data-substrate-analysis
Analyze fundamental data primitives, type systems, and state management patterns in a codebase. Use when (1) evaluating typing strategies (Pydantic vs TypedDict vs loose dicts), (2) assessing immutability and mutation patterns, (3) understanding serialization approaches, (4) documenting state shape and lifecycle, or (5) comparing data modeling approaches across frameworks.
安装与使用
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。
查看来源 ↗使用指南技能说明
Data Substrate Analysis
Analyzes the fundamental units of data and state management patterns.
Process
- Locate type files — Find types.py, schema.py, models.py, state.py
- Classify typing — Strict (Pydantic), structural (TypedDict), loose (dict)
- Analyze mutation — In-place modification vs. copy-on-write
- Document serialization — json(), dict(), pickle, custom methods
Typing Strategy Classification
Detection Patterns
| Strategy | Indicators | Files to Check |
|---|---|---|
| Pydantic | BaseModel, Field(), validator |
models.py, schema.py |
| Dataclass | @dataclass, field() |
types.py, models.py |
| TypedDict | TypedDict, Required[], NotRequired[] |
types.py |
| NamedTuple | NamedTuple, typing.NamedTuple |
types.py |
| Loose | Dict[str, Any], plain dict |
Throughout |
Analysis Questions
- Are boundaries validated (API ingress/egress)?
- Is nesting depth reasonable (<3 levels)?
- Are optional fields explicit or implicit None?
- Version migration path (Pydantic V1 → V2)?
Immutability Analysis
Mutable Patterns (Risk Indicators)
# In-place list modification
state.messages.append(msg)
state.history.extend(new_items)
# Direct dict mutation
state['key'] = value
state.update(new_data)
# Object attribute mutation
state.status = 'complete'
Immutable Patterns (Safer)
# Pydantic copy
new_state = state.model_copy(update={'key': value})
# Dataclass replace
new_state = replace(state, messages=[*state.messages, msg])
# Spread operator style
new_state = {**state, 'key': value}
# Frozen dataclass
@dataclass(frozen=True)
class State: ...
Serialization Strategy
Common Patterns
| Method | Code Pattern | Trade-offs |
|---|---|---|
| Pydantic JSON | .model_dump_json() |
Type-safe, automatic |
| Pydantic Dict | .model_dump() |
For internal use |
| Dataclass | asdict(obj) |
Manual, no validation |
| Custom | to_dict(), from_dict() |
Full control |
| Pickle | pickle.dumps() |
Fast, fragile, security risk |
| JSON | json.dumps(obj, default=...) |
Requires encoder |
Questions to Answer
- Is serialization implicit (automatic) or explicit (manual)?
- How are nested objects handled?
- Is deserialization validated?
- What happens with unknown fields?
Output Template
## Data Substrate Analysis: [Framework Name]
### Typing Strategy
- **Primary Approach**: [Pydantic/Dataclass/TypedDict/Loose]
- **Key Files**: [List of files]
- **Nesting Depth**: [Shallow/Medium/Deep]
- **Validation**: [At boundaries/Everywhere/None]
### Core Primitives
| Type | Location | Purpose | Mutability |
|------|----------|---------|------------|
| Message | schema.py:L15 | Chat message | Immutable |
| State | state.py:L42 | Agent state | Mutable ⚠️ |
| Result | types.py:L78 | Tool output | Immutable |
### Mutation Analysis
- **Pattern**: [In-place/Copy-on-write/Mixed]
- **Risk Areas**: [List of mutable state locations]
- **Concurrency Safe**: [Yes/No/Partial]
### Serialization
- **Method**: [Pydantic/Custom/JSON]
- **Implicit/Explicit**: [Description]
- **Round-trip Tested**: [Yes/No/Unknown]
Integration
-
Prerequisite:
codebase-mappingto identify type files -
Feeds into:
comparative-matrixfor typing decisions -
Related:
resilience-analysisfor error handling in serialization
使用示例
查看来源仓库中的使用示例与配置说明。
查看来源 ↗来源历史
- 收录时间
- 2026-01-20
- 索引更新时间
- 2026-01-28
- Branch
- main
收录与同步日期属于本站记录。源码修改历史请查看来源仓库。
GitHub ↗chat Comments (0)
Sign in to join the discussion and leave a comment.
No comments yet. Be the first to share your thoughts!