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Agent skill

Private Company Research

by HKUDS in HKUDS/Vibe-Trading

Runs a six-lens research framework on pre-IPO and private companies, labels every data point by confidence, and produces a fair-value range, exit-path analysis and information-gap map.

MITAuto-check passedBusiness, Finance & HR

Install Private Company Research

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

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

GitHub CLI
$ gh skill install HKUDS/Vibe-Trading private-company-research --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/private-company-research .claude/skills/private-company-research && 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
private-company-research
GitHub stars
35k
Token cost
~3.5k tokens
SKILL.md length
1,486 words
Files
1
Skills in repo
89
Repo updated
First seen
Licence
MIT

At a glance

Runs a six-lens research framework on pre-IPO and private companies, labels every data point by confidence, and produces a fair-value range, exit-path analysis and information-gap map.

  • Works in 4 steps: Data conflict arbitration: same metric… → Signal consistency matrix:… → Information jigsaw: white zones (known)… → …
  • Estimating what an unlisted company is worth from patchy public information
  • SKILL.md covers Framework Characteristics, AI Research Bias Self-Check…, Execution and Lens 1: Business Model & Users…, plus 9 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Built for unlisted companies where data is scarce, the skill sets out to estimate what a business is actually worth rather than its last funding valuation. It opens with a bias self-check covering false conservatism, false precision, the comparables trap and survivorship bias, and counters them by leaving blanks instead of speculating, labeling each data point high, medium or low confidence, and keeping fact separate from inference. With very little data it switches to a first-principles mode of four basic questions.

Research then runs six lenses, ideally in parallel through run_swarm with one worker per lens: business model, financial forensics, competitive landscape, risk and governance, technology and IP, and alternative-data signals. Results are cross-checked for consistency before any verdict. The outputs are a fair-value range, exit-path analysis across IPO, M&A and secondary routes, and a map of information gaps.

When your agent uses it

  • Estimating what an unlisted company is worth from patchy public information
  • Running business, financial, competitive, risk, technology and alternative-data lenses on one target
  • Mapping which key facts about a private company are still unknown
  • Analyzing exit paths such as IPO, M&A or a secondary sale

Example prompts

  • “Research ByteDance with the six-lens framework and give me a fair-value range.”
  • “Which facts about this pre-IPO company are unknown, and how confident are the rest?”
  • “Analyze the likely exit paths for a late-stage private company.”
  • “Cross-check the financial and alternative-data lenses for signal consistency.”

Requirements

  • Web search tools, and run_swarm for running the lenses in parallel

Workflow steps

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

  1. Data conflict arbitration: same metric across sources — list all, state which is adopted and why.
  2. Signal consistency matrix: business-growth signal vs hiring trend? tech-leadership narrative vs patent/talent data? valuation level vs…
  3. Information jigsaw: white zones (known) / gray (clues but uncertain) / black (unknown).
  4. Bias check: is positive info detailed while negative is brief? Does every positive judgment have a reverse check?

What it can do on your machine

Read from SKILL.md and the folder at commit 8e43007. 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

Private Company Research loads about 3.5k tokens when it runs. Until then it costs about 188 tokens; SKILL.md has 1,486 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~188
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k

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 8e43007, republished under its MIT licence (© HKUDS). 1,486 words, ~3,509 tokens.

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

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):

RoleLens
business-decoderBusiness model + product/user analysis: "what is this business, essentially"
financial-detectiveFinancial patchwork + valuation: "recover the true financial picture under missing data"
competitive-mapperIndustry + competition + substitution: "who competes, who could disrupt"
risk-governance-analystRisk全景 + management/governance/investors: "what could go wrong, who's at the helm"
tech-ip-analystTech stack / patents / R&D / moat: "is the tech barrier real and durable"
signal-minerAlternative data (hiring / patents / litigation / app / supply chain): "clues beyond the usual sources"

You (team-lead) integrate, patch the picture, cross-validate, output the final report.

Lens 1: Business Model & Users (business-decoder)

  • Core business definition: one sentence (Duan Yongping style: plain language to a smart layperson). What problem? For whom? If the company didn't exist, what would users do? Is demand rigid (cut in a downturn)?
  • Revenue model: ads/commission/subscription/take-rate/financial/SaaS/hardware; mix and trend; monetization efficiency (ARPU / take rate / conversion); recurring vs one-off; concentration; predictability.
  • Unit economics: CAC (paid vs organic, by channel, trend), LTV, LTV/CAC, payback, marginal cost, scale-inflection point.
  • Product matrix & flywheel: core + extension + incubation; network/data/scale flywheel; iteration speed.
  • Users: MAU/DAU (from QuestMobile/Sensor Tower/SimilarWeb), S-curve stage, stickiness (DAU/MAU, retention), profile, reputation (App Store trend, social sentiment).
  • Moat (6 dimensions, ★1-5): network effects, switching costs, brand mind, data barrier, regulatory license, scale economies — each with evidence + trend (widening/stable/narrowing) + durability. Overall: wide/narrow/none.

Lens 2: Financial Detective

No standard financials; multi-source patchwork + cross-validation. Every data point: source, time, confidence, derivation.

Source priority: 🟢 prospectus/regulatory filings, parent-company annual report disclosure, regulatory penalties, bond/ABS offering documents → 🟡 business registry, funding news, third-party reports, deep media (LatePost/The Information/36Kr/Bloomberg) → 🔴 industry extrapolation, ex-employee leaks.

Key metrics: revenue (scale/growth/mix/quantity×price), cost (gross margin/R&D/sales/G&A rates, vs peers), profit (EBITDA/net income/profitability timeline), cash flow (operating/burn rate/runway), efficiency (per-capita revenue, capital efficiency).

Cross-validation: list every source for the same metric; check convergence across methods; flag single-source ("isolated evidence") data.

Funding history: full timeline (round/amount/valuation/lead investor); health of the curve, interval, down-rounds, whether existing investors keep participating; latest-round terms (liquidation preference / anti-dilution / ratchet) and their effect on common-share value.

Valuation (multi-method): ① last-round (adjust for liquidation prefs, 20-40% discount); ② comparable public comps (3-5, PS/PE/EV-EBITDA, liquidity discount 20-30%); ③ DCF scenarios (bear/base/bull, each assumption grounded); ④ terminal-value rollback (5/10y terminal state → implied IRR); ⑤ transaction comps (recent M&A/funding multiples).

Valuation synthesis: do the methods converge? If divergent, explain. Distinguish "fair value" and "conservative (margin-of-safety) value".

Lens 3: Competitive Landscape (competitive-mapper)

  • Market: TAM/SAM/SOM, penetration, stage (emergence/growth/mature/decline), growth drivers.
  • Value chain (text map): upstream → company's link (profit pool share) → downstream; bargaining power; structural shifts.
  • Porter's five forces (★1-5): rivalry, new entrants, substitutes, supplier power, buyer power.
  • Competitor scan: direct/indirect/substitute/potential entrants (giants) — share, funding, strengths, weaknesses, threat level. Multi-dimensional compare with 2-3 closest competitors.
  • Dynamics: last-12-month changes; infer competitor strategy from hiring/patents/products; tech (esp. AI) and regulation effects; winner-take-all vs oligopoly.
  • Scenarios: company wins / coexistence / disrupted — conditions and probabilities.
  • Global benchmarks: overseas analogs, path, valuation, post-IPO performance, and benchmark limitations.

Lens 4: Risk & Governance (risk-governance-analyst)

  • Founder/CEO: background, foresight (3-year prediction accuracy), execution (promise delivery), values, controversies. ★1-5.
  • Core team: backgrounds, 2-year talent flow (who left/joined, net), complementarity, culture signals (Glassdoor trends), key-person dependency.
  • Equity & governance: founder control (dual-class / concerted action / VIE), dilution trend, employee equity; board, related-party, same-industry competition, majority/minority conflicts.
  • Investor roster: lead-investor brand, strategic capital synergy, exit pressure (fund life / secondary sales / ratchet maturity), red flags.
  • Risk matrix: regulatory / competition / tech / talent / funding / IPO / geopolitical / monetization / governance / compliance / macro / ESG — each probability × impact × severity × hedgeability × monitor.
  • Exit paths: A/HK/US IPO, M&A, secondary, SPAC, stay-private — probability, window, valuation, preconditions, obstacles.
  • Worst case (Munger inversion): 3 specific failure paths + probabilities; liquidation value; why smart money doesn't invest (≥5 reasons); failed analogs; the "thesis broken" signal.

Lens 5: Tech & IP (tech-ip-analyst)

  • Tech stack: inferred from hiring/blog/open-source/talks; tech-debt signals (refactor hiring, stack switches, outage complaints).
  • Patents (Google Patents/CNIPA/USPTO): total/pending/last-2y/field/citation/international; quality (core patents? aligned to business? litigation?); trend; vs competitors.
  • R&D: investment (headcount/expense rate vs peers), output (papers/conferences/open-source/blog), efficiency (research-to-product, commercialization).
  • Tech talent: core leaders' backgrounds, density (top-institution share), comp competitiveness, attrition signals, hiring direction (→ strategy).
  • Tech moat (★1-5): algorithm/model, data, engineering, talent, ecosystem — each with durability (AI-era half-life may be short).
  • AI/new-tech impact: is the company a beneficiary or a target of disruption?
Show full SKILL.md (568 more words)Show less

Lens 6: Alternative-Data Signals (signal-miner)

Private companies have limited conventional info; alt-data often beats news.

  • Hiring (LinkedIn/Boss/Indeed): scale/trend, structure (R&D/product/sales/data/international/compliance/IR — IR hiring = IPO signal; compliance = regulatory or IPO; JD tech stack = strategy).
  • App/product (App Store/七麦/SimilarWeb): rank, rating trend, downloads, update frequency, complaint themes, web traffic.
  • Social sentiment (Weibo/Zhihu/Xiaohongshu/X/Reddit): official engagement, organic discussion, KOL views, negative events, insider leaks.
  • Business/legal (天眼查/企查查): registry/paid-in/equity changes/subsidiaries (new = new biz; deregistered = contraction)/scope changes; litigation/arbitration/penalties/enforcement.
  • Supply chain: known suppliers (if listed, check their filings), procurement, partner evaluation.
  • Digital footprint: registered domains (new = new biz), subdomains (api/pay → architecture), trademarks (new brands).
  • Industry exposure: exec talks, awards, government/association interaction, media frequency/quality.
  • Secondary-market signals (if any): SharesPost/EquityZen, implied valuation vs last round, employee selling.

Anomaly list (most important): things inconsistent with the company's narrative; inconsistent with industry norms; sudden changes (hiring freeze / executive departures); unexplained.

Cross-Validation (team-lead, mandatory)

Before synthesis, the team-lead must:

  1. Data conflict arbitration: same metric across sources — list all, state which is adopted and why.
  2. Signal consistency matrix: business-growth signal vs hiring trend? tech-leadership narrative vs patent/talent data? valuation level vs competitive position? management narrative vs action signals? (contradictions must be explained)
  3. Information jigsaw: white zones (known) / gray (clues but uncertain) / black (unknown).
  4. Bias check: is positive info detailed while negative is brief? Does every positive judgment have a reverse check?

Final Report Structure

  1. One-line conclusion (50-100 words): what's it worth, why.
  2. Company snapshot (with confidence column).
  3. Six-lens scorecard (★1-5 + core judgment + confidence + completeness), overall score.
  4. Key data jigsaw (only cross-validated, with source count + confidence).
  5. Signal-consistency matrix.
  6. Per-lens summary (3-5 top findings each).
  7. Fair value assessment: business essence + 7-dimension moat card + 5-method valuation + fair value range (conservative/reasonable/optimistic + current market valuation + margin of safety %).
  8. Investment thesis: bull 5-7 (with sources) vs bear 5-7 (with sources), which side is stronger.
  9. Risk matrix (top 3 + mitigation).
  10. Exit-path assessment.
  11. Investment decision table (one-pager: core logic 3 sentences + value range + key assumptions & validation nodes + fatal risks & "thesis broken" signals + conclusion + expected return/timeline).
  12. Information-gap map (dimension / known / missing / missing-impact / how-to-get): does the gap affect the core conclusion? If yes, state "under missing X, conclusion confidence is Y".
  13. Tracking checklist (item / frequency / source / metric / alert threshold).
  14. Summary paragraph (150-250 words).

Save via write_file to reports/{company}/{company}-private-{YYYYMMDD}.md. Run report_audit on the numbers as a quality gate.

Data Labeling Standard (strict)

  • Every key data point: source (specific to media + article), time (year/month), confidence (🟢 prospectus/official / 🟡 credible media / 🔴 estimate/rumor).
  • Conflicting data: list all + explain difference and adoption.
  • Separate fact from inference: fact in normal text; inference in italics with derivation.
  • Missing info: explicitly mark "data missing"; never fabricate.

Key Principles

  1. 6 lenses in parallel (run_swarm, or sequential).
  2. Transparent derivation — show the math and assumptions; don't hand-wave numbers.
  3. Cross-validate — key data ≥2 sources; conflicts all listed.
  4. Signal-consistency check — mandatory cross-lens check at synthesis.
  5. Clear conclusion — don't dodge invest/watch/avoid; state confidence.
  6. Search in both EN and CN — private-company info spans both.
  7. Honest blanks — distinguish "sourced analysis" from "speculative fill"; "this dimension lacks data, no meaningful conclusion" is acceptable.
  8. Alt-data is not noise — hiring/patents/litigation/app data may be closer to truth than news.
  9. True-value focus — the goal is what the business is worth, not a pretty report. If info can't support a reliable valuation, say so.
  10. Scarce data ≠ bad company — short AI output ≠ low certainty. Under extreme scarcity, switch to first-principles mode.

© 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/private-company-research of HKUDS/Vibe-Trading.

Open the folder on GitHubat commit 8e43007

Compare with similar skills

Private Company Research 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.

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Management Deep Divexbtlin/ai-berkshire17k—~1.7kAutomated safety check: PassMIT

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Categories

Questions about Private Company Research

What does Private Company Research do?

Runs a six-lens research framework on pre-IPO and private companies, labels every data point by confidence, and produces a fair-value range, exit-path analysis and information-gap map. Built for unlisted companies where data is scarce, the skill sets out to estimate what a business is actually worth rather than its last funding valuation. It opens with a bias self-check covering false conservatism, false precision, the comparables trap and survivorship bias, and counters them by leaving blanks instead of speculating, labeling each data point high, medium or low confidence, and keeping fact separate from inference.

When should I use Private Company Research?

Private Company Research fits situations like: estimating what an unlisted company is worth from patchy public information; running business, financial, competitive, risk, technology and alternative-data lenses on one target; mapping which key facts about a private company are still unknown; analyzing exit paths such as IPO, M&A or a secondary sale.

How do I install Private Company Research in Claude Code?

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

How do I install Private Company Research in Codex?

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

Can I use Private Company Research 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 private-company-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/private-company-research, .gemini/skills/private-company-research, .github/skills/private-company-research and .opencode/skills/private-company-research in your project.

What does Private Company Research need to run?

SKILL.md names no scripts, command-line tools or credentials: Private Company Research is instructions for the agent only. Our summary lists: Web search tools, and run_swarm for running the lenses in parallel.

Does Private Company Research 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 Private Company Research 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 Private Company Research use?

Private Company Research 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 Private Company Research use?

About 3.5k tokens (SKILL.md is roughly 14k 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 Private Company Research?

Skills that share tags, products or a category with Private Company Research: Private Company Research (xbtlin/ai-berkshire, 17k stars), Consulting Analysis (bytedance/deer-flow, 84k stars), China Market Open Data Search (OpenSenseNova/SenseNova-Skills, 5.7k stars) and Longbridge Earnings (helsome/folio, 271 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Private Company Research?

HKUDS (a GitHub organization) maintains it in HKUDS/Vibe-Trading, which has 35,163 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 10, 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.