research-discipline

A short self-bias checklist to run at the START of any investment research task (stock screen / sector study / company deep-dive). Four biases that systematically warp AI research — leader-bias (only big caps), English-bias (miss JP/KR/TW players), narrative-bias (chase concept labels), confirmation-bias (only bullish evidence) — plus recency-bias. Load this first, then research with the corrections in mind. Not a workflow, just a 60-second attitude reset that materially improves coverage and honesty.

Quality

92%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

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Recency-bias	You rely on a cached/outdated figure because it ranks high in search.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?

This pairs with:

financial_rigor cross_validate — verify the numbers (data layer)
report_audit — verify the final report (output layer)
this skill — verify the reasoning (thinking layer)

Repository: HKUDS/Vibe-Trading
Path: agent/src/skills/research-discipline/SKILL.md
Commit: 8643fcd

Last updated: 2 days ago
First committed: 27 days ago

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.