How the Private-Company-Research Skill Handles Data Scarcity: A Technical Deep Dive

The private-company-research skill mitigates data scarcity through a three-tier defense system: hierarchical source ranking with confidence tags (🟢🟡🔴), mandatory cross-verification of all financial metrics, and a first-principles fallback mode that marks unknowns as "data missing" rather than fabricating estimates.

The private-company-research skill in the xbtlin/ai-berkshire repository is engineered specifically for the opacity of unlisted companies. It employs a "team-lead" agent architecture that treats data scarcity as a first-class constraint rather than an edge case, ensuring every analysis acknowledges uncertainty explicitly.

Architecture: Three Mechanisms for Scarce Data

The skill's workflow, defined in skills/private-company-research.md, implements a resilient pipeline that recognizes scarcity, quantifies uncertainty, and prevents the illusion of precision.

Multi-Source Hierarchy and Confidence Labelling

At the foundation lies a prioritized matrix of data sources (lines 85‑97). The skill defines a strict hierarchy ranging from high-fidelity documents (prospectuses, parent-company filings, regulatory disclosures) down to lower-trust signals (industry estimates, former-employee tips). Every source carries an explicit confidence tag:

• 🟢 High: Audited filings and direct disclosures
• 🟡 Medium: Third-party reports and financing news
• 🔴 Low: Deep-media articles and unverified estimates

These tags propagate through the entire workflow, ensuring that downstream agents weight data according to its proven reliability.

Cross-Verification with Transparent Attribution

Every key metric—revenue, cost, cash flow—is cross-validated against at least two independent sources whenever possible (lines 31‑34). The skill mandates that each data point records its origin, timestamp, and confidence level. When sources conflict, the workflow lists figures side-by-side with a concise rationale for the chosen value, preventing silent overriding of weak signals by strong ones.

First-Principles Mode for Extreme Scarcity

When public information is so thin that the hierarchy yields few facts, the workflow switches to a "first-principles" mode (lines 29‑33). Instead of forcing a full-blown report, the skill focuses on four core questions: business problem, team advantage, upside ceiling, and validation milestones. This mode enforces strict "leave-blank" discipline—any field unsupported by evidence is marked as data missing and excluded from valuation.

The skill's design philosophy is codified as a bias-mitigation principle (lines 26‑28): "宁可留白说‘不知道’，也不要用推测填满表格伪装确定性" ("Better to leave blank and say 'don't know' than to fill the form with speculation pretending to be certainty").

Implementation in Code

The scarcity-handling logic is exposed through a Python API that wraps the six specialized agents (team-lead, financial-detective, market-analyst, etc.). When data is unavailable, the system returns structured uncertainty markers rather than hallucinated values.

# Example: invoking the private-company-research skill for a startup with limited data

from ai_berkshire.skills import private_company_research

# Step 1 – launch the team

team = private_company_research.create_team(company_name="AcmeAI", lead="team-lead")

# Step 2 – ask the financial‑detective for FY2024 revenue

revenue_info = team.query(
    role="financial-detective",
    subject="拼凑AcmeAI财务数据并进行估值分析",
    question="AcmeAI 2024 年度收入是多少？"
)

print(revenue_info)

When data is scarce, the financial-detective agent returns a structured response with explicit confidence tagging:

{
  "metric": "Revenue FY2024",
  "value": "🔴 低 – 数据缺失",
  "sources": [],
  "notes": "未找到公开的收入披露；仅有媒体报道提及融资规模，无法推算收入。"
}

This pattern is applied across all six agents, ensuring every piece of information carries an explicit confidence indicator.

Configuration and Source Files

The scarcity-handling rules are encoded across the following repository files:

• skills/private-company-research.md — Master definition of the workflow, including the Data Sources Matrix and leave-blank discipline rules.
• codex-prompts/private-company-research.md — Generated slash-prompt that exposes the skill to Codex-style commands, kept in sync with the scarcity logic.
• AGENTS.md — Repository-wide description of the "team-lead" orchestration model used to coordinate agents during uncertain analysis.
• scripts/sync-codex-skills.py — Utility that regenerates the Codex version after any change to the Markdown source, ensuring scarcity-handling logic stays current.

Summary

• The private-company-research skill treats data scarcity as a core constraint, not an exception.
• Confidence tags (🟢🟡🔴) are mandatory for every data point, sourced from a hierarchical matrix defined in skills/private-company-research.md.
• Cross-verification requires two independent sources for key metrics, with conflicting values preserved and explained.
• First-principles mode triggers automatically for extreme scarcity, enforcing "leave-blank" discipline rather than fabrication.
• Implementation uses a team of specialized agents (team-lead, financial-detective) that return structured "data missing" flags when evidence is absent.

Frequently Asked Questions

What does the "leave-blank" discipline mean in the private-company-research skill?

The "leave-blank" discipline is a strict rule that prohibits agents from filling valuation templates with speculative numbers. When a metric like revenue or cash flow cannot be verified through the skill's multi-source hierarchy, the agent must explicitly mark the field as data missing (数据缺失) and exclude it from final calculations. This prevents the "illusion of precision" that plagues private-company analysis.

How does the skill determine which confidence tag to assign?

Confidence tags are determined by the Data Sources Matrix defined in skills/private-company-research.md (lines 85‑97). Audited prospectuses and regulatory filings receive 🟢 high confidence; third-party reports and financing news receive 🟡 medium confidence; industry estimates and media articles receive 🔴 low confidence. The financial-detective agent applies these tags automatically during the cross-verification phase.

Can the private-company-research skill generate reports when no financial data exists?

Yes, through first-principles mode. When the scarcity threshold is reached (no reliable sources available), the skill pivots to qualitative analysis focused on four core questions: business problem, team advantage, upside ceiling, and validation milestones. This mode produces actionable investment theses without fabricating quantitative metrics, ensuring intellectual honesty when analyzing early-stage or stealth startups.

Which agents are involved in handling data scarcity?

The skill utilizes six specialized agents coordinated by the team-lead agent. The financial-detective agent handles quantitative cross-verification and applies confidence tags, while the market-analyst and industry-expert agents provide qualitative context when numerical data is absent. All six agents adhere to the same confidence-labelling and leave-blank protocols defined in the master skill file.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}