.github/skills/skill-creator/SKILL.md
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
npx skillsauth add PackmindHub/packmind skill-creatorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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...").
scripts/)Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.
scripts/rotate_pdf.py for PDF rotation tasksreferences/)Documentation and reference material intended to be loaded as needed into context to inform Claude's process and thinking.
references/finance.md for financial schemas, references/mnda.md for company NDA template, references/policies.md for company policies, references/api_docs.md for API specificationsassets/)Files not intended to be loaded into context, but rather used within the output Claude produces.
assets/logo.png for brand assets, assets/slides.pptx for PowerPoint templates, assets/frontend-template/ for HTML/React boilerplate, assets/font.ttf for typographySkills 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:
scripts/rotate_pdf.py script would be helpful to store in the skillExample: 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:
assets/hello-world/ template containing the boilerplate HTML/React project files would be helpful to store in the skillExample: When building a big-query skill to handle queries like "How many users have logged in today?" the analysis shows:
references/schema.md file documenting the table schemas would be helpful to store in the skillTo 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:
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.
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:
scripts/package_skill.py <path/to/skill-folder>
Optional output directory specification:
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 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:
tools
Record polished UI demo videos and screenshots of a running web app using Playwright MCP — for client deliverables, release notes, feature walkthroughs, or bug repros. Produces an HD WebM video with chapter markers, a mandatory animated cursor overlay, and a mandatory subtitle bar that narrates each step (positioned deliberately so it never masks the UI being demonstrated), plus full-page screenshots at each step. Use this whenever the user asks to "record a demo", "create a screencast", "make a UI walkthrough video", "document this feature with video", "show the client how X works", "capture screenshots of the app", or anything similar — even when the user only says "make a video" or "take screenshots" in the context of a running frontend. Also use it when the user wants to demonstrate a workflow, generate marketing-quality footage of an app, or produce repeatable visual documentation.
tools
The canonical recipe for starting, checking, and stopping the Packmind local dev stack with Docker Compose — the single source of truth other skills and the Michel agent defer to. Covers bringing the full stack (PostgreSQL, Redis, NestJS API, React/Vite frontend on :4200, MCP server, nginx) up in the background, the init services (dependency install + TypeORM migrations) you must wait on, the critical host-port trap that the API on container port 3000 is NOT exposed to the host and must be reached via the frontend Vite proxy at localhost:4200/api/v0, confirming the API and frontend are actually serving before you depend on them, the persistent-volume gotcha that leaves stale Postgres schema and node_modules behind between runs, building the CLI, and tearing everything down so no container is left blocking the run. Use this whenever you need Packmind running locally — to verify a change, record a UI or CLI demo, hit the API, seed data, or reproduce a bug — and whenever you are about to start or stop `docker compose`. If you are an autonomous agent (e.g. Michel) that started the stack, you MUST use the teardown half before finishing. Prefer this over running `nx serve` on the host for anything that needs the real, containerized stack.
tools
Best practices for creating GitHub pull requests that include inline images — CLI terminal screenshots (from cli-demo-recorder), UI screenshots/videos (from ui-demo-recorder), or any other visual artifact. Use this skill whenever opening or updating a PR that has visual artifacts to embed, or when images aren't rendering in a PR description. Also use it when asked "how do I add screenshots to a PR", "why isn't my image showing", or "embed a demo in the PR".
tools
--- name: michel-create-packmind-dataset description: Seed a local Packmind instance with a realistic dataset — one organization populated with standards, commands, and skills — so an autonomous agent can exercise its own changes against lifelike data instead of an empty app. Use this whenever you need populated Packmind data to verify a change end-to-end: reproducing a bug that only shows with existing artifacts, recording a UI/CLI demo that needs content on screen, smoke-testing a new endpoint