Skill UI

This advanced workflow is designed to generate comprehensive, fact-checked research reports on any complex topic. It systematically performs fan-out web searches across multiple defined angles, fetches multiple sources, and employs a rigorous adversarial verification system (3-vote) to validate claims. The final output is a highly cited, synthesized report, ideal for academic or critical business research.

A comprehensive framework for creating and executing intelligent, goal-oriented action plans (GOAP). This system analyzes preconditions, optimizes costs, and provides adaptive replanning capabilities. It is designed for complex objectives that require multiple, dependent steps, ensuring robust task completion even when unexpected state changes occur.

This comprehensive guide covers idiomatic Go patterns, essential best practices, and advanced techniques for building robust, efficient, and maintainable Go applications. Topics include error wrapping, custom error types, context management for timeouts, concurrency patterns (Worker Pools, errgroup), and ensuring graceful shutdown.

A comprehensive guide to writing reliable and maintainable tests in Go. This skill covers the TDD methodology (Red-Green-Refactor cycle), advanced patterns like table-driven tests, subtests, benchmarks, and fuzzing. Learn how to ensure high code quality and test coverage in idiomatic Go projects.

This skill guides the operation, consolidation, and maintenance of complex working systems within Google Workspace (Docs, Sheets, Slides). It emphasizes treating files as integrated systems—such as trackers or planning hubs—requiring careful inspection, precise edits, and structural cleanup rather than simple content rewriting. Use it for consolidating scattered data, cleaning shared spreadsheets, or ensuring the correct canonical version of a document is utilized.

This tool executes the advanced `metaharness/darwin` security benchmark (ADR-155). It rigorously evaluates the security detection capabilities of AI systems against a controlled ground-truth corpus, providing critical metrics like TPR, FPR, and patch-pass rates. It compares the champion model against multiple baselines, offering empirical data crucial for iterative security improvement and drift detection.

Compute weighted similarity between two harness fingerprints using cosine, categorical agreement, and topology Jaccard. Use it to rank templates, detect drift, decide reuse vs scaffold, and generate confidence scores for recommender workflows.

This skill generates an enterprise-grade threat model report using the `harness threat-model` command. It categorizes threats across the MCP surface, providing a detailed report including the overall worst severity (clean, low, medium, high) and specific findings. Ideal for pre-launch security reviews and periodic audits when sharing findings with an InfoSec team.

This guide details the Hexagonal Architecture (Ports and Adapters) pattern, a robust method for decoupling core business logic from external infrastructure concerns. It teaches how to structure applications to make the domain model independent of databases, UI frameworks, or message queues. Implementing this pattern drastically improves testability, maintainability, and the ability to swap out external services without rewriting business rules.

A robust framework designed to track complex, long-running objectives that span multiple sessions, weeks, or even months. It manages goals through structured milestones, maintains persistent progress records, and includes critical drift detection to ensure projects stay on target and scope creep is managed effectively. Ideal for research programs, migration projects, or multi-stage development initiatives.

This tool orchestrates the entire firmware rollout lifecycle across large device fleets. It supports controlled, gradual deployment strategies, including canary testing and anomaly-gated advancement. Users can deploy new versions, check real-time status, execute rollbacks, or list all ongoing rollout campaigns, ensuring stable and reliable updates.

This skill guides the design and rigorous review of goal-oriented agent feedback loops. It helps ensure that complex autonomous multi-step agents are robust, preventing failures such as infinite looping, goal-gaming, or running to completion with incorrect assumptions. It covers defining machine-decidable goals, setting boundaries, and selecting the correct loop type (servo, regulator).