# Private Company Research

Deep research framework for pre-IPO / private companies (Ant Group, SpaceX, Stripe...

- Repository: https://github.com/HKUDS/Vibe-Trading (37375 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add HKUDS/Vibe-Trading --skill private-company-research -g -a claude-code -y`
Codex: `npx skills add HKUDS/Vibe-Trading --skill private-company-research -g -a codex -y`
Cursor: `npx skills add HKUDS/Vibe-Trading --skill private-company-research -g -a cursor -y`
Gemini CLI: `npx skills add HKUDS/Vibe-Trading --skill private-company-research -g -a gemini-cli -y`

## Description

Deep research framework for pre-IPO / private companies (Ant Group, SpaceX, Stripe, ByteDance...). Six analyst lenses, business model, financial forensics, competitive landscape, risk & governance, tech & IP, alternative-data signals, run in parallel via run_swarm, then cross-validated for signal consistency before any verdict. Built around the core challenge of private-company work: information is scarce, so every data point carries a confidence label (high / medium / low), inference is shown separately from fact, and 'I don't know' is a valid output. Outputs a fair-value range, exit-path analysis, and an information-gap map. Use for any unlisted company where you need to judge what the business is actually worth.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Private-Company Research: Multi-Lens Deep Framework

Deep research on an unlisted company (e.g. Ant Group, ByteDance, SpaceX, Stripe).

**Ultimate goal**: under information scarcity, recover the company's **true value**, not the market valuation, but what the business is actually worth.

## Framework Characteristics

Private vs public research: no standardized financials (multi-source patchwork + cross-validation); few valuation anchors (funding rounds, comparables, scenarios); large information asymmetry ("jigsaw" research); uncertain exit path (IPO / M&A / secondary).

## AI Research Bias Self-Check (core premise)

Private companies are where AI bias is worst. Watch for:

- **False conservatism**, with little data, AI gives conservative/vague conclusions, but scarce data ≠ bad company.
- **False precision**, to fill the template, AI disguises "reasonable guess" as "sourced analysis".
- **Comparables trap**, forcing a public-comp overlay inherits public-market logic and misses private-specific value.
- **Survivorship bias**, what's searchable online is mostly company-propagated good news.

**Counter**: prefer leaving blanks ("I don't know") over filling tables with speculation to fake certainty; label every data point with confidence (🟢high/🟡medium/🔴low); separate verifiable fact from inference; when information is extremely scarce, switch to "first-principles mode" and answer only: ① what real problem does this business solve? ② why this team? ③ ceiling if it succeeds / how it dies if it fails? ④ the key validation node at this stage?

> Invert the asymmetry: the market knows little about private companies → pricing is inefficient → that's exactly where alpha may live.

## Execution

Six lenses, best run **in parallel** (via `run_swarm`, one worker per lens; or sequentially via `web_search`):

The rest: https://github.com/HKUDS/Vibe-Trading/blob/HEAD/agent/src/skills/private-company-research/SKILL.md