This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
OpenAI Codex CLI and cross-platform skill authoring. Use when setting up Codex CLI, converting or syncing skills between Claude Code and Codex, configuring agents/openai.yaml, or validating cross-platform skill compatibility.
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
The agent converts Claude Code skills to Codex-compatible format, validates cross-platform compatibility, and builds skill registry manifests. It generates agents/openai.yaml configurations from SKILL.md frontmatter, runs 17 compatibility checks across both platforms, and produces skills-index.json for discovery systems.
agents/openai.yaml and copying scripts/references/assets.skills-index.json manifest for registries, discovery, and version pinning.scripts/, references/, assets/ tree.agents/openai.yaml.skills-index.json for a skill library.Before converting or building, confirm these inputs. If any is unknown or vague, ASK — do not assume:
codex_skill_converter.py vs cross_platform_validator.py vs skills_index_builder.py)--strict and the pass/fail gate)Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.
| Tool | Purpose | Command |
|---|---|---|
codex_skill_converter.py | Convert a Claude Code SKILL.md into Codex format (agents/openai.yaml) | python scripts/codex_skill_converter.py <skill_md> [--output-dir DIR] [--json] |
cross_platform_validator.py | Run 17 Claude Code + Codex + cross-platform compatibility checks on a skill dir | python scripts/cross_platform_validator.py <skill_dir> [--strict] [--json] |
skills_index_builder.py | Build a skills-index.json manifest from a directory of skills | python scripts/skills_index_builder.py <skills_dir> [--output FILE] [--format json|human] [--category CAT] |
Load the reference that matches the task — keep this file lean and pull detail on demand:
agents/openai.yaml structure, discovery/locations, invocation patterns, cross-platform patterns, frontmatter compatibility, install/versioning, and sync/CI/CD/GitHub distribution. Read when configuring openai.yaml or distributing a library.This skill covers:
agents/openai.yamlskills-index.json) for discovery and distributionThis skill does NOT cover:
.cursorrules, .windsurfrules)| Skill | Integration | Data Flow |
|---|---|---|
| code-reviewer | Convert code-reviewer's SKILL.md to Codex format so it can run in Codex CLI | codex_skill_converter.py reads code-reviewer's SKILL.md and generates agents/openai.yaml |
| senior-fullstack | Validate fullstack skill's cross-platform compatibility after adding Codex support | cross_platform_validator.py checks both SKILL.md frontmatter and openai.yaml structure |
| senior-devops | Embed skill validation and index building into CI/CD pipelines | DevOps workflows call cross_platform_validator.py --strict --json and skills_index_builder.py as pipeline steps |
| tech-stack-evaluator | Evaluate whether Codex CLI fits a project's AI tooling stack | Tech stack evaluator references Codex CLI capabilities and configuration patterns from this skill |
| senior-architect | Architect multi-agent skill systems that span Claude Code and Codex CLI | Architect uses cross-platform skill patterns and index manifests to plan skill distribution |
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
This skill should be used when the user asks to "check accessibility", "audit WCAG compliance", "scan HTML for a11y issues", "check color contrast", or "find accessibility violations in web pages".
日本語の概要は準備中です。原文の説明を表示しています。
Design and run statistically rigorous A/B tests and experiments. Use when planning experiments, calculating sample sizes, designing test variants, selecting metrics, analyzing results, or when someone says "let's test that."
日本語の概要は準備中です。原文の説明を表示しています。
Design and analyze A/B tests: sample size, test duration, and statistical significance for conversion experiments. Use when setting up an A/B test, calculating sample size, designing an experiment, or analyzing results.
日本語の概要は準備中です。原文の説明を表示しています。
Sales execution across pipeline, discovery, demos, negotiation, and closing. Use when qualifying opportunities, running MEDDIC discovery, building account plans, handling objections, structuring proposals, or forecasting pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Design ad creative across Google, Meta, LinkedIn, Twitter/X, and TikTok with platform format specs, headline formulas, and A/B testing. Use when writing ad copy, generating headline variations, creating ad sets, or validating creative.
日本語の概要は準備中です。原文の説明を表示しています。
Answer Engine Optimization (AEO): optimize content to be cited by LLMs (ChatGPT, Claude, Perplexity, Gemini) in their answers. Use when designing content for LLM citation, auditing citability, or structuring Q&A schema.
日本語の概要は準備中です。原文の説明を表示しています。