Skill UI

A guide for security researchers focused on finding exploitable, remote-reachable vulnerabilities for bug bounties or responsible disclosure. It emphasizes high-signal flaws such as SSRF, RCE, and SQL injection, while guiding users through the professional triage and PoC creation process for penetration testing.

This entry highlights a skill definition that lacks required metadata such as name and description, making it unusable until the missing information is provided.

Implements an ultra-compressed communication mode by drastically reducing token count while maintaining high technical accuracy. This mode strips filler words, articles, and pleasantries, allowing users to communicate complex technical concepts in the most concise way possible. Ideal for debugging, generating logs, or highly efficient development conversations. Supports multiple intensity levels, including classical Chinese modes.

This utility compresses verbose natural language notes, memories, and documents (like markdown files) into a highly dense format. By eliminating filler words, redundant phrases, and grammatical fluff, it significantly reduces the token count for large inputs. Crucially, it strictly preserves all technical content, including code blocks, inline code, URLs, file paths, and commands, ensuring technical accuracy while boosting AI processing efficiency.

This skill provides a comprehensive workflow to transform raw data into professional visual charts. It intelligently analyzes data features (e.g., trends, comparisons, flows) to select the optimal chart type (line, bar, pie, etc.). Users provide data and specifications, and the skill generates a high-quality, detailed chart image using a structured JavaScript process.

Guides AI agents through analyzing working code, spotting overly complex structures, then refactoring for clarity while preserving exact behavior and project conventions, especially after reviews or high-pressure edits.

This skill provides comprehensive guidelines for writing idiomatic, high-quality Dart and Flutter code. It covers naming conventions, type usage, structural best practices, and documentation standards. Use it when starting new features, reviewing pull requests for style compliance, or refactoring existing Dart codebases to improve consistency and maintainability.

This skill provides comprehensive linting and static analysis for Go projects using golangci-lint. It enforces numerous best practices, checks for common errors, manages configurations via `.golangci.yml`, and supports auto-fixing. Use it during the entire development lifecycle, including setting up CI/CD pipelines, running pre-commit checks, or refactoring large, legacy Go codebases to ensure high code quality and maintain security standards.

A comprehensive skill for engineering high-quality, observable Go services. It guides best practices across five key signals: structured logging (slog), Prometheus metrics, OpenTelemetry tracing, continuous profiling (pprof), and RUM. Use this when instrumenting new features, reviewing PRs, or auditing entire codebases to ensure systems are fully measurable for reliable diagnosis and alerting.

This skill provides a comprehensive guide to implementing high-performance, type-safe, in-memory caching in Go. It covers advanced features including nine eviction algorithms (e.g., W-TinyLFU, LRU), Time-To-Live (TTL), read-through loaders, and robust capacity sizing techniques. It is ideal for backend services requiring reliable, low-latency data access and efficient resource management.

A comprehensive, generics-first utility library inspired by Lodash, providing 500+ type-safe functions for Go collections. It enables highly declarative and functional programming patterns (Map, Filter, Reduce, GroupBy) over slices, maps, and strings, eliminating manual boilerplate loops. It supports immutable operations, concurrent processing (lop), and lazy iteration (loi) for advanced, efficient data transformations.

A library providing structured error handling for Go applications, moving beyond standard Go errors. It uses a fluent builder pattern to attach rich metadata—including domain context, user IDs, stack traces, custom attributes, and machine-readable error codes—directly to the error object. This ensures that crucial contextual data travels up the call stack, making errors highly diagnosable for on-call engineers and compatible with APM tools like Datadog or Sentry.