Kitfile Format for ModelKits

The Kitfile is the blueprint for every AI project packaged with KitOps.

It’s a simple, YAML-based manifest that defines what goes into a ModelKit: models, datasets, code, prompts, agent skill files, MCP server configurations, and documentation.

ModelKits use Kitfiles to package and version your project so it can be shared, stored, or deployed from any OCI-compatible registry.

Why Use a Kitfile?

A Kitfile helps you:

• Package your AI project with full traceability - whether it’s a model, an agent configuration, or both
• Make your work reproducible across environments
• Collaborate with DevOps, ML engineers, AI engineers, and app developers using a shared format
• Integrate into CI/CD pipelines and registries

Kitfile Sections

Each Kitfile includes one or more of the following sections:

Section	Description	Example contents
package	Project metadata (name, version, license, authors)	-
model	The serialized model and framework info	Model weights, GGUF files
datasets	Training, validation, or other datasets	CSV files, Parquet datasets
code	Code, scripts, and server configurations	Jupyter notebooks, MCP server code and config files
prompts	Prompt files and agent skill files	System prompts, SKILL.md, .cursorrules
docs	Additional documentation	README, usage guides

The prompts section is the natural home for agent skill files and prompt templates. The code section is where MCP server code and configuration files go. You can organize these however fits your project - the key is that everything gets versioned together.

You can extract the Kitfile from any existing ModelKit:

kit unpack [registry/repo:tag] --config -d .

Minimal Kitfile Examples

The only required fields are:

• manifestVersion
• At least one of code, model, datasets, docs, or prompts section

A ModelKit for a dataset:

manifestVersion: v1.0.0

datasets:
  - name: training data
    path: ./data/train.csv
  - name: validation data
    path: ./data/test.csv

A ModelKit for agent skills and prompts (no model required):

manifestVersion: v1.0.0

package:
  name: customer-support-agent
  description: Skill files and prompts for the customer support agent
  version: 2.1.0

prompts:
  - path: ./prompts/system.prompt.md
    description: System prompt for the support agent
  - path: ./skills/escalation.skill.md
    description: Skill for escalating tickets to human agents
  - path: ./skills/refund-processing.skill.md
    description: Skill for processing refund requests

Notes on Kitfile Behavior

• Kitfiles must use relative paths (not absolute)
• ModelKits can include only one model, but multiple datasets, code entries, or prompts
• You can currently only build ModelKits from local files, but support for remote sources (e.g. DVC, S3) is coming soon

Example: Full Kitfile with Model

manifestVersion: v1.0.0

package:
  authors:
    - Jozu
  description: Updated model to analyze flight trait and passenger satisfaction data
  license: Apache-2.0
  name: FlightSatML

code:
  - description: Jupyter notebook with model training code in Python
    path: ./notebooks

model:
  description: Flight satisfaction and trait analysis model using Scikit-learn
  framework: Scikit-learn
  license: Apache-2.0
  name: joblib Model
  path: ./models/scikit_class_model_v2.joblib
  version: 1.0.0

datasets:
  - name: training data
    description: Flight traits and traveller satisfaction training data (tabular)
    path: ./data/train.csv
  - name: validation data
    description: validation data (tabular)
    path: ./data/test.csv

prompts:
  - path: system.prompt.md
    description: System prompt for model inference

Example: Agentic AI Kitfile

This Kitfile packages agent skill files, prompts, and an MCP server configuration together. No model is needed when the agent uses a hosted API (e.g., Claude, GPT).

manifestVersion: v1.0.0

package:
  name: sales-research-agent
  description: Agent configuration for automated sales research pipeline
  authors:
    - Platform Team
  version: 3.0.1

prompts:
  - path: ./prompts/system-prompt.md
    description: Core system prompt defining agent persona and constraints
  - path: ./prompts/research-prompt.md
    description: Prompt template for company research tasks
  - path: ./skills/linkedin-enrichment.skill.md
    description: Skill for enriching leads with LinkedIn data
  - path: ./skills/competitive-analysis.skill.md
    description: Skill for generating competitive analysis summaries

code:
  - path: ./mcp-servers/crm-connector
    description: MCP server for CRM data access
  - path: ./mcp-servers/web-scraper
    description: MCP server for web scraping with rate limiting

docs:
  - path: ./README.md
    description: Agent setup and deployment guide

Learn More

➡️ Explore the Kitfile structure in detail

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