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Found 52 Skills
Run the Codex Readiness integration test. Use when you need an end-to-end agentic loop with build/test scoring.
Plans.mdのタスクを実装。スコープを聞いて自動判断、1タスクから全タスクまで。Use when user mentions '/work', execute plan, implement tasks, build features, work on tasks, 'do everything', 'implement', '実装して', '全部やって', 'ここだけ'. Do NOT load for: planning, reviews, setup, deployment, or breezing (team execution).
Multi-agent orchestration layer for OpenAI Codex CLI. Provides 30 specialized agents, 40+ workflow skills, team orchestration in tmux, persistent MCP servers, and staged pipeline execution.
Automated code review workflow using OpenAI Codex CLI. Implements iterative fix-and-review cycles until code passes validation or reaches iteration limit. Use when building features requiring automated code validation, security checks, or quality assurance through Codex CLI.
This is intended for use when OpenSpec workflows require dependency-aware parallel subagents that are compatible with OPSX commands, legacy OpenSpec commands, and Codex CLI prompt aliases.
Setup and workflow for using sqry semantic code search as an MCP server with OpenAI Codex CLI. Covers installation, MCP configuration via `~/.codex/config.toml`, and recommended patterns for code analysis tasks. Install this skill to give Codex access to sqry's 34 AST-based code analysis tools.
OpenAI Codex CLI code review with GPT-5.2-Codex, CI/CD integration
Invokes Codex CLI as a second opinion. Use for reviewing plans, code, architectural decisions, or getting an independent perspective from OpenAI's reasoning models.
Multi-instance (Multi-Agent) orchestration workflow for deep research: Split a research goal into parallel sub-goals, run child processes in the default `workspace-write` sandbox using Codex CLI (`codex exec`); prioritize installed skills for networking and data collection, followed by MCP tools; aggregate sub-results with scripts and refine them chapter by chapter, and finally deliver "finished report file path + key conclusions/recommendations summary". Applicable to: systematic web/data research, competitor/industry analysis, batch link/dataset shard retrieval, long-form writing and evidence integration, or scenarios where users mention "deep research/Deep Research/Wide Research/multi-Agent parallel research/multi-process research".
Debate implementation plans between Claude Code and Codex CLI. After Claude Code creates a plan, invoke this skill to have Codex review it. Both AIs debate through multiple rounds until reaching full consensus before implementation begins.
Ask Codex via local CLI and capture a reusable artifact
Run, watch, debug, and extend OpenClaw QA testing with qa-lab and qa-channel. Use when Codex needs to execute the repo-backed QA suite, inspect live QA artifacts, debug failing scenarios, add new QA scenarios, or explain the OpenClaw QA workflow. Prefer the live OpenAI lane with regular openai/gpt-5.4 in fast mode; do not use gpt-5.4-pro or gpt-5.4-mini unless the user explicitly overrides that policy.