Fill and validate structured Markdown forms with typed fields and export to JSON, YAML, or Markdown.
npx skills add https://github.com/jlevy/markform --skill markform
Please help me install this Agent Skill. Skill: markform Source: https://github.com/jlevy/markform/tree/main/docs/skill Command: npx skills add https://github.com/jlevy/markform --skill markform
Markdown-based form systems enable humans and AI agents to collaborate on data collection by embedding structured forms in Markdown, with typed fields, validation, and seamless export.
Copy a bundled .form.md file, then run markform to inspect, fill, validate, and export the form end-to-end.
High-intent search queries and answers about installing and using this skill.
Structured Markdown forms enforce typed fields and validation constraints directly within the document. You can validate populated .form.md files by running markform validate to ensure all data collection constraints are met before processing.
Yes, AI agents can incrementally fill Markdown forms using patch-based updates. This approach supports role-based workflows, allowing agents to progressively populate typed fields without overwriting existing structured data.
The best way to export Markdown form data is using the markform export command. It converts validated .form.md files into JSON, YAML, or standard Markdown formats for seamless integration into downstream data pipelines.
Yes, YAML frontmatter is required to define the form's name and description metadata. This frontmatter establishes the baseline structure necessary for AI-assisted environments to properly inspect and process the typed fields.
You can inspect a Markdown form by running the markform inspect command. This activation flow step allows you to review the typed fields, constraints, and role-based workflows defined in the .form.md file before population.
Use structured Markdown forms when you need collaborative data collection between humans and AI agents. They provide typed field validation and incremental patch-based updates, making them ideal for complex, guided workflows rather than simple static data entry.
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