Skill UI

Configure New Relic for full-stack observability: install infrastructure agents on Linux, Docker and Kubernetes, integrate APM agents for Node.js, Python, Java and Go, add custom events, metrics and spans, and write NRQL queries for dashboards and alerts.

A remote MCP server designed for AI agents to dynamically discover, inspect, and verify external tools, APIs, and services. It allows agents to search a curated index of AI-ready websites, check for machine-readable endpoints (like OpenAPI or MCP), and confirm if an endpoint responds correctly to JSON-RPC, enabling robust, dynamic agent workflow building.

Plan first, then orchestrate parallel agents with adversarial verification via the local odw daemon. Use when a task spans many files or items and benefits from concurrent agents.

Open Dynamic Workflows (ODW) is an advanced, open-source engine designed to manage complex coding tasks using multiple AI agents. It operates in three stages: planning the task into parallel subtasks, orchestrating various coding agents (like Codex or Antigravity) to work concurrently, and finally, performing an adversarial verification pass to challenge and validate the combined output before merging. It supports multiple model providers and is ideal for structured, high-reliability software development.

Guides agents through bounded parameter optimization for configurable systems with measurable objectives: define search space, budget, and baseline, run trials one at a time, log evidence, and recommend a configuration for HPO, inference tuning, simulations, or RL/control experiments.

OrcaReplay enables agents to query past execution recordings instead of relying on memory. It supports replaying or forking previous agent runs to investigate why specific actions occurred or to reproduce failures. This skill ensures accurate debugging by reading traces directly, distinguishing between recorded evidence and inferred data, and safely re-executing commands within isolated environments to verify historical behavior.

Coordinate focused subagents on substantial work while maintaining non-overlapping ownership and integrating verified results. Ideal for large-scope tasks where parallel processing reduces elapsed time. Decompose tasks into distinct assignments, run scouts in parallel, assign distinct ownership, and synthesize outputs. Keep trivial work with the coordinator to avoid overhead.

This skill provides a systematic pipeline for safely executing large-scale code refactors across multiple files or subsystems. It first performs parallel, dependency-aware analysis to map the entire scope, synthesizing a single, actionable plan. The process then breaks the work into independent 'work packets,' which are executed sequentially or in parallel by specialized worker agents, ensuring strict dependency management and behavior parity throughout the entire process.

A specialized skill designed for agents to proactively identify, analyze, and strategize high-impact contributions within trending Open Source repositories. It systematically performs repository discovery, extracts targeted issues (like 'help wanted' or 'good first issue'), and conducts deep feasibility analysis (including mergeability and complexity). Finally, it generates a structured 'Contribution Dossier' with actionable plans, significantly accelerating the process of becoming a top-tier OSS contributor.

API-first control plane for reply-aware cold outbound email workflows, managing inboxes, contacts, templates, sequences, approvals, pacing, webhooks, and delivery metrics for AI agents.

Coordinates multiple specialized agents via Claude Code's native Agent Tool for complex tasks requiring diverse expertise, such as comprehensive code analysis, feature reviews, and security audits. Enables sequential chains, context passing, and resumable work, with a synthesis protocol for unified recommendations.

Coordinates a bounded code review of a defined diff from correctness, authorization and performance perspectives, delegating to independent agents only when authorized and reproducing important findings centrally.