Skill UI

Automatically generates professional, white-labeled client-facing reports based on comprehensive performance data. Use this tool for weekly snapshots, monthly deep-dives, or quarterly business reviews (QBRs). It synthesizes KPI performance, channel breakdowns, root cause analysis, and actionable strategic recommendations, formatted for easy delivery via Slack, email, or Google Sheets.

This advanced methodology builds an evidence-linked causal chain from a localized fault to an actionable systemic root. It moves beyond simple proximate causes by requiring counterfactual testing, verifying multiple independent pathways, and prescribing countermeasures that eliminate enabling conditions, ensuring true recurrence prevention.

A rigorous methodology for systematic problem diagnosis and consequential decision-making. It operates in two modes: Problem Analysis (for root cause identification using IS/IS-NOT contrast) and Decision Analysis (for prioritizing options by screening MUSTs, weighting WANTs, and assessing adverse consequences). Ideal for complex engineering and business scenarios requiring structured evidence and risk assessment.

A comprehensive methodology for diagnosing complex systems where problems are emergent across multiple components. It guides the user to map system boundaries, stocks and flows, feedback loops, and underlying structural drivers. By identifying the highest feasible leverage point, it recommends structural interventions that address root causes rather than superficial symptoms.

Builds phase-based legal fee budgets, monitors WIP against budget, explains variance with root cause analysis, forecasts to complete, manages alternative fee arrangements (fixed/capped fees), models scope change financial impact, and supports realisation and write-off analysis for legal matters.

Analyze OFAC or OFSI enforcement actions from URLs, pasted text, or uploaded documents and generate a structured root cause analysis Excel (.xlsx) file. The output table covers root causes, what went wrong, how it went wrong, preventive controls, and organizational immunity checks for compliance teams, counsel, and consultants.

Analyze AI-assisted coding sessions to identify root causes, scope drift, and rework patterns. Generates detailed reports explaining what happened, why it happened, and actionable recommendations for improving future prompts and workflows. Essential for postmortems on AI coding sessions.

This skill provides a methodical approach to system performance troubleshooting and root-cause analysis using Brendan Gregg's USE and TSA methodologies. It guides users through evidence-backed investigation steps, from initial triage to detailed thread state analysis, ensuring every claim is supported by metrics. Ideal for SREs and DevOps engineers debugging latency, CPU, memory, or network issues on Linux systems, culminating in structured RCA reports or postmortems.

This skill enables parallel read-only multi-agent root-cause investigation for bugs, regressions, crashes, or unexplained failures. It deploys four specialized sub-agents to analyze reproduction, code paths, recent changes, and proof plans simultaneously. The main agent synthesizes findings into ranked hypotheses without editing files. Ideal for diagnosing complex software issues efficiently.

Expert in root cause analysis for errors, test failures, and unexpected behavior. Captures error messages, identifies reproduction steps, isolates failure locations, implements minimal fixes, and verifies solutions. Provides root cause explanations, evidence, code fixes, testing approaches, and prevention recommendations.

This skill specializes in debugging distributed systems and analyzing production incidents. It helps identify root causes of errors using observability tools, structured logging, and distributed tracing. You will implement fixes, establish preventive measures, and improve system reliability. Suitable for investigating recurring errors and designing error handling improvements.

This skill searches logs and codebases for error patterns, stack traces, and anomalies. It correlates errors across systems to identify root causes. Use it for building evidence-backed timelines for failures, comparing request outcomes, and analyzing stack traces. It supports log aggregation queries and anomaly detection to provide actionable findings and prevention strategies.