines · 62 tokens per session scan A 7f774c0338c2
strategy-discovery is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,258 stars, last pushed yesterday), licensed MIT. It adds 62 tokens to every session and 4,525 once invoked, about $0.0003 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 2026-09-03.
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