Author and validate prompts as code with YAML front matter and Markdown sections.
npx skills add https://github.com/PredictabilityAtScale/promptopskit --skill promptopskit
Please help me install this Agent Skill. Skill: promptopskit Source: https://github.com/PredictabilityAtScale/promptopskit/tree/main Command: npx skills add https://github.com/PredictabilityAtScale/promptopskit --skill promptopskit
This Skill provides structured guidance for creating and editing promptopskit prompts, including how to define defaults, manage variables, and enforce validation-safe templates.
Write a new .md prompt with front matter and system/prompt sections, then run the validator to ensure it conforms to the PromptOpsKit schema.
High-intent search queries and answers about installing and using this skill.
To validate prompt files for deterministic rendering, you write a Markdown file with YAML front matter and system/prompt sections, then run the validator to ensure it conforms to the required schema and regex validation rules.
Managing prompt defaults and environment overrides involves defining base variables in YAML front matter and applying folder defaults and tier overrides to ensure consistent, context-hardened prompt behavior across environments.
Yes, you can use markdown and YAML front matter to structure prompts as code by defining required sections, structured inputs, and validation-safe templates that enforce consistent prompt engineering workflows.
Scaffolding prompts for CI deployment requires creating structured markdown files with front matter and system sections, validating them against the schema, and applying includes to ensure safe, reliable prompt generation.
Prompt validation fails when using includes and structured inputs if the markdown file lacks required front matter, violates the regex validation rules, or contains undefined variables that break the deterministic rendering schema.
Yes, PromptOpsKit supports folder defaults and tier overrides for prompt engineering, allowing teams to apply environment-specific configurations and context hardening to ensure safe, reliable prompt rendering across providers.
selfagencyRefine agent prompts while preserving YAML front matter and Markdown structure.
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tizeeFormat JavaScript, TypeScript, Python, JSON, Go, and Rust files with external tools.
AgLyx3Design prompt patterns and evaluation frameworks for LLM-driven workflows.
