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This skill should be used when creating, refining, or auditing Claude Code Skills, including SKILL.md structure, scaffolding, packaging, and best practices.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
This skill should be used when auditing the recurring per-Anthropic-model-release checklist (model IDs, claude-code-action pin freshness, pricing drift, tier-map re-evaluation): it auto-fixes stale model-ID swaps into a CI-gated PR and flags the rest.
This skill should be used when performing exhaustive code reviews using multi-agent analysis, ultra-thinking, and worktrees.
This skill should be used when designing agent-native applications where agents are first-class citizens: architecting autonomous agents, creating MCP tools, building apps where features are agent-driven outcomes.
This skill should be used when working with DSPy.rb, a Ruby framework for type-safe, composable LLM applications.
This skill provides a promptfoo eval harness that measures whether a Soleur skill or agent edit actually improves behavior, comparing a skill arm against a baseline control arm.
This skill should be used when resolving all TODO comments in the codebase using parallel processing. It analyzes dependencies, creates a resolution plan with a mermaid flow diagram, and spawns parallel resolver agents.
| name | skill-creator |
| description | This skill should be used when creating, refining, or auditing Claude Code Skills, including SKILL.md structure, scaffolding, packaging, and best practices. |
| license | Complete terms in LICENSE.txt |
This skill provides guidance for creating effective skills.
Skills are modular, self-contained packages that extend Claude's capabilities by providing specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific domains or tasks—they transform Claude from a general-purpose agent into a specialized agent equipped with procedural knowledge that no model can fully possess.
Every skill consists of a required SKILL.md file and optional bundled resources:
skill-name/
├── SKILL.md (required)
│ ├── YAML frontmatter metadata (required)
│ │ ├── name: (required)
│ │ └── description: (required)
│ └── Markdown instructions (required)
└── Bundled Resources (optional)
├── scripts/ - Executable code (Python/Bash/etc.)
├── references/ - Documentation intended to be loaded into context as needed
└── assets/ - Files used in output (templates, icons, fonts, etc.)
Metadata Quality: The name and description in YAML frontmatter determine when Claude will use the skill. Be specific about what the skill does and when to use it. Use the third-person (e.g. "This skill should be used when..." instead of "Use this skill when...").
Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.
package.json is type:module, so a .js file using module.exports/require (CommonJS) fails at runtime with MODULE_NOT_FOUND (not at tsc). Name CommonJS helper/assert scripts .cjs (e.g. promptfoo custom asserts, which load via require); reserve .js/.mjs for genuine ESM. See knowledge-base/project/learnings/build-errors/2026-06-15-cjs-required-for-commonjs-scripts-and-promptfoo-harness-gotchas.md.Documentation and reference material intended to be loaded as needed into context to inform Claude's process and thinking.
Files not intended to be loaded into context, but rather used within the output Claude produces.
Skills use a three-level loading system to manage context efficiently:
*Unlimited because scripts can be executed without reading into context window.
To create a skill, follow the "Skill Creation Process" in order, skipping steps only if there is a clear reason why they are not applicable.
Skip this step only when the skill's usage patterns are already clearly understood. It remains valuable even when working with an existing skill.
To create an effective skill, clearly understand concrete examples of how the skill will be used. This understanding can come from either direct user examples or generated examples that are validated with user feedback.
For example, when building an image-editor skill, relevant questions include:
To avoid overwhelming users, avoid asking too many questions in a single message. Start with the most important questions and follow up as needed for better effectiveness.
Conclude this step when there is a clear sense of the functionality the skill should support.
To turn concrete examples into an effective skill, analyze each example by:
Example: When building a pdf-editor skill to handle queries like "Help me rotate this PDF," the analysis shows:
Example: When designing a frontend-webapp-builder skill for queries like "Build me a todo app" or "Build me a dashboard to track my steps," the analysis shows:
Example: When building a big-query skill to handle queries like "How many users have logged in today?" the analysis shows:
To establish the skill's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets.
At this point, it is time to actually create the skill.
Skip this step only if the skill being developed already exists, and iteration or packaging is needed. In this case, continue to the next step.
When creating a new skill from scratch, always run the init_skill.py script. The script conveniently generates a new template skill directory that automatically includes everything a skill requires, making the skill creation process much more efficient and reliable.
Usage:
${CLAUDE_PLUGIN_ROOT:-plugins/soleur}/skills/skill-creator/scripts/init_skill.py <skill-name> --path <output-directory>
The script:
scripts/, references/, and assets/After initialization, customize or remove the generated SKILL.md and example files as needed.
bun test plugins/soleur/test/components.test.ts to check the current word budget (1800 limit). Target 12-15 words for new skill descriptions — the budget is at 99% capacity as of 2026-04-05.plugins/soleur/docs/_data/skills.js in the SKILL_CATEGORIES map -- skills are NOT auto-discovered, and missing this step silently removes the skill from the docs site with no build error.subagent_type because the plugin registry is stale at session start; for live testing, use general-purpose agent type and embed the full instructions in the task prompt.When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of Claude to use. Focus on including information that would be beneficial and non-obvious to Claude. Consider what procedural knowledge, domain-specific details, or reusable assets would help another Claude instance execute these tasks more effectively.
To begin implementation, start with the reusable resources identified above: scripts/, references/, and assets/ files. Note that this step may require user input. For example, when implementing a brand-guidelines skill, the user may need to provide brand assets or templates to store in assets/, or documentation to store in references/.
Also, delete any example files and directories not needed for the skill. The initialization script creates example files in scripts/, references/, and assets/ to demonstrate structure, but most skills won't need all of them.
Writing Style: Write the entire skill using imperative/infinitive form (verb-first instructions), not second person. Use objective, instructional language (e.g., "To accomplish X, do Y" rather than "You should do X" or "If you need to do X"). This maintains consistency and clarity for AI consumption.
To complete SKILL.md, answer the following questions:
Once the skill is ready, it should be packaged into a distributable zip file that gets shared with the user. The packaging process automatically validates the skill first to ensure it meets all requirements:
${CLAUDE_PLUGIN_ROOT:-plugins/soleur}/skills/skill-creator/scripts/package_skill.py <path/to/skill-folder>
Optional output directory specification:
${CLAUDE_PLUGIN_ROOT:-plugins/soleur}/skills/skill-creator/scripts/package_skill.py <path/to/skill-folder> ./dist
The packaging script will:
Validate the skill automatically, checking:
Package the skill if validation passes, creating a zip file named after the skill (e.g., my-skill.zip) that includes all files and maintains the proper directory structure for distribution.
If validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command again.
After package_skill.py validates the structure but BEFORE the zip is
distributed, invoke the skill-security-scan advisory gate against the
newly-scaffolded SKILL.md:
bash ${CLAUDE_PLUGIN_ROOT:-plugins/soleur}/skills/skill-security-scan/scripts/run-scan.sh < <skill-folder>/SKILL.md
Operator handling:
LOW-RISK — proceed with packaging.REVIEW — present findings as informational; ask operator to confirm
before packaging.HIGH-RISK — present findings + override instructions referencing
skill-security-scan/references/override-mechanism.md.
Block packaging until either (a) the SKILL.md is revised, or (b) a valid
override artifact is committed under knowledge-base/engineering/security/skill-overrides/.The PreToolUse hook on Write (.claude/hooks/skill-security-scan-write.sh)
is the load-bearing gate at the tool layer — this Step 5b is the
operator-friendly cooperative-fast-path that surfaces findings early.
After testing the skill, users may request improvements. Often this happens right after using the skill, with fresh context of how the skill performed.
Iteration workflow:
bash -c "$VAR", process substitution, an eval-style invocation), label the fence ```text instead of ```bash and document the load-bearing mitigations (timeout, reject-regex, env scrub, trust source) in a per-skill Sharp Edges section. Otherwise the skill-security-scan calibration suite fails the first-party SKILL.md with HIGH-RISK on code-execution — there is no per-finding override mechanism for first-party legitimate uses. The fence is documentation; the orchestrator still dispatches via the Bash tool when it reads the prose, so runtime behavior is unchanged. See knowledge-base/project/learnings/2026-05-20-skill-md-shell-active-prose-calibration-carve-out.md.For auditing and improving existing skills, see the audit-skill workflow.
For detailed guidance on skill authoring, see:
Step-by-step guides for specific skill authoring tasks:
Starter templates for common skill types: