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 research-disciplinegit 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/research-discipline)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/research-discipline"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/research-discipline/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/research-discipline"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/research-discipline.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 | $0.00114 | $0.00660 |
| Opus 5 | $0.00057 | $0.00330 |
| Sonnet 5 | $0.00023 | $0.00132 |
| Haiku 4.5 | $0.00011 | $0.00066 |
Grade A, and why
research-discipline 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 8d 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:
- research-discipline — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 32 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Research Bias Self-Check
Run this at the start of any research task (screening, sector study, company deep-dive). These biases systematically warp AI-generated research — 60 seconds here materially improves coverage and intellectual honesty.
The biases and their corrections
| Bias | How it shows | Correction |
|---|---|---|
| Leader-bias | Search results are dominated by large-caps; you end up analyzing only the obvious names. | Deliberately search small/mid-caps and suppliers; add small cap / mid cap / supply chain to queries. Ask: "who is NOT in the top-10 that should be here?" |
| English-bias | You miss Japanese / Korean / Taiwanese / European players because English sources under-cover them. | For any hardware/supply-chain thesis, explicitly search JP/KR/TW markets in their own languages — they are often the actual choke-point owners. |
| Narrative-bias | You get pulled in by a concept label ("AI stock", "new energy") and analyze the marketing instead of the business. | Ignore the label; look at the actual product, unit economics, and financial statements. A company tagged "AI" may have no AI revenue. |
| Confirmation-bias | Once a thesis forms, you only search for evidence that supports it. | Force a Munger inversion: for every bull point, deliberately search the bear case ("X risks / problems / bear case"). Cite at least one disconfirming data point per conclusion. |
| Recency-bias | You rely on a cached/outdated figure because it ranks high in search. | For any material number, check its date. Prefer the last 30 days; mark anything older than a year as "possibly stale". |
How to apply
- Before the first search, read the rows above.
- Write the thesis in one sentence, then for each bias ask: "am I about to fall into this?"
- Consciously broaden the query plan: small-caps? non-English markets? the bear case? the latest data?
- After research, before writing conclusions, re-check: did I cite any disconfirming evidence? did I miss a non-English player? is any key figure stale?
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.
- 8d ago First seen · 32 lines · 114 tokens per session scan A be1a1297dbb3
research-discipline is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,258 stars, last pushed today), licensed MIT. It adds 114 tokens to every session and 660 once invoked, about $0.0006 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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