Create Skill

Skill & Command Generator

Generate well-structured skills or slash commands. Both are markdown files with YAML frontmatter—they share the same structure but differ in how they're triggered and described.

Phase 0: Understand Requirements

Parse $ARGUMENTS for type hint. If $ARGUMENTS is empty or insufficient, use AskUserQuestion to gather requirements — users are often unclear on what type of artifact they need or what the best design is.

Use AskUserQuestion to collect:

1. Primary objective — What should this do?
2. Trigger scenarios — When should it activate?
3. Inputs/outputs — What does it receive and produce?
4. Complexity — Simple, standard, or complex?
5. Execution needs — Isolated context? Delegated to specialized agent? Proceed to Phase 1 when at minimum Objective and Trigger Scenarios are established. Remaining dimensions can be resolved during generation.

Phase 1: Generate

Apply these principles throughout generation: use imperative voice and terse phrasing because every token in a generated skill body costs budget on every invocation, and Claude extrapolates well from precise nudges. Prefer instruction over example — state the rule with its reasoning so it generalizes to every input.

If creating a new skill directory (not editing an existing file):

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python3 ${CLAUDE_PLUGIN_ROOT}/skills/create-skill/scripts/init_skill.py <name> --path <dir> [--resources scripts,references,assets] [--examples]

Exit 0 = directory created, proceed to Step 1. Exit 1 = naming collision; ask user whether to overwrite or rename.

Step 1 — Choose type

• Skills: Trigger-rich, third-person description ("This skill should be used when..."); auto-triggered by routing
• Commands: Concise, verb-first description, under 60 chars; user-invoked via / menu

Step 2 — Write frontmatter

Read ${CLAUDE_PLUGIN_ROOT}/skills/create-skill/references/frontmatter-options.md for the full field catalog, description patterns, tool selection framework, and execution modifiers.

Description density rules: Keep descriptions under 150 tokens (200 absolute max) — they load every session. Descriptions over 250 characters are truncated in the skill listing. Derive trigger phrases from the user's actual words in Phase 0, not paraphrases. See the token budget, pushy routing pattern, and trigger derivation principles in frontmatter-options.md.

Intensional over extensional — apply to all generated content. State the rule directly with its reasoning rather than listing examples that imply the rule. An intensional rule ("quoted phrases must be verbatim user speech because routing matches on literal tokens") generalizes to every input the skill will encounter. An extensional approach requires the reader to reverse-engineer the rule — two reasoning hops instead of one, covering only the shape of those specific examples. Since this skill generates instructions that will themselves guide further generation, the quality of reasoning propagates.

Step 3 — Validate description discoverability

Before writing the body, verify the description will route correctly. Mentally generate:

1. 3 should-trigger prompts — realistic user messages that should activate this skill. Include at least one naive phrasing from a user who has never heard of the skill.
2. 3 should-NOT-trigger prompts — messages in adjacent domains that are close but should not activate. These test whether the description is too broad.

Evaluate: does the description cover all should-trigger prompts? Would it plausibly reject the should-NOT-trigger prompts? If coverage is weak, revise the description — add missing trigger phrases, tighten language to exclude adjacent domains, or add a negative trigger ("Not for X").

This step catches routing misses before the rest of the skill is built. Proceed when description coverage is adequate.

Step 4 — Write body

Construction rules:

• State objective explicitly in first sentence
• Use imperative voice ("Analyze", "Generate", "Identify") — no first-person ("I will", "I am")
• Context only when necessary for understanding
• XML tags only for complex structured data
• No "When to Use This Skill" section — body loads only after triggering; routing guidance there is never read by the routing decision
• Avoid headers deeper than H3 — deep nesting signals content that belongs in references/, not SKILL.md
• Use bang-backtick syntax for dynamic context injection when real-time data (git status, file list, env vars) improves the skill without requiring a tool call
• Preserve variable bindings when collapsing code blocks to prose. Code blocks serve two purposes: illustrating an operation and establishing workflow state. When a code block assigns variables (BASE=..., BRANCH=...) that later steps reference, collapsing it to prose without preserving the bindings leaves downstream $VAR references unbound. Add a "derive working variables" preamble that explicitly binds each variable in prose before the steps that use them

Both skills and commands follow the same body pattern:

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# Name

Brief overview (1-2 sentences).

## Process
1. Step one (imperative voice)
2. Step two
3. Step three

Dynamic Content:

Example — injecting live context. In a skill body, write lines like:

• Current branch: !`git branch --show-current`
• Recent commits: !`git log --oneline -5`
• Changed files: !`git diff --name-only`

Then add directives like "Summarize this pull request..."

These commands run when the skill is invoked. The model sees only the output — no tool calls needed. Use this for infallible probes (git status, env vars, file trees, process output) where failure is rare and the output is informational. Do not use for commands that may fail or need exit-code branching — those require Bash tool calls so the model can handle errors.

Note: The backslashes above escape the backticks so this documentation doesn't execute — in a real skill, write !`cmd` without the backslashes.

Step 5 — Script opportunity scan

Read ${CLAUDE_PLUGIN_ROOT}/skills/create-skill/references/script-patterns.md and apply the five signal patterns to every workflow step in the skill being generated:

For each identified script candidate:

1. Choose the archetype from references/script-patterns.md (init/validate/transform/package/query)
2. If the interface is non-trivial, delegate to claude-skills:create-cli skill to design it
3. Scaffold the script in scripts/ using the Python template from references/script-patterns.md
4. Wire it into SKILL.md with: trigger condition, exact invocation, output interpretation

Wiring rule: A script reference must state when to invoke (trigger condition), how to invoke (exact command with flags), and what to do with the result (exit code handling, which output fields matter).

Step 6 — Check delegation

Scan for existing resources before finalizing:

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Review available: skills, commands, agents, MCPs
For each workflow step, ask: "Do we already have this?"

Common delegation patterns:

• Git commits → Skill: claude-coding:commit

Always use fully qualified names:

• Skill: plugin-dev:hook-development (not just "hook-development")
• SlashCommand: /plugin-dev:create-plugin (not just "create-plugin")
• Task: subagent_type=plugin-dev:agent-creator

Step 7 — Validate

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python3 ${CLAUDE_PLUGIN_ROOT}/skills/create-skill/scripts/validate_skill.py <skill-directory> --output json

Exit 0 = proceed to Phase 2. Exit 1 = parse the errors array; each entry has field, message, severity. Resolve all critical and major items before writing to disk.

Phase 2: Deliver

Output Paths

Write and Confirm

Before writing:

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Writing to: [path]
This will [create new / overwrite existing] file.
Proceed?

Explain Your Choices

When presenting the generated skill/command to the user, briefly explain:

• What you set and why — "Added allowed-tools to scope Bash to git commands only, since the skill only needs git for commits"
• What you excluded and why — "hooks omitted (no validation needed), disable-model-invocation left unset (auto-triggering is appropriate)"
• Add more trigger phrases if routing misses expected inputs

Package for Distribution

Only when user explicitly requests a distributable file, run:

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python3 ${CLAUDE_PLUGIN_ROOT}/skills/create-skill/scripts/package_skill.py <skill-directory> [output-dir]

Exit 0 = .skill file created at output path. Exit 1 = validation failed; read stdout for details.

After Creation

Summarize what was created:

• Name and type
• Path
• How to invoke/trigger
• Suggested test scenario

Phase 3: Structural Lint

After writing the skill to disk, invoke the skill-lint agent to run a structural audit:

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Use Task tool with subagent_type=claude-skills:skill-lint:
"Lint the skill at <path-to-skill-directory>. Auto-apply critical and major fixes, report
minor findings for user decision."

Wait for the agent to complete. If it auto-applied fixes, note them in the Phase 4 summary. If it reports minor findings, include them in the evaluation output for the user to decide.

Proceed to Phase 4 when the lint agent returns.

Phase 4: Evaluate

Score the generated skill/command:

Target: 9.0/10.0. If below, refine once addressing the weakest dimension, then deliver.

Before finalizing, load ${CLAUDE_PLUGIN_ROOT}/skills/create-skill/references/generation-standards.md and verify the validation checklist passes.

Execute phases sequentially.