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-dev-managergit 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-dev-manager)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/strategy-dev-manager"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/strategy-dev-manager/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-dev-manager"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/strategy-dev-manager.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 | $0.00031 | $0.02866 |
| Opus 5 | $0.00015 | $0.01433 |
| Sonnet 5 | $0.00006 | $0.00573 |
| Haiku 4.5 | $0.00003 | $0.00287 |
Grade A, and why
strategy-dev-manager 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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy Development Manager
Purpose
SDM orchestrates the full lifecycle from academic paper or research report to validated factor or strategy. It ingests documents, extracts quantitative signals, implements and backtests them through the existing tool chain, evaluates results against statistical thresholds, and monitors long-term decay. SDM does not reinvent any step. It delegates to the tools already available (read_document, factor_analysis, backtest, alpha_bench, and the hypothesis/autopilot stack) and adds a thin coordination layer with persistent artifact tracking.
Use this skill whenever a user wants to go from "here is a paper" to "I have a working, monitored factor or strategy in the system."
When to Use
Decision tree for routing user requests:
- User provides a paper or report path → Phase 1: INGEST
- User says "extract factors from this paper" → Phase 2: EXTRACT
- User says "implement and backtest" or "run the backtest" → Phase 3: IMPLEMENT
- User says "evaluate results" or "check if it works" → Phase 4: EVALUATE
- User says "check decay" or "monitor factors" → Phase 5: MONITOR
- User says "disable factor" →
sdm_status(action="disable", artifact_id=...) - User says "enable factor" →
sdm_status(action="enable", artifact_id=...) - User says "list my factors" or "show status" →
sdm_status(action="list")
When the user's intent spans multiple phases (for example "read this paper and build a factor"), run the phases sequentially from INGEST through EVALUATE.
Workflow
Phase 1: INGEST
Parse the source document and classify its content.
- Call
read_document(paper_path)to extract the full text from the PDF or report. - Classify the paper type:
- factor-research: the paper proposes one or more cross-sectional factors with formulas (for example Jegadeesh and Titman 1993, Fama-French 1993)
- strategy: the paper describes entry/exit rules, position sizing, and risk management (for example Avramov and Chordia 2006, turtle trading)
- mixed: the paper contains both factor definitions and strategy rules
- Extract key information from the parsed text:
- Methodology description
- Mathematical formulas (preserve LaTeX notation)
- Variable definitions and data requirements
- Universe and time period studied
- Performance metrics reported in the paper
What ships with it
8 files 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.
- examples.md 7.6 KB
- references/decay_thresholds.md 1.9 KB
- references/scheduled_decay_scan.md 7.4 KB
- references/strategy_extraction_guide.md 11 KB
- references/strategy_metrics.md 9.5 KB
- templates/decay_report.md 805 B
- templates/factor_signal_engine.py 2.7 KB runs code
- templates/strategy_signal_engine.py 3.1 KB runs code
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 · 228 lines · 31 tokens per session scan A 29f53773bd0f
strategy-dev-manager is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,258 stars, last pushed yesterday), licensed MIT. It adds 31 tokens to every session and 2,866 once invoked, about $0.0002 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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