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research-discipline

Check investment research for bias before searching and before writing conclusions. A short checklist broadens coverage beyond large companies and English sources, challenges bullish assumptions, and checks whether important figures are current.

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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.

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适用场景

  • Include smaller companies and suppliers in stock screens
  • Expand supply chain research to local language sources
  • Test investment theses against opposing evidence
  • Check the dates of key figures before drawing conclusions

包含的文件(1)

  • SKILL.md2.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

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

BiasHow it showsCorrection
Leader-biasSearch 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-biasYou 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-biasYou 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-biasOnce 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-biasYou 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

  1. Before the first search, read the rows above.
  2. Write the thesis in one sentence, then for each bias ask: "am I about to fall into this?"
  3. Consciously broaden the query plan: small-caps? non-English markets? the bear case? the latest data?
  4. 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)

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