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Create decision-pack Programmatically

Generate standardized decision-pack directories with skeletons and permissions via Python API.

Updated Mar 19, 2026
Search Tags:#python#permissions#scaffold#skeletons#decision-pack#dlab#generate_dpack
One-click install
npx skills add https://github.com/benmaier/decision-agent-placeholder --skill create-decision-pack-programmatically
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Create decision-pack Programmatically
Source: https://github.com/benmaier/decision-agent-placeholder/tree/main/.claude/skills/create-dpack
Command: npx skills add https://github.com/benmaier/decision-agent-placeholder --skill create-decision-pack-programmatically

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Generate complete, reproducible decision-packs for autonomous agents by programmatically scaffolding all required files and directories from Python code.

Core Features & Use Cases

  • Create a fully configured decision-pack skeleton including config.yaml, docker/, opencode/ with orchestrator.md, and permissions.
  • Customize skeletons (skills, tools, subagents, parallel_agents) and optional features (modal integration, python_lib) via a Python API.
  • Use cases include rapid prototyping of agents, reproducible environments for testing, and scalable deployment pipelines.

Quick Start

Call generate_dpack with a config dictionary to scaffold a complete decision-pack directory.

Frequently Asked Questions about Create decision-pack Programmatically

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

FAQPage Schema
How do I programmatically scaffold a decision-pack directory using Python?▼

You can programmatically scaffold a decision-pack by calling the generate_dpack function with a config dictionary to create a complete directory skeleton including config.yaml, docker/, and opencode/ files.

What files are included when generating a decision-pack skeleton?▼

Generating a decision-pack skeleton creates config.yaml, a docker/ directory, an opencode/ directory with orchestrator.md, and configurable permissions for reproducible agent environments.

Can I customize skeletons and permissions when creating a decision-pack?▼

Yes, you can customize skeletons such as skills, tools, subagents, and parallel_agents, alongside permissions and optional features like modal integration and python_lib via a Python API.

What is the best way to create reproducible environments for autonomous agent testing?▼

The best way to create reproducible environments is programmatically generating decision-packs from Python code, ensuring standardized configurations and Docker-related files for scalable deployment pipelines.

Does decision-pack generation support optional modal integration?▼

Yes, decision-pack generation supports optional modal integration, allowing you to include modal features in the parameterized workflow when scaffolding the directory structure.

When do I need to programmatically scaffold decision-packs instead of manually creating them?▼

You need to programmatically scaffold decision-packs when building standardized configurations for rapid prototyping of agents, requiring reproducible environments and scalable deployment pipelines.

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