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-generategit 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-generate)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/strategy-generate"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/strategy-generate/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-generate"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/strategy-generate.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 | $0.00021 | $0.04279 |
| Opus 5 | $0.00010 | $0.02139 |
| Sonnet 5 | $0.00004 | $0.00856 |
| Haiku 4.5 | $0.00002 | $0.00428 |
Grade A, and why
strategy-generate 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 2d 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Workflow
- Requirements parsing: parse user intent, extract instrument codes, time range, and strategy logic, then write
config.json - Strategy design: think through the 5 questions of data / signal / position sizing / backtest / validation
- Strategy coding: write
code/signal_engine.py(following theSignalEnginecontract) - Syntax check:
bash("python -c \"import ast; ast.parse(open('code/signal_engine.py').read()); print('OK')\"") - Run backtest: call the
backtesttool (built into the engine; no need to writerun_backtest.py) - Evaluate results: read
artifacts/metrics.csvand judge by the review criteria - Iterative fixing: if results are poor, modify with
edit_file→ runbacktest→ re-evaluate
You only need to write signal_engine.py and config.json. The backtest tool automatically handles data loading and backtest execution.
Requirements Parsing
Extract the following from the user's description:
- Instrument codes: process them according to the normalization rules below
- Time range: if the user does not specify dates, default to 10 years back from today (for example, if today is
2026-03-18, thenstart_date=2016-03-18,end_date=2026-03-18) - Indicator warm-up: a long lookback (MA200, a 252-day z-score) needs bars from before the requested period. Move
start_dateback to load them and declare the boundary withwarmup_bars— the requested period is what gets graded, and undeclared warm-up bars are graded too. Silently backdatingstart_dateby a year turns a 10-year backtest into an 11-year one that still calls itself 10 years: the extra year's trades, CAGR and benchmark all enter the report, the run succeeds, and the numbers look internally consistent - Strategy logic: entry / exit conditions and indicator parameters
If critical information is missing, you must ask the user instead of guessing:
- Instrument not specified → ask which instrument they want to backtest (offer several popular suggestions)
- Strategy description is vague (for example, "help me build a strategy") → provide 2-3 strategy directions for the user to choose from
- Mixed markets but not clearly specified → confirm the data source
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago Changed 3659a1fcef09
- 7d ago First seen · 220 lines · 21 tokens per session scan A 1aecc3a57cac
strategy-generate is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,177 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 4,279 once invoked, about $0.0001 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.
Other skills, from other repositories
hyperliquid
Use when backtesting, deploying, checking funding readiness, or debugging a Hyperliquid strategy through Superior Trade Unified API — writing Freqtrade configs and strategy code, running sweeps, checking managed-wallet balances, trading HIP-3 perps, or diagnosing a deployment that will not start or trade.
polymarket
Use when the user wants to trade, research, or backtest Polymarket prediction markets through Superior Trade — finding markets by slug or event URL, placing a single immediate market order, writing NautilusTrader strategies, running filled-data backtests, funding pUSD, or deploying and monitoring a live Polymarket…
backtesting
Use when running, interpreting, or designing backtests on Superior Trade — anything about backtest windows, trade-count thresholds, exit-reason mix, parameter sweeps, walk-forward validation, zero-trade diagnosis, compute-cost estimation, or "is this backtest result trustworthy?". Pair with the relevant strategy…
basis-arb
Use when the user asks for spot-perp basis trade, basis arbitrage, cash-and-carry, perp discount, or any setup that reads the spot–perp basis as a positioning signal. Long-perp leg only — pure two-leg basis arb requires a paired spot short (or long) which Freqtrade can't run cleanly. The strategy below captures the…
fees-optimizations
Use when the user asks about fees, fee optimization, slippage, maker vs taker, post-only or ALO orders, fee tiers, builder code fees, effective spread, order pricing, lowering trading costs, or why a live Hyperliquid Freqtrade strategy underperforms its backtest. Also use proactively for high-turnover designs (5m or…
aerodrome
Use when creating, validating, backtesting, deploying, sizing, or troubleshooting Aerodrome/Base spot trading strategies through the Superior Trade API, especially Freqtrade configs using exchange.name "aerodrome", AERO/USDC or CHECK/USDC pairs, AMM market swaps, wallet/gas balance checks, no-orderbook pricing, or…