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Agent skill

Investment Research Bias Check

by HKUDS in HKUDS/Vibe-Trading

A short checklist to read at the start of stock screens, sector studies and company deep-dives that counters leader, English-language, narrative, confirmation and recency bias.

MITAuto-check passedBusiness, Finance & HR

Install Investment Research Bias Check

skills CLI
$ npx skills add HKUDS/Vibe-Trading --skill research-discipline -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install HKUDS/Vibe-Trading research-discipline --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agent/src/skills/research-discipline .claude/skills/research-discipline && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
research-discipline
GitHub stars
35k
Token cost
~720 tokens
SKILL.md length
326 words
Files
1
Skills in repo
89
Repo updated
First seen
Licence
MIT

At a glance

A short checklist to read at the start of stock screens, sector studies and company deep-dives that counters leader, English-language, narrative, confirmation and recency bias.

  • Works in 4 steps: Before the first search, read the rows… → Write the thesis in one sentence, then… → Consciously broaden the query plan:… → …
  • Starting a stock screen or sector study where coverage could be skewed toward large caps
  • SKILL.md covers The biases and their corrections and How to apply
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

This skill is a quick attitude reset before investment research begins. It names five biases that distort AI-generated research and pairs each with a correction. Leader-bias is countered by searching small and mid caps and suppliers, English-bias by searching Japanese, Korean and Taiwanese markets in their own languages, and narrative-bias by ignoring concept labels in favor of the real product and financial statements.

Confirmation-bias is handled by searching the bear case for every bullish point and citing at least one disconfirming data point per conclusion, and recency-bias by checking the date of every material figure and marking old ones as possibly stale. The agent writes its thesis in one sentence, asks which bias it is about to fall into, widens the query plan, and re-checks before drawing conclusions. The text positions it as the reasoning check alongside separate number and report audits.

When your agent uses it

  • Starting a stock screen or sector study where coverage could be skewed toward large caps
  • Beginning a company deep-dive and wanting the bear case searched deliberately
  • Reviewing an AI-written research note for stale figures and one-sided evidence

Example prompts

  • “Before we screen semiconductor suppliers, run the research bias checklist.”
  • “Re-check this company write-up for confirmation bias and stale numbers.”
  • “Plan a supply-chain study of battery makers that also covers Korean and Japanese players.”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  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…

What it can do on your machine

Read from SKILL.md and the folder at commit b1f6ce7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Investment Research Bias Check loads about 720 tokens when it runs. Until then it costs about 132 tokens; SKILL.md has 326 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~132
When it runs · the whole SKILL.md, loaded when a task matches
~720

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from HKUDS/Vibe-Trading at commit b1f6ce7, republished under its MIT licence (© HKUDS). 326 words, ~720 tokens.

Download SKILL.mdSave it as .claude/skills/research-discipline/SKILL.md (or your agent's skills folder).
name
research-discipline
description
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.
category
analysis

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)

© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in agent/src/skills/research-discipline of HKUDS/Vibe-Trading.

Open the folder on GitHubat commit b1f6ce7

Compare with similar skills

Investment Research Bias Check next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Investment Research Bias Check compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Investment Research Bias Check this skillHKUDS/Vibe-Trading35k—~720Automated safety check: PassMIT
Financial Data Cross-Validationxbtlin/ai-berkshire17k—~1.4kAutomated safety check: PassMIT
Zhengxi Fund Manager Views Librarylyra81604/zhengxi-views1.8k—~1.6kAutomated safety check: PassMIT
AI-Trader Market IntelHKUDS/AI-Trader23k—~1.1kAutomated safety check: PassNone
Stock Deep Analysis Workflowwbh604/UZI-Skill7.1k—~9.1kAutomated safety check: NotesMIT
Supply Chain Bottleneck Hunterxbtlin/ai-berkshire17k—~2.6kAutomated safety check: PassMIT

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Categories

Questions about Investment Research Bias Check

What does Investment Research Bias Check do?

A short checklist to read at the start of stock screens, sector studies and company deep-dives that counters leader, English-language, narrative, confirmation and recency bias. This skill is a quick attitude reset before investment research begins. It names five biases that distort AI-generated research and pairs each with a correction.

When should I use Investment Research Bias Check?

Investment Research Bias Check fits situations like: starting a stock screen or sector study where coverage could be skewed toward large caps; beginning a company deep-dive and wanting the bear case searched deliberately; reviewing an AI-written research note for stale figures and one-sided evidence.

How do I install Investment Research Bias Check in Claude Code?

Run `npx skills add HKUDS/Vibe-Trading --skill research-discipline -a claude-code`. Or copy the skill folder (agent/src/skills/research-discipline in HKUDS/Vibe-Trading) into .claude/skills/research-discipline in your project. Claude Code loads it when a task matches its description.

How do I install Investment Research Bias Check in Codex?

Run `npx skills add HKUDS/Vibe-Trading --skill research-discipline -a codex`. Or copy the skill folder (agent/src/skills/research-discipline in HKUDS/Vibe-Trading) into .agents/skills/research-discipline in your project. Codex loads it when a task matches its description.

Can I use Investment Research Bias Check in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HKUDS/Vibe-Trading --skill research-discipline -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/research-discipline, .gemini/skills/research-discipline, .github/skills/research-discipline and .opencode/skills/research-discipline in your project.

What does Investment Research Bias Check need to run?

SKILL.md names no scripts, command-line tools or credentials: Investment Research Bias Check is instructions for the agent only.

Does Investment Research Bias Check access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Investment Research Bias Check safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Investment Research Bias Check use?

Investment Research Bias Check is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Investment Research Bias Check use?

About 720 tokens (SKILL.md is roughly 2.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Investment Research Bias Check?

Skills that share tags, products or a category with Investment Research Bias Check: Financial Data Cross-Validation (xbtlin/ai-berkshire, 17k stars), Zhengxi Fund Manager Views Library (lyra81604/zhengxi-views, 1.8k stars), AI-Trader Market Intel (HKUDS/AI-Trader, 23k stars) and Stock Deep Analysis Workflow (wbh604/UZI-Skill, 7.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Investment Research Bias Check?

HKUDS (a GitHub organization) maintains it in HKUDS/Vibe-Trading, which has 35,097 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 9, 2026.

Source: HKUDS/Vibe-Trading on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.