Reference implementation of a v2 CodexKit skill. Copy this folder as a starting point for new skills. Demonstrates all 10 required sections, 4C verification, and the tier-2 folder structure.
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。
This is a working reference skill. Copy the entire
codexkit-skill-template/folder and rename it to create a new skill.
Write a clear, one-sentence purpose. Answer: "After running this skill, the user will have ___."
Identify the 3–5 key frameworks or standards that professionals use in this domain. Use the Deep Research Prompt to generate domain knowledge if needed.
Break the workflow into 4–7 steps. Each step should produce a concrete, visible output — not just "think about X."
Write domain-specific 4C questions. The Consequence question is the most important: "If this output were used immediately, what could go wrong?"
List situations where the skill's default approach breaks down. For each, provide a mitigation or fallback.
| Input | Required | Format |
|---|---|---|
| Skill domain description | Yes | Free text — the problem space this skill serves |
| Target audience | Yes | Who will consume the output (developers, managers, executives) |
| Existing materials | Recommended | Any templates, standards, or examples to build from |
A complete skill folder with:
SKILL.md following v2 format (10 sections)agents/openai.yaml with interface blockCHANGELOG.md initialized at v1.0.0name, description, version, category| Check | Question |
|---|---|
| Correctness | Does the skill's procedure match established domain frameworks? |
| Completeness | Are all 10 required sections present and substantive (not placeholder)? |
| Context-fit | Would the skill produce useful output for its stated audience? |
| Consequence | If a contributor copied this skill as-is, what would they get wrong? |
Good: A skill with 6 procedure steps where each step ends with "Produce: {specific table/section}." The 4C questions reference domain-specific metrics (e.g., "Does the NPS calculation exclude neutral responses?").
Bad: A skill with a single step "Analyze the data and produce a report." Verification says "Check if it's correct." No edge cases listed.
node ./scripts/validate-pack.mjs passes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
Design rigorous A/B test plans with hypothesis, sample size calculation, Minimum Detectable Effect (MDE), randomization strategy, and decision rules. Includes guardrail metrics and rollout playbook. Use when planning product experiments, conversion optimization, or data-driven feature decisions.
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。
Review REST and GraphQL API designs for consistency, usability, and best practices. Covers naming conventions, versioning strategy, error format, pagination, authentication patterns, and breaking change detection. Use when reviewing API specs, designing new APIs, or auditing existing endpoints.
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。
Write Architecture Decision Records (ADRs) following the Michael Nygard format. Captures context, options considered, decision rationale, and consequences. Use when making technology choices, framework selections, or any architectural decision that future developers need to understand.
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。
Assess organizational readiness for financial audits (internal or external). Map assertions to account balances, check evidence completeness, score readiness using a Red/Amber/Green framework, and generate a remediation timeline. Aligned with SOX, IFRS, and GAAP audit standards. Use before scheduled audits or when preparing for first-time compliance.
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 safe recurring Codex automations with clear prompts, outputs, schedules, and gating rules.
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。
Refine Product Backlog Items to meet INVEST criteria. Write User Stories with Acceptance Criteria in Given/When/Then format, estimate with Story Points, and flag dependencies. Use before sprint planning when backlog items need grooming. Do not use to prioritize the backlog — that is the Product Owner's decision.
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。