What problem does it solve? Researching private companies is hard because there are no standardized financial reports, information is scarce and scattered, and AI-generated analysis tends to fill gaps with false precision. This Skill coordinates a multi-agent research team that pieces together multi-source data, cross-validates conflicting figures, and produces an honest intrinsic-value assessment for companies like Ant Group, Xiaohongshu, SpaceX, or Stripe. ## Core Features & Use Cases - Six-Agent Parallel Research: Launches business-decoder, financial-detective, competitive-mapper, risk-governance-analyst, tech-ip-analyst, and signal-miner agents simultaneously, each with a detailed task template from the references file. - Cross-Validation & Signal Consistency: Arbitrates conflicting data across agents, checks whether growth narratives match hiring signals, and maps information into known/uncertain/unknown zones. - Confidence-Labeled Valuation: Every data point carries a source and confidence rating (high/medium/low), and valuation is triangulated via recent funding rounds, comparable companies, DCF scenarios, terminal-value back-casting, and transaction benchmarks. - Use Case: Ask it to research a pre-IPO unicorn; it returns a full report with a six-dimension scorecard, moat assessment, bull/bear cases, risk matrix, exit-path analysis, and a one-page invest/watch/avoid decision table saved to a reports directory. ## Quick Start Ask the agent to run a private company deep research on a specific company name, for example requesting a full valuation analysis of SpaceX.
