Skill UI

This skill utilizes the Taisly Agent Kit to manage the entire workflow for publishing approved short-form videos across major platforms, including TikTok, Instagram Reels, YouTube Shorts, X, and Facebook. It facilitates agent-assisted publishing by ensuring coordination of captions, metadata, and campaign details. A critical feature is the mandatory explicit confirmation gate, which prevents any state-changing publishing action without explicit user approval, prioritizing safety and compliance.

This advanced trading journal captures the full context of a trade, recording not only the fill price and P&L, but also the initial thesis, the defined plan (stop/target), and the emotional state at the time of entry. It integrates with Notion to log and review trades, grading the quality of decisions and execution against the user's own plan, ensuring users learn from process, not just profit.

This skill guides the secure management and operation of Virtual Private Servers (VPS) and deployed server-side agents. Use it when SSH access, system restarts, log inspection, or complex workflow automation (like n8n or custom agents) is required on authorized remote hosts. It emphasizes following strict operational protocols and obtaining explicit user permission for sensitive actions.

WGM is a robust, agentic methodology designed to transform vague or rough requirements into working, validated software. It enforces a disciplined workflow across six stages: deep alignment (Grill), structured planning, deterministic iterative building (Loop), mandatory holdout-scenario judging, and final documentation audits. It ensures that the resulting code converges against rigorously tested acceptance criteria rather than relying on single-pass generation.

This skill guides the design, implementation, and auditing of inclusive digital products based on WCAG 2.2 standards. It covers the POUR principles and provides technical guidance for generating semantic ARIA (Web) and native accessibility traits (iOS/Android). Use it when defining UI components, auditing compliance gaps, or ensuring robust keyboard navigation and focus management.

This tool manages the formal documentation of significant architectural decisions (ADRs). It automatically assigns sequential numbering, generates structured Markdown files (ADR-NNN), and registers the decision in the AgentDB graph. Use it when documenting technology choices, API designs, or data model changes to maintain a clear, traceable, and centralized history of system evolution.

This skill builds and maintains a persistent index and dependency graph for Architecture Decision Records (ADRs). It processes both v3-style and YAML plugin-style formats, storing ADR content in the `adr-patterns` namespace and relationships (like supersedes, amends, depends-on) in `adr-edges`. It is essential for synchronizing the graph when ADRs are updated, imported, or when bootstrapping ADR tracking on an existing codebase.

This tool reviews code changes (via diff) against accepted Architecture Decision Records (ADRs) to detect potential architectural drift, compliance violations, and adherence issues. It simulates a pre-merge check, ensuring that new code respects established architectural constraints and decisions made by the team. Ideal for CI/CD pipelines and mandatory code reviews.

This tool reads the persisted ADR graph to surface critical data integrity issues. It detects dangling references (links to non-existent ADRs), conflicting supersede cycles (A supersedes B and B supersedes A), and status mismatches. It is essential for CI/CD pipelines and post-indexing validation to ensure architectural decision records are consistent and reliable.

Diagnoses full-stack failure modes in agent and LLM applications, including wrapper regressions, memory contamination, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings and code-first fixes for production agent systems.

A lightweight CLI tool for systematically comparing various coding AI agents (such as Claude Code, Aider, and Codex) head-to-head. It measures crucial metrics—pass rate, API cost, execution time, and consistency—on reproducible tasks defined via YAML manifests. Use this to objectively select the best agent for your codebase or to validate model updates before production deployment.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.