create-decision-pack-programmatically

name	Create decision-pack Programmatically
description	How to create a dlab decision-pack directory using generate_dpack() from Python code

Creating a decision-pack Programmatically

Reference: dlab/create_dpack.py

What Is a decision-pack

A decision-pack is a directory that defines everything needed to run an agent in a Docker container:

my-dpack/
  config.yaml              # Name, model, hooks
  .env.example             # Required API keys
  .gitignore               # Excludes .env
  docker/
    Dockerfile             # Container setup
    requirements.txt       # Dependencies (or environment.yml / pixi.toml)
    modal_app/             # (optional) Modal serverless compute
  opencode/
    opencode.json          # Permissions and default agent
    agents/
      orchestrator.md      # Main agent system prompt
      example-worker.md    # (optional) Subagent
    tools/                 # (optional) Custom TypeScript tools
    skills/                # (optional) Knowledge files
    parallel_agents/       # (optional) Parallel agent configs

generate_dpack()

from pathlib import Path
from dlab.create_dpack import generate_dpack

dpack_path = generate_dpack(
    output_dir=Path("."),
    config={
        # Required
        "name": "my-dpack",

        # Optional (shown with defaults)
        "description": "dlab decision-pack: my-dpack",
        "docker_image_name": "dlab-my-dpack",
        "default_model": "opencode/big-pickle",
        "requires_data": True,
        "requires_prompt": True,
        "cli_name": "",                 # Override command name for install (default: name)
        "package_manager": "pip",       # pip | conda | uv | pixi
        "base_image": "python:3.11-slim",
        "agent_name": "orchestrator",
        "agent_description": "Main orchestrator for my-dpack",

        # Skeletons — which directories to scaffold
        "skeletons": {
            "skills": True,             # opencode/skills/ with example
            "tools": True,              # opencode/tools/ with example-tool.ts
            "subagents": True,          # opencode/agents/example-worker.md
            "parallel_agents": True,    # opencode/parallel_agents/ + parallel tool
        },

        # Permissions — written to opencode.json
        "permissions": {
            "bash": "allow",
            "edit": "allow",
            "webfetch": "allow",
            "websearch": "allow",
            "external_directory": "allow",
            "task": "allow",
            "skill": "allow",
            "codesearch": "allow",
            "lsp": "deny",
            "todoread": "allow",
            "todowrite": "allow",
        },

        # Optional features
        "python_lib": False,
        "python_lib_name": "",          # e.g. "my_dpack_lib"
        "modal_integration": False,
        "selected_skills": [],          # List of {"org_slug": "...", "skill_name": "..."}

        # Overwrite existing directory
        "overwrite_existing": False,
    },
    on_progress=print,  # Optional callback for progress messages
)

Config Keys Detail

config.yaml

Generated with these keys:

• name, description, docker_image_name, default_model, requires_data, requires_prompt
• hooks section: active pre-run: deploy_modal.sh when modal enabled, commented template otherwise

Package Managers

Manager	Base Image	Env File	Notes
pip	python:3.11-slim	requirements.txt	Simplest
conda	continuumio/miniconda3:latest	environment.yml	Scientific Python
uv	python:3.11-slim	requirements.txt	Fast pip replacement
pixi	debian:bookworm-slim	pixi.toml	Modern conda-forge

When modal_integration=True, modal is automatically added to the env file.

Agent .md Frontmatter (tools section)

The tools section in orchestrator.md depends on skeleton selections:

Skeletons	Tools Block
parallel_agents=True (with or without subagents)	parallel-agents: true only
subagents=True only	read: true, edit: true, bash: true, task: true
Neither	read: true (placeholder)

Permissions

Defined in CONFIGURABLE_PERMISSIONS (list of tuples). First 6 are high-impact: webfetch, websearch, bash, edit, external_directory, task

Remaining are internal: skill, codesearch, lsp (default deny), todoread, todowrite

Hardcoded (always set): read, glob, grep, list = allow; question = deny

Custom Tools (TypeScript)

Custom tools in opencode/tools/ MUST use execute, not run:

import { tool } from "@opencode-ai/plugin"

export default tool({
  description: "What this tool does",
  args: {
    input: tool.schema.string().describe("Input description"),
  },
  async execute(args) {          // MUST be "execute", NOT "run"
    // Use Bun shell for CLI commands (Python, bash, etc.)
    const result = await Bun.$`python -c "print('hello')"`.nothrow()
    const stdout = result.stdout.toString()
    const stderr = result.stderr.toString()

    if (result.exitCode !== 0) {
      return `ERROR (exit code ${result.exitCode}):\n${stderr}`
    }
    return stdout.trim()
  },
})

CRITICAL rules for custom tools:

• MUST use execute, not run — OpenCode calls def.execute(args, ctx) internally. Using run causes def.execute is not a function at runtime.
• **Use Bun.$\...`for CLI commands** — tools run inside OpenCode's Bun runtime. Use.nothrow()` to handle non-zero exit codes gracefully.
• Always check result.exitCode — return errors as strings so the agent can diagnose issues.

Modal Integration

When modal_integration=True, generates:

• docker/modal_app/__init__.py + example.py with hash-based cache busting
• deploy_modal.sh pre-run hook (respects DLAB_RUN_MODAL_TOOL_LOCALLY env var — skips deploy when set to 1)
• opencode/tools/run-on-modal.ts (if tools skeleton enabled) — uses modal.Function.from_name("{name}-compute", "run_compute")
• modal added to env file
• MODAL_TOKEN_ID + MODAL_TOKEN_SECRET in .env.example

The generated deploy_modal.sh hook checks DLAB_RUN_MODAL_TOOL_LOCALLY (default: 1 = local). Set to 0 in the .env file to enable Modal cloud execution. The hook also checks for Modal tokens and skips deployment if they're missing. decision-packs can rename this variable to something domain-specific (e.g., the MMM dpack uses DLAB_FIT_MODEL_LOCALLY).

Note: All environment variables starting with DLAB_ are automatically forwarded from the host to the Docker container by the dlab CLI. decision-packs can define their own DLAB_* variables for configuration without any framework changes.

.env.example

Auto-generated from the selected model's provider. Uses get_provider_env_vars(model_id) which checks:

1. Cached provider env vars from models.dev API
2. Fallback KNOWN_PROVIDER_ENVS dict (anthropic, openai, opencode, google, deepseek, etc.)

Skills from Decision Hub

Pass selected_skills as a list of dicts with org_slug and skill_name. These are downloaded via the Decision Hub API and extracted into opencode/skills/.

Creation Process

1. Validates decision-pack name (alphanumeric, hyphens, underscores)
2. Creates everything in a temp directory (tempfile.TemporaryDirectory)
3. On success: atomically moves to final location (with overwrite support)
4. On failure: temp dir is auto-cleaned

Helper Functions

from dlab.create_dpack import (
    validate_dpack_name,    # Returns error string or None
    filter_models,           # Case-insensitive substring filter
    get_model_list,          # KNOWN_MODELS + cached API models
    get_provider_env_vars,   # Env vars needed for a model's provider
    fetch_models_from_api,   # Fetch from models.dev (network call)
    ask_skills,              # Natural-language skill search via Decision Hub
)