skillify

name	skillify
description	Convert a freshly completed task or described workflow into a reusable skill. Use when the user invokes $skillify, asks to create a skill from the current process, wants generic repeatable steps captured, or requests a skill with optional name/destination clauses such as "$skillify as Spec to English in ~/.agent_skills" or "$skillify in projectname".

Skillify

Use this skill to turn a completed or described workflow into a reusable skill folder. The default output location is adjacent to this skillify skill. If the user provides as <NAME> or in <DIRECTORY>, infer the intended name or destination from natural language and honor it.

Workflow

1. Infer the skill name and destination from the user's request.
2. If there is no explicit name, infer a concise hyphen-case name from the workflow. Prefer action-oriented names under 64 characters.
3. If there is no explicit destination, create the new skill adjacent to this skillify skill; use the parent directory of the loaded skillify/SKILL.md.
4. Identify concrete examples from the just-completed task or the user's description. If the source workflow is ambiguous, ask one concise clarifying question.
5. Decompose the workflow into generic, repeatable steps.
6. Classify each step:
   • LLM-suited: translation, judgment, synthesis, naming, prioritization, visual QA, or context-specific choices.
   • Script-suited: deterministic parsing, extraction, rendering, file transforms, validation, API calls with stable parameters, and repetitive boilerplate.
7. Create the destination skill folder. Prefer the system skill-creator initializer when available. If it is not available or is blocked, use:

python <skill-dir>/scripts/create_skill_skeleton.py <skill-name> --path <destination> --resources scripts

8. Author scripts for the script-suited steps first. Keep scripts parameterized, deterministic, and runnable without loading large context.
9. Write SKILL.md with concise frontmatter and body instructions. Include when to invoke each script, where LLM judgment is required, and validation steps.
10. Add agents/openai.yaml with display_name, short_description, and a default_prompt that explicitly mentions $<skill-name>.
11. Run script smoke tests and validate the skill:

python <skill-creator-dir>/scripts/quick_validate.py <destination>/<skill-name>

If quick_validate.py is unavailable, at minimum verify SKILL.md has valid YAML frontmatter with name and description, and the folder name matches the skill name.

Destination Rules

• No in clause: create the skill adjacent to this skillify skill, meaning in the same parent directory as the loaded skillify folder.
• Absolute or ~ path: use that directory.
• Relative path containing /: resolve from the current working directory.
• Bare names such as projectname: infer the intended skill directory from the user's active workspace and known local conventions. Do not assume a fixed folder name such as agent_skills; inspect or ask if the destination is ambiguous.

Quality Bar

• Do not encode one-off facts as universal instructions.
• Do not put generated reports, README files, changelogs, or process notes in the skill.
• Keep the skill lean; move only reusable deterministic code into scripts/.
• Test every new script with representative inputs before finishing.