Skill UI

Transforms repo structure and commit history into two actionable markdowns—an architecture summary and a narrative story—so analysts can trace system intent, data flow, and team evolution.

Evolver is a comprehensive self-evolution engine designed for autonomous AI agents. It analyzes complex runtime history, identifies performance bottlenecks, and autonomously generates and applies improvements. Communication is architecturally confined to a local Proxy Mailbox, ensuring secure and reliable interaction with the EvoMap Hub for asset management and task acquisition. Ideal for developing highly adaptive, continuously improving intelligent systems.

Analyzes, manipulates, compares, and visualizes phylogenetic or hierarchical trees. Supports Newick/Nexus I/O, topology edits, Robinson-Foulds comparisons, gene-tree evolutionary events, NCBI/GTDB taxonomy queries, and publication rendering. Works with existing trees after alignment and inference, not for inferring from raw sequences.

This skill enables the construction and analysis of phylogenetic trees using tools like MAFFT for alignment, IQ-TREE 2 for maximum likelihood inference, and FastTree for large datasets. It supports evolutionary analysis, microbial genomics, viral phylodynamics, and molecular clock studies. Visualization is handled via ETE3 or FigTree. Ideal for inferring evolutionary relationships, detecting horizontal gene transfer, and reconstructing ancestral sequences in biological research.

This specialized skill provides a robust framework for tracking and updating financial investment signals. It systematically assesses the impact of new market information—such as breaking news or price movements—on existing investment theses. The system determines if a signal is Strengthened, Weakened, Falsified, or Unchanged, providing a crucial layer of continuous signal validation for sophisticated financial decision-making.

Provides comprehensive methods for executing ClickHouse schema migrations, including column changes, engine migrations, and complex modifications like changing ORDER BY. It teaches best practices for zero-downtime schema updates and implements a versioned migration runner pattern for robust data evolution.

A universal infrastructure for automatically evolving and optimizing AI agents across any domain. Using LLM-driven mutation and iterative solve-observe-evolve cycles, it enhances critical components like prompts, skills, and memory by benchmarking performance against measurable datasets, enabling the creation of self-improving AI systems.

This comprehensive guide assists developers in maintaining and upgrading integrations with the Finta fundraising CRM API. It covers critical areas such as API version detection, handling schema evolution (e.g., structured stage objects), best practices for data migration, and troubleshooting common breaking changes (like field renames, permission changes, or enum changes) to ensure continuous and reliable data synchronization.

A comprehensive guide detailing best practices, API version detection, and schema migration strategies for upgrading the Glean enterprise search platform. It covers critical steps like updating connectors, handling schema evolution (e.g., flat permissions to structured ACL), and implementing robust rollback strategies to ensure uninterrupted search functionality during major platform updates.

This CLI alias serves as the primary interface for the Codex /pua:pro protocol, designed for advanced code operations within the Claude Code environment. It manages complex workflows related to self-evolution, platform telemetry monitoring, key performance indicators (KPIs), and system configuration updates, ensuring robust and consistent protocol adherence.

Strategic advisory for startup CAIOs and founders on model build-vs-buy, EU AI Act and NIST risk classification, API-to-self-hosted cost breakeven, and AI team org evolution.

Provides high-level strategic guidance for founders and Chief Data Officers (CDOs) in startups. This skill covers critical decisions such as AI training data rights and consent provenance, selecting optimal data architectures (warehouse, lakehouse, data mesh), valuing customer data as a strategic asset for M&A or fundraising, and planning data team organizational evolution. It focuses exclusively on strategic decision-making, not tactical engineering.