Skillpack Creator

Overview

Turn a successful task into a reusable SkillPack. Extract the stable workflow, decide what belongs in a local skill versus pack-level prompts, generate the pack structure, and package it only after the workflow is explicit and repeatable.

Workflow

1. Normalize the source task

Reduce the finished task into a clean execution spec:

• Capture the user goal, concrete deliverable, and the final successful workflow (not the full exploratory transcript).
• List required skills, tools, files, secrets, and environment assumptions.
• Separate deterministic steps from heuristic steps; remove dead ends and debugging noise.
• If the task is still too broad, narrow the scope instead of writing a vague mega-skill. If key success conditions depend on hidden human judgment, mark the pack as a best-effort assistant workflow.

Ask for missing stable facts or infer only the low-risk pieces.

2. Decide what the pack should contain

• Local skill (skills/): reusable procedural knowledge. Keep scripts minimal unless reproducibility depends on exact file generation or repetitive shell steps.
• Scripts (scripts/): repeated shell or file-generation logic where reliability matters.
• References (references/): detailed schemas, API notes, or conventions that should not bloat SKILL.md.
• Prompts (skillpack.json): 1, 3 pack-level starter inputs for the UI, not a DAG or state machine. See references/skillpack-format.md for exact pack semantics.

3. Create the pack specification

Before writing files, define the pack spec. Prefer one local orchestrator skill plus a small number of external skills. Example minimal manifest: