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agentic-ai-coding-standard

kumaran-is/claude-code-onboarding/.claude/skills/agentic-ai-coding-standard/SKILL.md

This skill provides coding standards for Python agentic AI services with LangChain/LangGraph. Use when reviewing or writing Python agentic AI code. Covers state management, tool definitions, graph structure, error handling, and observability.

Skill35 starsChanged 4 months ago

What's in it

  1. Agentic AI Coding Standards
  2. Key Rules
  3. Import Ordering
  4. Naming Conventions
  5. File Structure
  6. Reference
  7. Error Handling

Tools it asks for

  • Read
---
name: agentic-ai-coding-standard
description: "This skill provides coding standards for Python agentic AI services with LangChain/LangGraph. Use when reviewing or writing Python agentic AI code. Covers state management, tool definitions, graph structure, error handling, and observability."
allowed-tools: Read
metadata:
  triggers: LangChain, LangGraph, agentic AI, Python AI agent, FastAPI agent, state management, tool functions, guardrails, AI agent coding standard
  related-skills: agentic-ai-dev, code-reviewer, security-reviewer
  domain: backend
  role: specialist
  scope: review
  output-format: report
last-reviewed: "2026-03-15"
---

**Iron Law:** Always consult the agentic-ai-dev skill and its MCP sources before writing agent code; never generate LangGraph/LangChain patterns from memory.

# Agentic AI Coding Standards

Mandatory coding standards for all Python agentic AI services using LangChain, LangGraph, and FastAPI.

## Key Rules

| # | Rule | Standard |
|---|------|----------|
| 1 | State typing | Always `TypedDict`; never `dict[str, Any]` |
| 2 | Message lists | `Annotated[list[BaseMessage], add_messages]` |
| 3 | Loop protection | `iteration_count` in state + max check in routing function |
| 4 | Tool functions | `@tool` + docstring + try/except + return strings |
| 5 | LLM instantiation | Factory function; never inline `ChatAnthropic()` in nodes |
| 6 | Temperature | `0` for factual; `0.7` only for creative tasks |
| 7 | Checkpointing | `PostgresSaver` in production; `MemorySaver` only in tests |
| 8 | Error handling | Log + return error state; never swallow exceptions |
| 9 | Naming | `build_<name>_agent()`, `<verb>_node()`, `<Name>State` |
| 10 | Config | pydantic-settings with fail-fast; no `os.getenv()` with silent defaults |
| 11 | Type hints | `mypy --strict`; `Literal` for routing return types |
| 12 | Async | `async def` for all I/O; `ainvoke`/`astream` in API routes |
| 13 | Logging | structlog with `agent_name`, `thread_id`, `node_name` context |
| 14 | Secrets | Never log API keys; redact PII before logging |
| 15 | Testing | Basic invoke + tool usage + iteration limit + error recovery |
| 16 | Cost | Track tokens; configure budget caps; use cheapest viable model |
| 17 | Imports | Group: stdlib → third-party → langchain/langgraph → local |

## Import Ordering

```python
# 1. Standard library
from __future__ import annotations
import json
from typing import Annotated, Literal

# 2. Third-party
from fastapi import APIRouter, Depends
from pydantic import BaseModel, Field

# 3. LangChain / LangGraph
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from langchain_core.tools import tool
from langgraph.graph import END, StateGraph
from langgraph.graph.message import add_messages

# 4. Local
from ..core.config import settings
from ..core.logging import get_logger
```

## Naming Conventions

| Element | Pattern | Example |
|---------|---------|---------|
| State | `<Name>State` | `AgentState`, `RAGState`, `MultiAgentState` |
| Graph builder | `build_<name>_agent()` | `build_react_agent()`, `build_rag_agent()` |
| Node function | `<verb>_node()` | `agent_node()`, `retrieve_node()`, `grade_node()` |
| Tool function | `<verb>_<noun>()` | `search_web()`, `query_database()`, `calculate_cost()` |
| Provider factory | `LLMProviderFactory` | Singleton, injected via `Depends()` |
| Config | `Settings` | pydantic-settings, singleton `settings` instance |
| Exception | `<Name>Error` | `AgentError`, `ToolError`, `LLMProviderError` |

## File Structure

```
src/<service>/
├── agents/
│   ├── graphs/          # build_*_agent() functions
│   ├── nodes/           # *_node() functions
│   ├── tools/           # @tool functions
│   └── state.py         # TypedDict state schemas
├── rag/                 # RAG-specific code
├── memory/              # Checkpointing + semantic memory
├── guardrails/          # Input/output validation
├── llm/providers.py     # LLM factory
├── core/
│   ├── config.py        # pydantic-settings
│   ├── logging.py       # structlog setup
│   └── exceptions.py    # Exception hierarchy
├── observability/       # Metrics + tracing
├── models/schemas.py    # Pydantic request/response
├── api/routes/          # FastAPI routes
└── main.py              # FastAPI app + lifespan
```

## Reference

For concrete code examples and anti-patterns, Read [reference/agentic-standards-examples.md](reference/agentic-standards-examples.md).

## Error Handling

**Import errors**: Verify LangChain/LangGraph package versions match `pyproject.toml` constraints.

**State type mismatches**: Ensure all graph state fields use `TypedDict` with proper `Annotated` types — never `dict[str, Any]`.

**Graph recursion errors**: Check `recursion_limit` in config and verify `iteration_count` is incremented in routing functions.

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