skillify

Creates and upgrades modular SKILL.md skill packages for Claude Code, Codex, and ECC harnesses.

Updated Mar 22, 2026
One-click install
npx skills add https://github.com/diazMelgarejo/orama-system --skill skillify-diazmelgarejo
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: skillify
Source: https://github.com/diazMelgarejo/orama-system/tree/main/bin/orama-system/skills/skillify
Command: npx skills add https://github.com/diazMelgarejo/orama-system --skill skillify-diazmelgarejo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, and includes scripts (resource) and references (resource) components.

What problem does it solve? Authoring agent skills by hand leads to bloated SKILL.md files, missing discovery metadata, namespace collisions with other skill suites, and inconsistent packaging across harnesses like Claude Code, Codex, and Gemini. ## Core Features & Use Cases - Guided Skill Authoring: Interactive intake workflow that gathers name, purpose, triggers, and boundaries, then generates a concise SKILL.md orchestrator with one-level modular references, examples, and eval checklists. - Collision and Clobber Guards: Runs namespace collision checks against shared global skill roots and a clobber guard before any write, preventing overwrites of unrelated same-named skills. - Cross-Harness Packaging: Packages a canonical skill into an Anthropic-schema-compliant .skill zip for claude.ai upload, bundling cross-repo references and trimming frontmatter automatically. - Use Case: Ask to create a new orama-system sub-skill for rotating API keys; skillify interviews you for missing facts, previews the frontmatter, writes SKILL.md plus references and eval files, validates line counts and code fences, and registers it only after your confirmation. ## Quick Start Ask the agent to create a new orama-system skill for your task, specifying the target harness and trigger phrases, then confirm the proposed outline before any files are written.

Frequently Asked Questions about skillify

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a new Claude Code skill with skillify?

Invoke skillify with the skill's purpose, target harness, and trigger phrases. It interviews you for missing facts, previews the frontmatter and outline, then writes a concise SKILL.md plus modular references under bin/orama-system/skills/<name>/ after a clobber guard passes.

What is the difference between skillify, gstack's /skillify, and Anthropic's skill-creator?

This skillify builds canonical orama-system skills for this repo's multi-harness stack. gstack's /skillify only codifies browser scrape flows into deterministic scripts, and Anthropic's skill-creator is the general-purpose official plugin. Workflow step 0 asks you to disambiguate when the request is unclear.

Can I package a skill as a .skill file for claude.ai upload?

Yes. Run scripts/package_skill.py against the canonical skill directory. It stages a copy, bundles cross-repo references, trims frontmatter to Anthropic's packaged schema with description under 1024 characters, validates, and zips to <name>.skill.

Does skillify support Codex and Gemini skill installs?

Yes. Codex installs are thin wrappers containing only name and description frontmatter pointing at the canonical repo card, never copied bodies. Gemini reconciliation uses an ownership manifest with adapter, link, and preserve-external actions plus archive receipts.

Why does skillify refuse to write to ~/.claude/skills directly?

The ~/.claude/skills namespace is shared with external suites like gstack, and a past incident silently overwrote gstack's own same-named skillify file. Publishing there is handled by scripts/install-skills.sh under disambiguated slugs like oramasys-skillify after collision checks.

What are the SKILL.md size limits skillify enforces?

Newly generated SKILL.md files target under 200 lines, and existing or exceptional files must stay under 500 lines. Long procedures, templates, and checklists are offloaded to one-level modular reference files instead of nesting reference chains.