Skillpack Creator Agent Skill

Create a reusable SkillPack from a successful completed task. Use when the user wants to convert a one-off research, coding, analysis, or content workflow into a distributable local SkillPack with `skillpack.json`, local skills under `skills/`, starter prompts, start scripts, and an optional zip package.

3k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

1129

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/CreminiAI/skillpack --skill skillpack-creator

The instruction itself

10 sections, as written by the author

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:

{
  "name": "company-research",
  "description": "Research a company and produce a summary report",
  "version": "1.0.0",
  "prompts": ["Research {company} and create a report with financials and competitors"],
  "skills": [
    { "name": "research-orchestrator", "source": "./skills/research-orchestrator", "description": "Orchestrate company research across multiple sources" }
  ]
}

4. Create the local orchestrator skill

Create skills/<skill-name>/SKILL.md with frontmatter and imperative workflow instructions:

---
name: research-orchestrator
description: "Orchestrate multi-source company research. Use when the user wants a structured company report covering financials, competitors, and market position."
---

Write the stable workflow as imperative steps in the body.
Add scripts/ only for fragile or repeated operations; add references/ only for detailed information.

5. Materialize the pack

Use scripts/scaffold_skillpack.py when you have the pack spec:

# Basic
python3 skills/skillpack-creator/scripts/scaffold_skillpack.py \
  --manifest /tmp/skillpack.json \
  --output /absolute/path/to/output-pack

# With zip
python3 skills/skillpack-creator/scripts/scaffold_skillpack.py \
  --manifest /tmp/skillpack.json \
  --output /absolute/path/to/output-pack \
  --zip

The script validates the manifest, writes skillpack.json, creates skills/, copies start.sh/start.bat from templates/, and optionally runs npx -y @cremini/skillpack zip.

6. Validate the result

Before handing the pack back, confirm:

The manifest matches the intended pack scope
Every declared skill has a valid name, source, and description
Local skills are present under the target pack's skills/
Starter prompts are concrete enough to reproduce the workflow
Zip only after the pack runs as a directory

Output Standard

Produce:

A short summary of the stabilized workflow.
The target pack structure and skill inventory.
The created or updated local skill files.
The generated skillpack.json.
Whether the pack was zipped and where the zip lives.

Other skills for the same job

different authors, same section of the catalogue

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Multi-agent research skill for parallel research execution (10 agents, battle-tested with real case studies).

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Internal guidance for composing Codex and GPT-5.4 prompts for coding, review, diagnosis, and research tasks inside the Codex Claude Code plugin

Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

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Harness Engineering

by muratcankoylan

This skill should be used when designing autonomous agent harnesses: research loops, evaluation scaffolds, locked and editable surfaces, durable logs, novelty gates, pruning, rollback, PR preparation, and human approval boundaries.

3k tokens

How to use it

Copy the folder

Take creminiai/skillpack-creator from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference npx. Without those the skill loads but fails at the first command.