Skill UI

This skill allows users to mine their extensive coding history across various AI agents (Claude, Codex, Gemini, etc.) into a persistent, structured corpus. It surfaces reproducible workflows as evidence and interactively helps the user forge these past practices into reusable, installed skills across their agent ecosystem. It is designed to synthesize judgment from raw transcripts into actionable, portable skills.

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

Calculates statistical power and required sample sizes for study planning. It supports both closed-form methods (for standard tests like t-tests, ANOVA) and simulation-based methods (for complex models like mixed models or survival analysis). Use this skill to determine the minimum detectable effect (MDE) or justify sample sizes for grants and IRB protocols before data collection.

This skill implements Autonomous Optimization (AO) using Hypothesis Tree Refinement (HTR). It iteratively improves concrete artifacts (like code, models, or data pipelines) by running structured, long-term experiments against a measurable objective. It maintains a persistent hypothesis tree, coordinating subagents to test hypotheses in isolation while using a held-out test evaluator to prevent overfitting and ensure reliable, cumulative research progress. Ideal for complex benchmark tuning, agent refinement, or optimizing entire workflows.

This skill acts as a goal-integrity governance layer for complex, high-risk, and multi-phase tasks. It ensures the agent remains strictly aligned with the user's original objective by generating formal Goal Contracts, Phase Checks, Deviation Notices, and Final Audits. Essential for robust backend development, API calls, and validation workflows, preventing silent goal drift.

Dep handles the entire journey from local code to production deployment. This skill automates containerization using Docker and docker-compose, establishes robust CI/CD pipelines (linting, testing, security scanning, deployment), manages environment variables and secrets, and generates Infrastructure as Code (Terraform/Pulumi) configurations. It ensures observability and provides rollback procedures.

This skill maps engineering changes (code diffs, architecture designs, IaC) to specific financial compliance controls, such as PCI-DSS v4.0 and MAS TRM. It generates an auditable report detailing compliance gaps, required evidence, and concrete remediation steps, helping teams catch violations before formal audits.

This skill transforms vague or high-level project ideas into eight comprehensive, interconnected planning documents (PRD, TechSpec, Design, Schema, etc.). It establishes a definitive, persistent source of truth for the project's scope, architecture, and design guidelines before a single line of code is written. Ideal for initiating brand-new software projects to prevent scope creep and planning gaps.

This skill acts as a literature-research assistant, orchestrating a Python CLI to interface with APIs like Semantic Scholar and arXiv. It enables users to search for academic papers by topic, retrieve detailed metadata, perform impact analysis using citation tracking, download PDFs, and extract text content. It streamlines the entire academic research workflow from initial discovery to deep reading.

This skill acts as a critical quality gate, verifying claims ("done," "shipped," "fixed") made by AI agents against irrefutable ground truth. Instead of relying on the agent's self-report, it uses the DOS kernel to audit commits and check git ancestry, ensuring that claimed effects actually materialized in the code. Ideal for integrating AI workflows with robust CI/CD practices.

This skill designs and audits the core engineering harness required for reliable AI agent collaboration within a codebase. It establishes critical infrastructure elements such as change tracking, linting rules, CI/CD gates, and standardized documentation, ensuring the repository remains the single source of truth for all intelligent agents. Ideal for setting up complex development workflows.

This skill simulates a world-class Senior System Design Engineer with 20+ years of experience at top tech companies. It helps users design, review, and scale complex, highly available, and resilient distributed systems. It covers every aspect from database sharding and microservices decomposition to caching strategies, observability stacks, and CI/CD pipelines, providing detailed blueprints and mathematical scaling estimates.