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{"openclaw":{"requires":{"bins":"[Truncated]","env":"[Truncated]"},"notes":"Instruction-only skill. Analyzes financial data from OPENCLAW_DATA_DIR to generate trend reports and advisory insights. No external services or credentials required."}}
Analytics and Advisory Intelligence
The previous 17 skills process, file, store, and protect. This skill thinks. It reads across all the data the system has built up and asks: what does this mean? What should the accountant know that they have not thought to ask?
An accountant managing 40 clients cannot spot a VAT liability trend building across one client's 12 months of data while simultaneously processing another client's payroll and preparing a third client's annual tax return. This skill does the cross-sectional, longitudinal reading that busy humans cannot. It surfaces the finding. The accountant decides what to do with it.
This skill is purely advisory. It reads, analyses, and reports. It never takes action, never modifies client records, and never submits anything. Every insight it surfaces is a prompt for human judgement, not a trigger for automated action.
No external credentials required. Analyzes financial data from local files to generate trend reports and advisory insights.
Core Philosophy
Proactive, Not Reactive: The system already handles reactive work. This skill looks ahead — identifying problems before they become crises and opportunities before they are missed
Cross-Client Vision: No single-client skill can see patterns that span the portfolio. This skill aggregates anonymised data across clients to detect sector trends, identify outliers, and benchmark clients against peers
Plain English Findings: Every output is written for accounting assistants. No statistical jargon, no unexplained numbers. Finding + evidence + suggested action
Confidence-Rated: Every finding carries a confidence level. A pattern with three data points is labelled differently from one with 18. The accountant knows how much weight to give each insight
Read-Only Always: This skill reads from all data sources but writes only to /data/reports/analytics/. It has no write access to /data/clients/, /data/compliance/, or any operational directory
Overnight Operation: Heavy analysis runs outside business hours. Lightweight queries run on demand but are bounded by pre-computed daily outputs
openclaw analytics morning-advisory --date today
openclaw analytics morning-advisory --date today --high-risk-only
openclaw analytics monthly-report --period 2026-01 --all-clients
openclaw analytics monthly-report --period 2026-01 --format pdf
openclaw analytics quarterly-review --quarter 2026-Q1 --all-clients
openclaw analytics ask --query "which clients have increasing VAT liability over the last 6 months"
openclaw analytics ask --query "which clients are at risk of not meeting their Q2 tax payment"
openclaw analytics ask --query "are there any expense categories that look unusual this month"
Analysis Modules
1. Compliance Risk Scoring
Every client receives a compliance risk score on a 1-10 scale, computed nightly. Feeds the dashboard portfolio view and morning advisory.
Compliance_Risk_Score:inputs:-Late filings in the last 12 months (weight:30%)-Compliance gaps currently open (weight:25%)-Missing documents pending more than 14 days (weight:20%)-AADE penalty history (weight:15%)-Days until next deadline vs documents received (weight:10%)score_bands:1-3:"Low risk — all obligations current, no gaps"4-6:"Medium risk — minor gaps or historical delays"7-8:"High risk — active gaps or recent penalties"9-10:"Critical — immediate attention required"output_location:"/data/reports/analytics/{YYYY-MM-DD}_risk-scores.json"refreshed:"Nightly at 01:00 Athens time"
2. Financial Trend Analysis
Reads Skill 15 financial statement outputs across multiple periods to detect directional movement.
Financial_Trends:metrics_tracked:gross_margin:calculation:"(Revenue - Cost of Sales) / Revenue"alert_threshold:"Drop of more than 5 percentage points vs same period last year"finding_template:"{Client} gross margin has fallen from {X}% to {Y}% — {delta}pp decline over {N} months. Primary driver appears to be {top expense category change}."vat_liability_trend:calculation:"Net VAT payable per period"alert_threshold:"Increase of more than 20% vs prior 3-month average"finding_template:"{Client} VAT liability has increased {X}% over {N} months. May reflect higher turnover, a change in customer mix, or a classification issue worth reviewing."staff_cost_ratio:calculation:"Staff costs (EGLS account 64) / Revenue"alert_threshold:"Ratio increase of more than 3 percentage points"cash_position:calculation:"Cash and bank balances (account 38) trend"alert_threshold:"Declining for 3 consecutive months"finding_template:"{Client} cash position has declined for {N} consecutive months (from EUR {X} to EUR {Y}). With a tax payment of EUR {Z} due on {date}, this warrants a cash flow conversation."minimum_periods_required:3preferred_periods:12confidence_note:"Findings with fewer than 6 data points are marked LOW CONFIDENCE"
3. Anomaly Detection
Values statistically unusual relative to a client's own history and sector peers.
Anomaly_Detection:methods:own_history_comparison:description:"Value more than 2 standard deviations from client's own trailing average"example:"Electricity costs EUR 4,200 in January vs trailing 12-month average of EUR 1,800"sector_peer_comparison:description:"Value more than 1.5x or less than 0.5x the sector median for that account"note:"Sector medians computed from anonymised aggregates across the portfolio"vat_rate_anomaly:description:"Transaction classified at incorrect VAT rate based on product/service category"example:"Food product invoice at 24% rather than correct 13% reduced rate"action:"Flag for accountant review — never auto-correct"output_fields:-metric:"What was measured"-actual_value:"What was found"-expected_range:"What is normal for this client / sector"-deviation:"How far outside normal"-confidence:"HIGH / MEDIUM / LOW"-suggested_action:"Plain English recommendation"-data_source:"Which files the finding is based on"
4. Cash Flow Forecasting
Projects cash position forward 1-3 months using historical patterns and known upcoming obligations.
Aggregated across the portfolio. All cross-client aggregation is anonymised.
Portfolio_Intelligence:sector_benchmarks:computed_from:"Anonymised aggregates of all active clients per sector"metrics:-Mediangrossmarginbysector-Medianstaffcostratiobysector-MedianVATliabilityaspercentageofrevenuebysectorrefreshed:"Monthly — after all monthly statements are generated"minimum_clients_per_sector:3portfolio_risk_distribution:description:"Distribution of risk scores across the full portfolio over time"workload_concentration:description:"Risk-weighted workload per accountant — alerts if one person carries disproportionate risk"common_issues:description:"Issues appearing across multiple clients simultaneously"examples:-"6 retail clients showing declining margins — possible sector-wide trend"-"3 clients using the same supplier showing unusual payment patterns"-"4 clients with outstanding document requests over 21 days"
Morning Advisory Output Format
Pre-computed overnight. Ready by 08:00. Pulled by conversational assistant (Skill 14).
MORNING ADVISORY — 19/02/2026
Generated: 19/02/2026 05:47 Athens time
WATCH LIST — 3 clients warrant attention today
1. GAMMA CONSTRUCTIONS AE (EL555444333) — RISK: HIGH (8/10)
Cash position has declined 3 consecutive months. Current balance EUR 12,400.
Tax payment of EUR 8,900 due 25/03/2026. Margin is tight.
Suggest: Review with client before month end.
2. DELTA SERVICES EPE (EL777888999) — RISK: MEDIUM (6/10)
VAT liability increased 34% vs prior 3-month average. January: EUR 4,100
vs average of EUR 3,060. Not yet investigated.
Suggest: Check whether turnover genuinely increased or a classification
issue exists in the January invoices. Confidence: MEDIUM (4 months data)
3. EPSILON RETAIL OE (EL222333444) — RISK: MEDIUM (5/10)
Bank statements for January not received. VAT filing due 25/02/2026 in 6 days.
Suggest: Send document request today.
PORTFOLIO SNAPSHOT
Active clients: 42 | Low risk: 31 | Medium: 8 | High: 3 | Critical: 0
Next high-volume deadline: VAT February 25/02/2026 (6 days, 18 clients affected)
SECTOR NOTE
Retail sector (7 clients): Gross margins down an average of 3.2pp vs Q4 2025.
Pattern appears sector-wide. May be worth raising with retail clients proactively.
Confidence: MEDIUM (7 clients, 3 months of data)
File System
Analytics_File_Structure:owns:"/data/reports/analytics/"daily_outputs:-"{YYYY-MM-DD}_risk-scores.json"-"{YYYY-MM-DD}_morning-advisory.json"-"{YYYY-MM-DD}_morning-advisory.txt"-"{YYYY-MM-DD}_anomalies.json"monthly_outputs:-"{YYYY-MM}_portfolio-report.pdf"-"{YYYY-MM}_sector-benchmarks.json"-"{YYYY-MM}_trend-analysis.json"on_demand_outputs:-"{YYYY-MM-DD}_{AFM}_risk-detail.json"-"{YYYY-MM-DD}_{AFM}_forecast.json"-"{YYYY-MM-DD}_{AFM}_trends.json"reads_from:-"/data/clients/*/financial-statements/"-"/data/clients/*/compliance/"-"/data/clients/*/correspondence/"-"/data/banking/reconciliation/"-"/data/efka/"-"/data/reports/analytics/"# Prior outputs for trend continuitynever_writes_to:-"/data/clients/"-"/data/compliance/"-"/data/banking/"-"/data/efka/"
Scheduling
Analytics_Schedule:nightly_run:time:"01:30 Athens time (after backup and integrity check)"operations:-"Compute risk scores for all active clients"-"Run anomaly detection across all clients with new data"-"Update trend analysis for clients with new financial statements"-"Generate morning advisory"duration_estimate:"10-30 minutes depending on portfolio size"monthly_run:trigger:"After all monthly statements generated (typically 5th-10th of following month)"operations:-"Recompute sector benchmarks"-"Generate portfolio monthly report"-"Run 12-month trend analysis for all clients with sufficient history"on_demand:available_during:"Business hours"rate_limit:"Maximum 10 on-demand requests per hour"bounded_by:"Pre-computed daily outputs where possible"
Memory_Integration:log_episodes:trueepisode_types:-morning_advisory_generated-anomaly_detected_and_surfaced-risk_score_computed-forecast_generatedlog_failures:truefailure_types:-insufficient_data_for_analysis-sector_benchmark_too_small-financial_statements_missinguseful_patterns_to_detect:-"Anomaly types consistently dismissed by accountants — thresholds may be too sensitive"-"Risk score bands that consistently predict actual compliance problems — calibration quality"-"Clients where forecasts are consistently wrong in one direction — systematic bias"rate_limit_group:"analytics_operations"
Error Handling
Error_Responses:insufficient_history:output:"{Client} has only {N} periods of data — trend analysis requires at least 3. Showing available data with LOW CONFIDENCE marker."action:"Produce what is possible, clearly labelled. Do not refuse to show anything."sector_too_small:output:"Sector benchmark for {sector} cannot be computed — only {N} clients (minimum 3 required). Showing own-history analysis only."missing_statements:output:"Financial trend analysis for {client} requires Skill 15 outputs for {period}. Statements not yet generated."action:"Show analysis based on available periods. Note the gap explicitly."on_demand_rate_limit:output:"On-demand analysis limit reached (10/hour). Pre-computed morning advisory is available now. Full analysis available after {time}."action:"Direct to pre-computed outputs. Never drop the request silently."
Success Metrics
A successful deployment of this skill should achieve:
Morning advisory ready every business day before 08:00 Athens time
Risk scores computed for 100% of active clients nightly
Anomaly detection surfaces at least one actionable finding per week across the portfolio
Cash flow forecasts accurate within 15% for the 1-month horizon (measured retrospectively)
Zero false positives that cause unnecessary client alarm — confidence ratings are accurate
All findings in plain English that an accounting assistant can act on immediately
Read-only confirmed — no writes to any operational directory, ever
Remember: This skill is the difference between an accounting system that processes the past and one that helps the firm prepare for the future. The value is not in the data it holds — the value is in what it notices that humans would have missed.
