Vibe-Trading is a personal trading agent that gives an AI system tools for market analysis, algorithmic trading, backtesting, and related workflows. It is for users who want an agent to research and evaluate trading strategies or manage simulated and other trading activities. The catalogue contains skills that expose these trading capabilities to compatible agents.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add HKUDS/Vibe-Trading --skill private-company-researchgit clone --depth 1 https://github.com/HKUDS/Vibe-TradingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hkuds/vibe-trading/private-company-research)<a href="https://agentmods.dev/skills/hkuds/vibe-trading/private-company-research"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/private-company-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hkuds/vibe-trading/private-company-research"><img src="https://agentmods.dev/badge/skills/hkuds/vibe-trading/private-company-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00153 | $0.03287 |
| Opus 5 | $0.00077 | $0.01643 |
| Sonnet 5 | $0.00031 | $0.00657 |
| Haiku 4.5 | $0.00015 | $0.00329 |
Grade A, and why
private-company-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- private-company-research — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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):
| Role | Lens |
|---|---|
| business-decoder | Business model + product/user analysis: "what is this business, essentially" |
| financial-detective | Financial patchwork + valuation: "recover the true financial picture under missing data" |
| competitive-mapper | Industry + competition + substitution: "who competes, who could disrupt" |
| risk-governance-analyst | Risk全景 + management/governance/investors: "what could go wrong, who's at the helm" |
| tech-ip-analyst | Tech stack / patents / R&D / moat: "is the tech barrier real and durable" |
| signal-miner | Alternative data (hiring / patents / litigation / app / supply chain): "clues beyond the usual sources" |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 161 lines · 153 tokens per session scan A 1581466137bc
private-company-research is a skill published in the GitHub repository HKUDS/Vibe-Trading (33,258 stars, last pushed yesterday), licensed MIT. It adds 153 tokens to every session and 3,287 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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