Agent skill

Mas Data Model

by AUTO-MAS-Project in AUTO-MAS-Project/AUTO-MAS

Define backend data modeling standards for Python services. An agent skill from AUTO-MAS-Project/AUTO-MAS.

AGPL-3.0Auto-check passedDatabases

Install Mas Data Model

skills CLI
$ npx skills add AUTO-MAS-Project/AUTO-MAS --skill mas-data-model -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install AUTO-MAS-Project/AUTO-MAS mas-data-model --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/AUTO-MAS-Project/AUTO-MAS.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/mas-data-model .claude/skills/mas-data-model && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
mas-data-model
GitHub stars
708
Token cost
~1.6k tokens
SKILL.md length
829 words
Files
2
Skills in repo
15
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Define backend data modeling standards for Python services. An agent skill from AUTO-MAS-Project/AUTO-MAS.

  • Works in 3 steps: API contract models (schema) → persisted/runtime config models (config,… → task/runtime state models (task)
  • Refactoring models in app/models (schema/config/task)
  • SKILL.md covers Objective, Scope, Model Ownership and Structure And Types, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Mas Data Model is an agent skill from AUTO-MAS-Project/AUTO-MAS. Define backend data modeling standards for Python services. Use when designing or refactoring models in app/models (schema/config/task), normalizing shared fields, choosing types/defaults/validation strategy, and evolving model contracts with backward compatibility.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Databases, covering Database schema design and Refactoring. It works with Python. The repository describes itself as: 多脚本多配置统一管理与自动化工具 | 轻松管理大量脚本并存储多个用户配置、设计自动化任务流、监看脚本日志,大幅提高自动化代理效率与稳定性!. The licence is AGPL-3.0.

When your agent uses it

  • Refactoring models in app/models (schema/config/task)
  • Normalizing shared fields
  • Choosing types/defaults/validation strategy
  • Evolving model contracts with backward compatibility

Example prompts

  • “/mas-data-model”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. API contract models (schema)
  2. persisted/runtime config models (config, ConfigBase)
  3. task/runtime state models (task)

What it can do on your machine

Read from SKILL.md and the folder at commit 699de5a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Mas Data Model loads about 1.6k tokens when it runs. Until then it costs about 70 tokens; SKILL.md has 829 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~70
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from AUTO-MAS-Project/AUTO-MAS at commit 699de5a, republished under its AGPL-3.0 licence (© AUTO-MAS-Project). 829 words, ~1,597 tokens.

Download SKILL.mdSave it as .claude/skills/mas-data-model/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
mas-data-model
description
Define backend data modeling standards for Python services. Use when designing or refactoring models in app/models (schema/config/task), normalizing shared fields, choosing types/defaults/validation strategy, and evolving model contracts with backward compatibility.

MAS Data Model

Objective

Build backend data models that are explicit, consistent, and evolution-friendly.

Scope

Apply to model definitions under app/models:

  1. API contract models (schema)
  2. persisted/runtime config models (config, ConfigBase)
  3. task/runtime state models (task)

Model Ownership

  1. schema models define external API contracts only.
  2. config models define persisted configuration structure and validation behavior.
  3. task models define runtime execution state and orchestration-facing status.
  4. Do not mix transport, persistence, and runtime concerns in one model class.
  5. ConfigBase subclasses define real persisted config templates; every ConfigItem must be declared before super().__init__() to be indexed, settable, and saved.
  6. MultipleConfig represents a dictionary-like collection of ConfigBase instances and must be declared with all allowed concrete config classes.

Structure And Types

  1. Use nested group models for meaningful domains (Info, Run, Notify, Data).
  2. Keep shared semantics aligned with mas-schema-naming.
  3. Keep domain-specific fields inside dedicated domain blocks.
  4. Keep index items and payload models separated.
  5. Prefer concrete types over Any.
  6. Use Literal or explicit enums for bounded value sets.
  7. Use optional types only when missing value has real business meaning.
  8. Avoid stringly-typed booleans/numbers in new model fields.
  9. Keep datetime/time fields format-stable and documented.
  10. When a field is optional by omission, prefer absence over sentinel values that violate the declared type.

Defaults And Validation

  1. Use safe defaults for collection fields (default_factory when mutable).
  2. Use None defaults only for truly optional semantics.
  3. Avoid hidden execution-policy changes in defaults.
  4. Keep validation close to model definition.
  5. Keep correction/normalization deterministic.
  6. Do not encode high-level business workflow in low-level field validators.
  7. Do not duplicate validation already guaranteed by config manager or collection base classes; validate only the invariant still at risk, such as referenced uuid existence.
  8. Favor validators that reject or normalize one missing invariant, not "just in case" fallbacks for impossible states.
  9. Treat config validators as auto-correction behavior, not just passive checks: RangeValidator, OptionsValidator, BoolValidator, path validators, EncryptValidator, VirtualConfigValidator, and MultipleUIDValidator can rewrite stored values.
  10. Use VirtualConfigValidator(function) for computed display/config fields that should be read through normal config access but must not be set or persisted as user input.

Relationships And Sensitive Data

  1. Keep IDs typed consistently as strings in API-facing schema unless migration is planned.
  2. Keep relation fields explicit (scriptId, userId, queueId).
  3. Keep index models lightweight and independent of heavy payload models.
  4. Avoid duplicating relationship semantics with synonym fields.
  5. Mark and isolate sensitive fields clearly (password, token, key).
  6. Avoid exposing sensitive values in response models unless explicitly required.
  7. Keep encryption/decryption policy out of schema contracts and in proper model/service layers.
Show full SKILL.md (387 more words)Show less

Evolution And Compatibility

  1. Prefer additive model changes over breaking removals.
  2. Keep backward read compatibility during rename migrations.
  3. Keep API conversion logic explicit when old/new fields coexist.
  4. Avoid adding a second config field for the same user choice.
  5. If modes are mutually exclusive, merge them into one selector and make downstream toggling explicit.
  6. If modes are not mutually exclusive, keep one existing selector as the source of truth.
  7. Do not introduce future raw-config save fields or detailed-mode markers until persistence, UI, and runtime consumption all exist.
  8. Do not add placeholders for a future config surface when the product still lacks the corresponding edit entry or consumer.

Anti-Patterns

  1. One model serving unrelated responsibilities across layers.
  2. New fields duplicating existing semantics with different names.
  3. Validator logic performing network/file/process side effects.
  4. Unbounded Dict[str, Any] replacing known structured fields.
  5. Large domain policies hidden in model defaults.
  6. Defensive validators re-checking invariants enforced by lower-level config containers.
  7. Separate config options forcing users to choose the same domain concept twice.
  8. Optional fields represented by invalid placeholder values instead of omission or a correct union type.
  9. Defining config fields after super().__init__() and expecting them to participate in normal config load/save behavior.
  10. Adding a config class to one layer while forgetting the corresponding GlobalConfig collection, CLASS_BOOK/registry entry, schema type, or API handling.

Review Checklist

  1. Model belongs to the correct layer (schema/config/task).
  2. Field naming aligns with canonical shared semantics.
  3. Types and optionality express real business meaning.
  4. Defaults are safe and behaviorally stable.
  5. Constraints are explicit and deterministic.
  6. Sensitive fields are protected from accidental exposure.
  7. Change is backward-compatible or includes migration handling.
  8. Placement follows mas-module-boundary and API usage follows mas-api-contract.
  9. New validators check only missing invariants, not guarantees already provided by base containers.
  10. New config fields do not duplicate an existing selector or tab/mode choice.
  11. Optionality is expressed by the type system or field absence, not by values that conflict with the declared type.
  12. Config classes document every ConfigItem with nearby comments and are grouped by Info, Run, Task, Data, Notify, or the local domain grouping used by neighbors.
  13. New multi-config relationships include the allowed classes in MultipleConfig([...]) and any needed UID-reference field points at the owning collection.

© AUTO-MAS-Project, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .agents/skills/mas-data-model of AUTO-MAS-Project/AUTO-MAS.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 699de5a

Compare with similar skills

Mas Data Model next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Add Mpk Taskmirage-project/mirage2.5k—~4.5kAutomated safety check: PassApache-2.0
Effect TSmattiacerutti/supernova187—~2.8kAutomated safety check: PassMIT
SQL Schema Policy Validatorrominirani/antigravity-skills592—~264Automated safety check: PassNone

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Works with

Questions about Mas Data Model

What does Mas Data Model do?

Define backend data modeling standards for Python services. An agent skill from AUTO-MAS-Project/AUTO-MAS. Mas Data Model is an agent skill from AUTO-MAS-Project/AUTO-MAS. Define backend data modeling standards for Python services.

When should I use Mas Data Model?

Mas Data Model fits situations like: refactoring models in app/models (schema/config/task); normalizing shared fields; choosing types/defaults/validation strategy; evolving model contracts with backward compatibility.

How do I install Mas Data Model in Claude Code?

Run `npx skills add AUTO-MAS-Project/AUTO-MAS --skill mas-data-model -a claude-code`. Or copy the skill folder (.agents/skills/mas-data-model in AUTO-MAS-Project/AUTO-MAS) into .claude/skills/mas-data-model in your project. Claude Code loads it when a task matches its description.

How do I install Mas Data Model in Codex?

Run `npx skills add AUTO-MAS-Project/AUTO-MAS --skill mas-data-model -a codex`. Or copy the skill folder (.agents/skills/mas-data-model in AUTO-MAS-Project/AUTO-MAS) into .agents/skills/mas-data-model in your project. Codex loads it when a task matches its description.

Can I use Mas Data Model in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add AUTO-MAS-Project/AUTO-MAS --skill mas-data-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mas-data-model, .gemini/skills/mas-data-model, .github/skills/mas-data-model and .opencode/skills/mas-data-model in your project.

What does Mas Data Model need to run?

SKILL.md names no scripts, command-line tools or credentials: Mas Data Model is instructions for the agent only. Our summary lists: Python 3.

Does Mas Data Model access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Mas Data Model safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Mas Data Model use?

Mas Data Model is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Mas Data Model use?

About 1.6k tokens (SKILL.md is roughly 6.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Mas Data Model?

Skills that share tags, products or a category with Mas Data Model: Content Modeling Best Practices (sanity-io/agent-toolkit, 188 stars), Saleor Django Migration Rules (saleor/saleor, 23k stars), Add Mpk Task (mirage-project/mirage, 2.5k stars) and Effect TS (mattiacerutti/supernova, 187 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Mas Data Model?

AUTO-MAS-Project (a GitHub organization) maintains it in AUTO-MAS-Project/AUTO-MAS, which has 708 GitHub stars. The repository holds 15 skills in this directory. The repository was last updated on October 8, 2026.

Source: AUTO-MAS-Project/AUTO-MAS on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.