codexkit-skill-template

# Skill Template (Exemplar) > This is a working reference skill. Copy the entire `codexkit-skill-template/` folder and rename it to create a new skill. ## When to Use - When creating a brand-new CodexKit skill from scratch - When upgrading an existing skill to the v2 format - When reviewing whether a skill meets the quality bar ## Procedure ### Step 1 — Define the Intent and Trigger Write a clear, one-sentence purpose. Answer: "After running this skill, the user will have ___." Then decide whether it should be directly invoked by name, automatically selected from its description, or support both. Keep the description concrete enough to distinguish it from neighboring skills. ### Step 2 — Map the Domain Identify the 3–5 key frameworks or standards that professionals use in this domain. Use the [Deep Research Prompt](../../templates/deep-research-prompt.md) to generate domain knowledge if needed. ### Step 3 — Write the Procedure Break the workflow into 4–7 steps. Each step should produce a concrete, visible output — not just "think about X." ### Step 4 — Add Verification Write domain-specific 4C questions. The Consequence question is the most important: "If this output were used immediately, what could go wrong?" ### Step 5 — Document Edge Cases List situations where the skill's default approach breaks down. For each, provide a mitigation or fallback. ## Inputs | 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 | ## Output A complete skill folder with: - `SKILL.md` following v2 format (10 sections) - `agents/openai.yaml` with interface block - `CHANGELOG.md` initialized at v1.0.0 ## Quality Criteria - [ ] Purpose is a single, clear sentence — no compound goals - [ ] Invocation mode and trigger description are clear; near-miss requests remain out of scope - [ ] Procedure steps each produce a visible deliverable - [ ] Quality Criteria are measurable or concretely observable - [ ] 4C Verification questions are domain-specific, not generic - [ ] Edge Cases cover at least 3 realistic failure scenarios - [ ] Examples show annotated contrast between good and bad output - [ ] Frontmatter has `name`, `description`, `version`, `category` ## Verification (4C) | 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? | ## Edge Cases - **Skill spans multiple domains** — Split into separate skills. One skill = one task. - **No established framework exists** — Document the decision rationale in Procedure. Use first-principles reasoning instead of citing standards. - **Output format varies by context** — Provide 2–3 output templates and add selection criteria. ## Examples > **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. ## Definition of Done - [ ] All 10 sections filled with substantive content - [ ] ``agents/openai.yaml` created with valid interface block - [ ] `node ./scripts/validate-pack.mjs` passes - [ ] At least one person besides the author has reviewed the skill ## Changelog - v1.0.0 — Initial release as exemplar for 5-Layer Skill Framework