Detect cost anomalies in a cloud billing export (AWS/GCP/Azure/generic).

Models the expected daily spend for each day as a rolling-median baseline times a day-of-week factor, then flags days whose actual spend deviates from that baseline by a robust (median-absolute-deviation-based) modified z-score. This is resistant to the "masking effect" that causes textbook mean+standard-deviation detectors to miss anomalies once a few large spikes have inflated the standard deviation. It does NOT call any cloud provider API — the caller must already have exported the billing data to a CSV/text string and pass its contents directly.

Args: params (AnalyzeBillingWasteInput): Validated input containing: - csv_content (str): Raw billing export text (see field description for supported provider formats). - analysis_type (AnalysisType): 'waste-audit' (default), 'cost-forecast', or 'efficiency'. - response_format (ResponseFormat): 'markdown' (default) or 'json'.

Returns: str: Markdown report, or a JSON object with this schema: { "anomalies": [ {"date": str, "expectedCost": float, "actualCost": float, "deviation": float, "zScore": float, "severity": "LOW"|"MEDIUM"|"HIGH"|"CRITICAL", "description": str} ], "metrics": {"averageDailyCost": float, "stdDeviation": float, "sharpeRatio": float, "wastePercentage": float}, "recommendations": [ {"title": str, "description": str, "potentialSavings": float, "effort": "LOW"|"MEDIUM"|"HIGH"} ], "summary": str }

Error response: "Error: <message>" when the CSV cannot be parsed
(e.g. no recognizable date/cost columns).

Examples: - Use when: "Why did our AWS bill spike last month?" -> paste the CUR export contents as csv_content. - Use when: "What will we spend next month at this rate?" -> analysis_type="cost-forecast". - Don't use when: you need architecture/reliability risk instead of cost — use cloudsealed_score_architecture_risk.

Score architecture risk from a declared system inventory.

Scores single-point-of-failure, excessive-coupling, and scalability-gap risk (0-100 each) for every declared system using explicit, weighted rules — not a trained model. Every score ships with a rule-by-rule breakdown so the reasoning is auditable, not a black box. Calls the Predictive-ML-Core production HTTP service (or a self-hosted instance if PREDICTIVE_ML_CORE_URL is set).

Args: params (ScoreArchitectureRiskInput): Validated input containing: - company_name (str): Name of the company/project. - systems (list[SystemInput]): Declared inventory — each with name, type (APPLICATION|DATABASE|API|THIRD_PARTY_SERVICE), criticality (LOW|MEDIUM|HIGH|CRITICAL), public_facing, and optional data_sensitivity/auth_method. - historical_metrics (Optional[HistoricalMetricsInput]): Observed latency/throughput, improves the scalability-gap score. - response_format (ResponseFormat): 'markdown' (default) or 'json'.

Returns: str: Markdown report, or a JSON object with this schema: { "predictions": [ {"systemName": str, "riskScores": {"singlePointOfFailure": int, "excessiveCoupling": int, "scalabilityGap": int}, "scoreBreakdown": {...rule-by-rule points and rationale...}, "findings": [{"title": str, "severity": str, "description": str, "remediation": str}], "recommendations": [{"title": str, "description": str, "effort": str}]} ], "architectureSummary": str, "overallArchitectureScore": int }

Error response: "Error: <message>" if the service is unreachable or
rejects the request (e.g. empty systems list).

Examples: - Use when: "Is our checkout service a single point of failure?" -> declare it with criticality=CRITICAL, type=API. - Use when: "Which of these services should we harden first?" -> declare the whole inventory and compare riskScores. - Don't use when: you need cost/billing analysis — use cloudsealed_analyze_billing_waste.

Error Handling: - Returns "Error: Request timed out..." if the service doesn't respond within 30s. - Returns "Error: ..." with the upstream message on 4xx/5xx responses.