Skill UI

This skill automatically enriches research wiki pages that are left as bare scaffolds after initial ingestion. It reads multiple external sources (like arXiv/alphaxiv) and fills in 10 structured `_TODO._` sections (e.g., Problem/Gap, Key Results, Open Questions) with detailed prose summaries. Use this skill to transform incomplete research scaffolds into a comprehensive, structured knowledge base.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

A comprehensive guide for securing container images and runtime environments. Covers best practices from Dockerfile authoring (minimal base images, multi-stage builds, non-root users) through advanced runtime enforcement (seccomp, AppArmor) and Kubernetes security controls. Essential for production hardening and maintaining supply chain integrity.

This tool indexes local files or directories into a persistent FTS5 knowledge base. This allows subsequent search operations to efficiently retrieve highly relevant, focused snippets from the project source without needing to re-read the raw files every time. It is crucial for managing and searching large, complex codebases or detailed project documentation.

This tool accesses a persistent FTS5 knowledge base containing previously indexed project documentation, local content, and session memory. Instead of feeding entire raw sources into the conversation context, it allows users to perform targeted, efficient searches using specific queries and source scoping. This is ideal for quickly retrieving technical details, project specifications, or historical context without lengthy re-reading.

This orchestrator dynamically manages and loads multiple specialized Go skills based on the current task context. Whether you are designing an API, debugging a panic, auditing security, or optimizing performance, it automatically combines the most relevant skills (e.g., gRPC loads testing, error handling, and core Go skills). It acts as a comprehensive guide to Go best practices and modern development patterns.

This skill enforces an algorithm-first discipline, requiring the user to state the time and space complexity (Big-O), the intended data structure, and the core algorithm family before writing any complex code (loops, queries, recursion). It serves as a critical gatekeeper to prevent common performance pitfalls like O(n²) complexity, N+1 database queries, and brute-force implementation errors, significantly improving code robustness and efficiency.

A comprehensive tool for performing pathway and gene-set enrichment analysis (GSEA, ORA, ssGSEA, GSVA). Used in computational biology to determine which biological pathways or gene sets are significantly over-represented in a given list of genes or ranked gene data. Supports multiple databases (GO, KEGG, Reactome, MSigDB) and provides tools for result interpretation, visualization, and publication-ready reporting.

This skill provides a rigorous, operational framework for product and user research. It helps researchers select the appropriate study method (generative, evaluative, validation) based on goals and product stages. It also calculates defensible sample sizes and saturation levels, and synthesizes raw observations into governed, evidence-based insights, ensuring all findings are rooted in cross-participant recurrence.

This skill provides comprehensive financial oversight for internal R&D programs and portfolios. It facilitates multi-period budget planning, applying indirect F&A rates, and tracks critical metrics like burn rate and runway against value-inflection milestones. It also supports the capitalization vs. expense determination process based on established accounting standards (IAS 38/ASC 730). Crucially, the tool only provides decision support and never books entries or determines final accounting treatment.

This skill captures and persists critical user decisions, project constraints, and architectural choices across multiple sessions and agents. It saves this context into a structured memory database (`mdbase`), ensuring that future development steps or agents can retrieve the original intent without re-deriving it from scratch. Ideal for documenting project rules, tech stacks, and design rationale.

This guide provides a systematic, safe workflow for refactoring rapidly developed or AI-generated fullstack applications. It focuses on identifying and removing dead code, fixing broken imports, and consolidating repetitive logic while strictly maintaining existing API contracts, routes, and core functionality. Use this before launch to ensure a stable, maintainable production baseline, following a 'surgery, not demolition' philosophy.