Skill UI

This skill applies a rigorous, evidence-first discipline to coding tasks, inspired by Fable-like best practices. It guides agents through a structured workflow: inspecting the workspace, tracking goals and findings, grounding all conclusions in verifiable evidence, and executing thorough verification gates before declaring completion. Ideal for complex development, debugging, or adapting cross-model prompt guidance into robust, verifiable project rules.

Provision headless backend services for AI agents through Cohesivity: managed hosting, databases, storage, LLM access, auth, and third-party APIs via one HTTP API. Use for bootstrapping or extending app backends with explicit user consent.

This skill compiles durable, non-obvious findings into an interlinked markdown knowledge store using atomic files and wiki links. It ensures agents retain knowledge across sessions without relearning facts. It involves creating one fact per file with metadata like confidence and provenance, maintaining an index for easy retrieval. Ideal for research, competitive intel, or investigation results.

This skill orchestrates a comprehensive multi-dimensional code review using specialized agents. It covers code quality, architecture, security, performance, testing, and documentation. Users can configure focus areas like security, performance, or TDD compliance. It integrates static analysis, security scanning, and automated metrics to provide actionable feedback and remediation guidance for software development projects.

Build AI agents that mimic human computer interaction, including viewing screens, moving cursors, clicking buttons, and typing text. This skill covers frameworks like Anthropic's Computer Use and OpenAI's Operator, focusing on vision-based control, sandboxing, and security. Ideal for desktop automation, GUI automation, and browser agents.

Configure a Rails project to run inside Conductor workspaces for parallel coding agents: creates conductor.json, bin/conductor-setup, and script/server scaffolding, links shared dev secrets, and updates Redis config to use REDIS_URL with isolated ports and namespaces.

This skill covers strategies for compressing conversation history in AI agent sessions to manage context window limits. It focuses on optimizing tokens-per-task rather than just tokens-per-request, using methods like anchored iterative summarization and structured summaries. It addresses artifact trail integrity and evaluation metrics for coding agents to prevent information loss during long sessions.

This skill provides production-tested patterns for building AI agents that autonomously discover, evaluate, and acquire token listings for cryptocurrency exchanges. It covers systematic crypto business development, including multi-chain scanning, token scoring systems, wallet forensics for safety verification, and managing outreach pipelines with human oversight. Built from real-world experience, it teaches LLM cascade patterns and on-chain identity registration.

A framework for building production-ready, low-latency, multimodal AI agents. It allows developers to orchestrate complex pipelines that seamlessly handle real-time audio, video, and text data. Key capabilities include integrating multiple AI services (LLMs, STT, TTS), managing conversation context, and enabling tool use and function calling for advanced, interactive AI applications.

Build features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation. This skill provides a structured workflow from exploratory data analysis and hypothesis formation to feature implementation with integrated analytics, A/B testing infrastructure, and post-launch analysis.

A protocol that provides LLM agents with persistent structural memory of a codebase. It maps the project as a directed graph, tracking not only dependencies (what imports what) but also the 'reasons' (why it connects). This allows agents to understand context, perform impact analysis, and navigate large repositories without loading the entire source code into the context window.

This skill performs a two-model debate review on code pull requests from GitHub, GitLab, or Azure DevOps. It uses two AI agents to argue over code changes before posting a final inline comment. Ideal for thorough pre-merge reviews or local working tree checks. It requires delegate-skills and authenticated access to the version control platform.