Skill UI

This skill enables orchestrating agents to delegate bounded coding tasks to the OpenAI Codex CLI. The orchestrator drafts the brief, runs the command, and maintains full responsibility for reviewing diffs and committing changes. It prioritizes safety by preventing automatic commits, requiring explicit user requests and manual gate verification.

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.

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.

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.

Maintain durable feature-level memory across AI coding sessions using lightweight Markdown tracks. It organizes status, source-of-truth docs, decisions, risks, and changes for long-lived features. Helps AI agents resume work accurately without relying on chat history. Ideal for repositories needing structured project memory without reorganizing all documentation.

Build background coding agents in remote sandboxed environments with image registries, warm pools, snapshots, and self-spawning sessions for scalable multiplayer execution.

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.

This skill acts as an anti-over-engineering guardrail, instructing AI coding agents to maintain strict scope control and minimalism. It guides the AI to only modify the files and lines explicitly requested by the user, avoiding unnecessary abstractions, unrequested refactors, or scope creep. Use it when you prefer the simplest, most direct solution and want to prevent the agent from 'grinding' the code base.

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 skill implements advanced 'Gatekeeper' logic to optimize AI agent performance in long-running workflows. It prevents context window bloat by sharding metadata, allocating token budgets, and enforcing Atomic Precision to deliver purely functional, filler-free responses. Ideal for complex coding, multi-step reasoning, and large document analysis where conciseness is critical.

Audit-first SEO/AEO growth workflow for coding agents: discover the project, audit technical and content issues, implement fixes, research search intent, build foundational pages, set up measurement, deploy, verify, and optionally monitor weekly.