
Article Prompts to Skills
FreeTransform articles into reusable AI skills.
Free · Opens the source repo
What Article Prompts to Skills does
Article Prompts to Skills is a tool designed for developers and designers looking to convert existing articles, tutorials, or prompt packs into modular AgentSkills. This skill focuses on extracting independent capabilities from source material, allowing users to create reusable skills that can be easily integrated into various projects. By following a structured approach, users can ensure that the resulting skills maintain the essential mechanics of the original content while stripping away unnecessary branding and layout elements.
The extraction process begins with a careful inspection of the source material, where users are encouraged to inventory explicit prompts, examples, and acceptance criteria. This ensures that each skill created is grounded in the original content and serves a specific purpose. The skill emphasizes the importance of creating one skill per reusable behavior, avoiding the pitfalls of combining unrelated prompts into a single vague package.
Once the extraction ledger is built, users can package each skill according to a defined structure, including necessary files such as SKILL.md and demo assets. The skill provides guidelines for writing effective prompts and creating demos that showcase the core functionality of the new skills. By adhering to these instructions, users can produce skills that are not only functional but also easy to validate and demonstrate.
This tool is particularly valuable for those who frequently work with educational content, design patterns, or workflow ideas and wish to turn them into actionable skills for AI agents. By streamlining the conversion process, Article Prompts to Skills helps users save time and effort while enhancing their development workflow.
When to use it
Use this skill when you need to convert prompts or tutorials into independent AgentSkills for AI applications.
When not to use it
This skill may not be suitable for creating skills from loosely structured content or when the source material lacks clear prompts.
What you can build with it
Converting a Design Tutorial
Transform a step-by-step design tutorial into focused skills that can be reused across different projects.
Creating Skills from Prompt Packs
Extract and package prompts from a prompt pack into independent skills that can be easily invoked by AI agents.
Streamlining Educational Content
Turn educational articles into actionable skills that facilitate learning and interaction for AI applications.
How to install Article Prompts to Skills
View source1. Install with the skills CLI
npx skills add mengto/skills/article-prompts-to-skills --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by mengtoArticle Prompts to Skills
Turn source prompts into small, demonstrable capability packages. Preserve the useful behavior, not the source page's brand or layout.
1. Inspect Before Extracting
- Read the repository instructions and the complete source article.
- Run
git status --short; preserve unrelated work. - Inventory every explicit prompt, heading, example, asset, and acceptance criterion.
- Search existing
SKILL.mdfiles for overlapping capabilities before creating folders. - If the source is raw HTML and the prompts do not exist yet, use
$html-to-interaction-promptsfirst, then return here.
Do not infer a family of skills from a title alone. Trace each proposed skill to source evidence.
2. Build an Extraction Ledger
Create a working table before editing files:
| Source prompt | Reusable capability | Keep | Remove | Skill name | Demo proof |
|---|
Apply these boundaries:
- Create one skill per independently reusable behavior.
- Merge steps only when separating them would make either step unusable.
- Update an existing skill when its contract already covers the capability.
- Skip decorative or editorial fragments that do not create a repeatable method.
- Name skills after the outcome or mechanism, never after the source article.
3. Extract the Portable Contract
Keep the source's successful mechanics:
- behavior and state transitions;
- data model and parameter defaults;
- timing, easing, spacing, and responsive rules;
- accessibility, reduced-motion, and keyboard behavior;
- performance constraints and failure modes;
- acceptance checks that prove the result.
Remove source-specific packaging:
- brand names, marketing copy, and proprietary content;
- page layout that is unrelated to the capability;
- hard-coded palettes, assets, and selectors;
- incidental implementation choices that do not affect the outcome.
The result must transfer to a different subject, layout, and visual system without rewriting the core instructions.
4. Package Every Skill
Initialize every new folder with the installed skill-creator initializer, then complete this contract:
agent-skills/<category>/<skill-name>/
SKILL.md
agents/openai.yaml
references/ # only when detailed reusable guidance is needed
assets/ or scripts/ # only when the workflow genuinely reuses them
demo/
index.html
PROMPT.md
preview.jpg
input.md # required for workflow skills
expected-output.md # required for workflow skills
Write SKILL.md in imperative form. Keep only name and description in frontmatter. Put all trigger phrases in the description. Keep operational steps, constraints, pitfalls, and validation commands in the body.
Write agents/openai.yaml from the final skill:
- use a human-readable display name;
- keep the short description between 25 and 64 characters;
- make the default prompt explicitly invoke
$skill-name.
5. Write Three Useful Example Prompts
Put these headings in demo/PROMPT.md:
Minimal prompt
Invoke the skill and ask for one clear outcome.
Use $skill-name to add <capability> to <target>.
Recreate the demo
Describe the reference experience, implementation contract, deliverables, and acceptance checks. Specify what must remain local and self-contained.
Remix prompt
Change the subject, content, palette, and composition while preserving the mechanism, accessibility behavior, responsive rules, and performance contract.
Read references/example-packages.md for visual, mixed-source, and workflow examples.
6. Build Proof, Not Decoration
Make every demo original, self-contained, and inspectable:
- demonstrate the core mechanism on the first screen;
- use realistic content instead of labels such as “demo card”;
- add controls only when they expose meaningful states;
- support 390px through 1440px layouts;
- use semantic HTML and visible focus states;
- provide reduced-motion behavior for animated work;
- keep dependencies local and avoid a build step unless the skill requires one;
- use
input.mdandexpected-output.mdfor nonvisual workflows.
Never treat a screenshot as the implementation. The HTML demo must work.
7. Validate the Complete Package
Run validation in proportion to the artifact:
- Run the skill creator's
quick_validate.pyon every new or changed skill. - Run repository demo validation and any targeted syntax checks.
- Open each demo in the permitted browser at desktop and mobile sizes.
- Exercise the primary interaction, keyboard focus, and reduced-motion path.
- Confirm the console is clean.
- Capture a real browser preview at the repository's shared dimensions.
- Rebuild the demo and screenshot galleries, then validate again.
- Scan changed files for secrets, tokens, private paths, and private client data.
Do not claim visual or interaction verification from static file inspection alone.
8. Commit Only the Task
Stage the new skill folders and the gallery files they require. Review git diff --cached --stat and git diff --cached before committing. Leave pre-existing dirty files untouched.
Report:
- the source-to-skill mapping;
- the example prompt and demo paths;
- validation and browser evidence;
- the commit hash;
- unrelated dirty files that remain outside the commit.
Failure Modes
- Page clone: copying the source layout instead of extracting the mechanism.
- Mega-skill: combining independent prompts into one vague skill.
- Micro-fragments: turning every sentence into a skill without reusable behavior.
- Theme lock-in: hard-coding the source's palette, assets, or copy.
- Prompt-only package: omitting a functioning demo and preview.
- Fake proof: claiming interaction verification without exercising it.
- Dirty-tree spillover: staging unrelated modifications or generated files.
Frequently asked questions about Article Prompts to Skills
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