Skill UI

This skill helps maintainers verify reported bugs in the mcp-toolbox project. It involves analyzing issue reports, checking versions and configurations, and attempting local reproduction to determine if the bug is real, fixed, or misconfigured. The process requires citing specific evidence like file paths and commit SHAs to support verdicts such as confirmed, already-fixed, or client-side issues. It ensures efficient bug triage without unnecessary infrastructure setup.

Mine ancient French legal sources from Gallica (BNF) via stable APIs: parliamentary debates since 1871, old case reports (Sirey, Dalloz), doctrine, theses, official texts, and legal journals. Retrieve OCR extracts, page numbers, and image links for verification. Ideal for historical legal research and genealogy of laws before modern databases.

This skill helps identify issues in Superpowers sessions by analyzing transcripts and evidence. It assists in troubleshooting problems like repeated work, ignored plans, or unexpected costs. The process involves locating session files, triaging dimensions, generating detailed reports, and optionally creating GitHub issues or export bundles for maintainers. All findings must cite specific paths and lines.

Searches 153 adapters across 122 jurisdictions for foreign extradition and surrender decisions, each in its own language. Localises the requesting state's name per jurisdiction, gates hits for relevance, and saves dated reports for comparative extradition research. Leads, not authorities.

Query self-hosted or Matomo Cloud analytics as a privacy-first GA4 alternative. Pull organic traffic, landing pages, device, country, referrer and keyword reports via the Matomo Reporting API.

Design AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review. Deliver justified decisions, verification methods, PDF reports, and visualizations.

Generate decision-ready reports from AI agent evaluation runs. This skill ensures autonomous, assisted, failed, and timed-out outcomes remain distinct and comparable. It defines metrics with correct denominators, handles latency and cost populations honestly, and maps evidence to decision gates. Ideal for benchmarking, regression testing, and production readiness validation of AI agents.

This AI-driven system integrates multi-dimensional health data including lifestyle, medical history, and mental health to identify anomalies and predict risks. It provides personalized health recommendations based on evidence-based guidelines and generates comprehensive interactive reports. Supports intelligent Q&A for health metrics and trends, ensuring data privacy with local processing. Ideal for users seeking data-driven health insights without replacing professional medical advice.

Enables agents to perform authorized security assessments, blue team exercises, and penetration testing on AI products, agents, and infrastructure. Uses first-principles reasoning to identify vulnerabilities like prompt injection and tool abuse, generating evidence-based security reports without relying solely on payload libraries.

Forensic analyzer for AI coding sessions. Identifies root causes of scope drift and rework by classifying user specs, agent errors, and repo friction. Generates evidence-backed reports to improve future prompts and project health.

Orchestrates authorized multi-stage penetration testing workflows spanning reconnaissance, initial access, privilege escalation, and lateral movement. Coordinates specialist skills to plan coherent attack paths, manage risk, and generate comprehensive security assessment reports for red team engagements and authorized security assessments.

This skill enables independent auditing of AI agent, gateway, or automation workflow runs. It reconstructs events from traces, checkpoints, and tool calls to verify success claims without trusting self-reports. Users establish contracts, build claim ledgers, normalize evidence, rank witnesses, and assign verdicts like proven or contradicted. It ensures integrity in multi-step automation and separates autonomous success from human-assisted outcomes.