Skill UI

This guide details the comprehensive workflow for distributing a single AI skill across various local agent skill directories (Codex, Claude, Pi, Hermes). It addresses canonical locations, symlink management, and specific synchronization commands to ensure that all agents detect the latest skill version while correctly navigating common development pitfalls.

Ditto mines user words from local coding-agent session logs to create private work, design, and writing profiles. It requires explicit user approval before mining begins and keeps all data local. This skill routes a compatible Ditto runtime to extract patterns from real coding history rather than questionnaires, ensuring privacy and evidence-backed personalization for AI agents.

EAS Simulator runs remote iOS or Android emulators on EAS infrastructure, enabling developers to test apps via CLI, AI agents, or browser preview. It unlocks testing environments where local simulators cannot run, such as Linux or cloud agents. Use this to verify app changes remotely on real device environments.

This skill helps developers create highly focused, folder-specific documentation (`CLAUDE.md` and `AGENTS.md`). It systematically analyzes all files in a target directory to extract project purpose, operational constraints, naming conventions, and critical decisions. This deep context ensures that future AI agents (like Claude or Codex) operate with localized knowledge, preventing them from relying on global, generalized instructions and improving their accuracy in that specific domain.

Maps, explains, and lints repository-scoped coding-agent instruction files such as AGENTS.md, CLAUDE.md, Copilot and Cursor rules. Run local read-only scans to inventory instructions, audit recursive imports, and export JSON or SARIF diagnostics before changing code.

Audit named agent instructions, tool definitions, and embedded prompts locally with LintLang 0.8.0. Reports finding codes, severities, and locations without editing files or calling a model.

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.

Set up OpenClaw locally or on a Mac mini with Docker Compose, macOS hardening, launchd services, networking, monitoring, and production-grade always-on agent workflows.

Find, use, create, install, share, update, and migrate agent skills stored in OpenViking, including from Git or local folders, so they work across harnesses and machines.

Pilot Protocol provides AI agents with permanent network addresses, encrypted P2P messaging, and NAT traversal capabilities. It includes an installable app store for agent-native services. Use it for direct agent communication, stable addressing across clouds, and installing local capabilities without REST plumbing.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.

This skill scaffolds the necessary configurations for advanced engineering workflows. It guides the user through setting up the issue tracking system (GitHub, GitLab, or local markdown), defining the canonical triage label vocabulary, and establishing the domain documentation context (single or multi-context). It must be run once to initialize the repository for subsequent agent-driven development skills.