Update to OloLand DD

{
  "description": "Use when analyzing risks for M&A deals, evaluating risk severity, quantifying financial impact of risks, or building risk matrices. Provides the institutional risk taxonomy (311 risk factors across 67 categories) with forensic QoE methodology.",
  "included_files": [
    {
      "relative_path": "agents/openai.yaml",
      "size_in_bytes": 438
    }
  ],
  "name": "risk-analysis",
  "skill_md_contents": "---\nname: risk-analysis\ndescription: Use when analyzing risks for M&A deals, evaluating risk severity, quantifying\n  financial impact of risks, or building risk matrices. Provides the institutional\n  risk taxonomy (311 risk factors across 67 categories) with forensic QoE methodology.\n---\n\n# Risk Analysis Framework\n\n## Risk Taxonomy: 311 Risk Factors Across 67 Categories\n\n### Commercial Risks\nMarket position, competitive dynamics, customer concentration (top 10 revenue share), revenue sustainability (recurring vs one-time), pricing power, channel dependency, regulatory exposure, supply chain, geographic concentration.\n\n**Industry overlays:**\n- **SaaS**: NDR decay, CAC payback, cohort economics, deferred revenue cliff, professional services dependency\n- **Manufacturing**: LIFO liquidation, deferred maintenance, bill-and-hold, obsolete inventory\n- **Healthcare**: Reimbursement dependency, regulatory risk, M&A integration complexity\n\n### Financial Risks\nLiquidity (current ratio, quick ratio), debt capacity (leverage, coverage), profitability (margin trends, quality), revenue quality (recognition policy, deferrals), working capital (DSO/DIO/DPO trends), off-balance-sheet items.\n\n### Legal Risks\nContract enforceability, change-of-control provisions, IP ownership, pending/threatened litigation, compliance (FCPA, GDPR, SOX), environmental liability, employee agreements.\n\n### HR Risks\nKey person dependency, management depth, retention risk (turnover trends), compensation benchmarking, cultural integration, union exposure, pending employment claims.\n\n### Technology Risks\nArchitecture (monolith vs microservices), technical debt, security posture, scalability constraints, platform dependency, open-source licensing, data privacy compliance.\n\n## Forensic Quality of Earnings\n\nWhen evaluating financial risks, apply these deterministic tests:\n\n1. **Beneish M-Score** (8-variable model): DSRI, GMI, AQI, SGI, DEPI, SGAI, TATA, LVGI. Score below -1.78 = likely manipulation.\n2. **Benford's Law**: First-digit conformity review on suitable transaction data. Preserve signed amounts: the standalone tool uses absolute magnitude for negatives and ignores zero/unusable values. Report only returned sample size, status, distributions, chi-square, MAD, and conformity; the current standalone response supplies no p-value or account/transaction rankings. An insufficient sample is a gap. Deviations prompt investigation, not proof of fraud; conformity does not rule fraud out.\n3. **EBITDA Bridge**: Verify every add-back. Non-recurring items must be truly non-recurring.\n4. **Revenue Quality Deep Dive**: Recognition timing, bill-and-hold, channel stuffing indicators.\n\n## Risk Severity Scale\n\n| Score | Label | Criteria |\n|-------|-------|----------|\n| 5 | Critical | Deal-killer. >20% probability of >25% EV impact. Requires structural mitigation or deal restructuring. |\n| 4 | High | Material. >30% probability of 10-25% EV impact. Requires price adjustment or contractual protection. |\n| 3 | Medium | Significant. Manageable with standard protections (reps & warranties, indemnification). |\n| 2 | Low | Minor. Identifiable but unlikely to materially affect valuation or deal structure. |\n| 1 | Minimal | Negligible. Standard industry risk, no specific mitigation needed. |\n\n## Cross-Deal Risk Calibration\n\nBefore assessing risks, check historical patterns:\n- Use `find_similar_deals` to see which risk categories materialized in similar past deals\n- Check if the firm's predictions were systematically biased (e.g., underestimating customer concentration risk)\n- Adjust severity accordingly — institutional calibration beats generic assessment\n\n\n## OpenAI execution requirements\n\nUse the connected MCP server's current tool schemas. Do not invent tool arguments, job-status endpoints, inputs, or outputs. If a referenced tool is unavailable, explain the limitation and request the needed connection or data. Invoke writes only when the user has requested the corresponding action. Do not initiate purchases or payments. An unrun, unsupported, or unavailable procedure is a gap, never a passing result.\n\n\nOpenAI cannot execute local lifecycle hooks. Use the packaged explicit setup/review workflow when requested. Server-side MCP auditing remains authoritative; preserve evidence blockers and human-only approval gates.\n\nClaim-correction submission is unsupported in this OpenAI upload because the current handler records surface=cowork. Do not invoke submit_agent_claim_correction from OpenAI. Explain the limitation and retain proposed correction text for human review without claiming submission. See unsupported-workflows.json for the excluded canonical workflow.\n"
}