Skill UI

The Ejentum Reasoning Harness is a comprehensive library of cognitive operations designed to guide complex AI agents through critical thinking tasks. It exposes four specialized harnesses (reasoning, code, anti-deception, memory) that mitigate common failure modes like hallucination and reasoning decay. Instead of a fixed answer, the agent ingests a detailed, executable scaffold—including failure patterns, procedures, and falsification tests—to ensure robust, self-verified output.

A structured founder interview designed to gather the canonical, comprehensive data for a company, including financials, team structure, business metrics, and strategic risks. It generates a single, authoritative `company-context.md` file, serving as the single source of truth for all subsequent AI agents and strategic decision-making processes. Essential for initial setup or after major company life events like funding rounds or pivots.

Expert skill for building Model Context Protocol (MCP) servers and tools from scratch. Covers the full development lifecycle—from defining tool schemas and resources to implementation, testing, and publishing to registries. Supports robust patterns using TypeScript and Python for integrating custom capabilities into AI agents.

This is a comprehensive lookup reference for the Mercury MCP server tools. It guides AI agents on how to interact with a virtual team workspace, enabling structured communication for messaging teammates, managing threads and tasks, scheduling complex automations, and performing administrative graph edits. Use this when your agent needs to execute specific team workflows.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

A comprehensive skill orchestrator designed for AI coding agents working with Go/Golang. Instead of relying on single skills, this tool analyzes the entire task context—be it designing an API, debugging a panic, or auditing security—and dynamically loads all necessary, relevant skills (e.g., concurrency, testing, error handling, and security) simultaneously. It ensures a holistic approach to Go development.

This skill captures and persists critical user decisions, project constraints, and architectural choices across multiple sessions and agents. It saves this context into a structured memory database (`mdbase`), ensuring that future development steps or agents can retrieve the original intent without re-deriving it from scratch. Ideal for documenting project rules, tech stacks, and design rationale.

Polis Protocol transforms static AI agent workflows into a self-improving, dynamic team environment. It uses a learning router to assign tasks based on an agent's historical track record and effectiveness on specific domains, rather than fixed roles. It is vendor-agnostic, allowing diverse agents (e.g., Claude, Gemini) to collaborate under a unified, auditable governance structure where agents can propose and vote on amending the protocol's core rules.

This tool audits the activation metadata of coding skills used by AI agents. It grades skills (A-F) based on description clarity, simulates which skill a prompt will trigger, and identifies metadata collisions where one skill might silently overshadow another. It is essential for ensuring robust, predictable, and reliable AI agent behavior during development and CI processes.

This skill provides a comprehensive playbook for setting up and orchestrating multi-agent systems using the standalone Antigravity 2.0 Agent Manager alongside the Antigravity IDE. It guides developers on creating a dual-window workspace to coordinate specialized AI agents (e.g., front-end, back-end, QA) to work in parallel on a single codebase, significantly boosting development efficiency and managing concurrent code changes.

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.

This tool automates the diagnosis of production errors tracked by Sentry. By leveraging the Composio CLI, it retrieves comprehensive issue details, full stack traces, breadcrumbs, and associated commits. It maps stack frames to local source code, allowing agents to propose fixes and triage issues directly, eliminating manual copy-pasting.