Skill UI

An advanced debugging skill designed for AI agents. It utilizes gdb-cli to provide intelligent assistance for C/C++ programs, enabling the analysis of core dumps, debugging live processes, and investigating complex issues like crashes, deadlocks, and memory corruption. It correlates runtime state inspection with source code context for deeper insights.

Uses AWS Athena to define forensic tables and run queries against CloudTrail, VPC Flow, S3 access, and ALB logs, helping security teams detect unauthorized access, data exfiltration, lateral movement, and privilege escalation during incident investigations.

Hunt for threats in AWS environments by leveraging Detective behavior graphs, correlated GuardDuty findings, and automated entity timelines spanning IAM users, EC2 instances, and IP addresses to streamline cloud-native incident investigations.

Guides multi-round investigation of any GitHub repository, combining API queries, discovery searches, and deep dives to produce structured markdown reports with summaries, timelines, metrics tables, and Mermaid diagrams for executive-ready briefs.

Performs a systematic and structured investigation to determine the true source of bugs, errors, and unexpected system behavior, rather than merely treating symptoms. This method utilizes techniques like the Five Whys, hypothesis-driven testing, stack trace analysis, and gathering evidence (logs, git diffs) across multiple phases. It is ideal for complex, intermittent, or deeply rooted technical issues in software development.

A structured methodology for coordinating multiple, simultaneous investigation threads to determine the root cause of complex system failures, performance regressions, or integration bugs. This skill decomposes a large problem into independent tracks (e.g., database, application code, infrastructure), assigns ownership, and manages structured sync points (Continue/Pivot/Converge) to converge on a solution faster than serial debugging. Essential for high-stakes production incident response.

A comprehensive automation toolkit for managing Sentry error tracking and monitoring operations. Users can programmatically investigate issues, configure project-level alert rules, manage release versions, and monitor organizational structure. Essential for DevOps and development teams needing deep integration and programmatic control over error data.

A comprehensive, four-stage methodology for debugging complex software issues, ensuring that fixes target the root cause rather than merely masking symptoms. This guide mandates rigorous investigation (gathering evidence, tracing data flow), pattern analysis, controlled hypothesis testing, and disciplined implementation via test-driven development. Use this when facing production bugs, test failures, performance degradation, or unexplained system behavior, especially under time pressure.

This technique is used when multiple failure points (e.g., different test files or subsystems) are discovered, and these failures are independent of each other. Instead of debugging them sequentially, you dispatch separate, focused AI agents to work on each problem domain concurrently. This maximizes efficiency by allowing parallel investigation, dramatically reducing the time spent on large-scale debugging tasks.

This bundle collects comprehensive diagnostic information for troubleshooting ClickUp API v2 issues. It generates a redacted, safe bundle containing authentication status, rate limit details, workspace enumeration, and API latency measurements. Use this when preparing support tickets or investigating persistent connectivity problems.

This skill provides a comprehensive guide for deploying a containerized Finta integration service using Docker. It connects to the Finta API to manage critical fundraising data, including tracking fundraising rounds, investor pipelines, and deal flow analytics. The implementation covers environment configuration, health checks, and rolling update strategies, ensuring zero-downtime operation during active fundraising campaigns.

GateGuard acts as a powerful pre-tool-use hook, forcing the LLM to undergo deep, factual investigation before allowing any code edits, file writes, or destructive Bash commands. Unlike simple self-assessment, it requires citing specific facts (e.g., imports, data schemas, affected functions), significantly increasing the reliability and depth of AI-generated outputs. Ideal for complex coding tasks, data manipulation, and high-stakes development environments.