Article Prompts To Skills
Meng ToCommunityCreated by Meng To
- Skill
- Meta (authoring, orchestration, planning, memory)
- Agent tooling
- MIT
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a systematic process for extracting reusable capabilities from static content like articles or tutorials. It guides the user through inspecting source material, building an extraction ledger, defining a portable contract, and packaging the result into a standardized AgentSkill structure. The goal is to transform prose-based instructions into functional, demonstrable, and independent agent capabilities while removing source-specific branding or layout constraints.
What it does
- Analyzes source articles to identify reusable behaviors and mechanisms.
- Creates an extraction ledger to map source prompts to independent skills.
- Defines portable contracts including state transitions, data models, and performance constraints.
- Packages skills with standardized documentation, demo files, and validation scripts.
- Ensures skills are decoupled from original branding, assets, and page layouts.
- Provides a framework for creating minimal, recreation, and remix-style usage prompts.
When to use it
- When you need to turn tutorial sections or design patterns into reusable agent capabilities.
- When converting a collection of prompts into a structured, maintainable skill library.
- When you want to isolate a specific interaction or workflow from a larger article.
- When you need to ensure new skills meet repository standards for documentation and validation.
Advantages
- Promotes modularity by creating one skill per independent capability.
- Ensures portability by stripping away source-specific branding and hard-coded assets.
- Standardizes the development process for new agent capabilities.
- Includes built-in validation steps to ensure the resulting skill is functional.
- Facilitates the creation of diverse usage examples for better skill adoption.
Usage examples
- “Use article-prompts-to-skills to extract the interactive card pattern from the provided tutorial.”
- The agent will analyze the tutorial, identify the card mechanism, and generate a new skill folder containing the necessary documentation and demo files.
- “Use article-prompts-to-skills to convert this prompt pack into individual, reusable AgentSkills.”
- The agent will create an extraction ledger and package each distinct capability into its own standardized skill directory.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/mengto/article-prompts-to-skills/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip article-prompts-to-skills.zip -d .agents/skills
Documentation by Meng To
Article 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 skillsWrite 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.
Files
- ATTRIBUTION.mdAdded by AutoGineer867 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md6.3 KiB
- agents/openai.yaml301 B
- demo/PROMPT.md1.9 KiB
- demo/expected-output.md1.9 KiB
- demo/index.html18.3 KiB
- demo/input.md1.3 KiB
- demo/preview.jpg142.6 KiB
- references/example-packages.md3.5 KiB
Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- Meng To
- Publisher
- Meng To (Community)
- Source
- MengTo/Skills · agent-skills/codex/article-prompts-to-skills · commit 5ea1661
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Meng To or Meng To.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download article-prompts-to-skills.zip