Skillshub anth-migration-deep-dive

install

source · Clone the upstream repo

git clone https://github.com/ComeOnOliver/skillshub

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/jeremylongshore/claude-code-plugins-plus-skills/anth-migration-deep-dive" ~/.claude/skills/comeonoliver-skillshub-anth-migration-deep-dive && rm -rf "$T"

manifest: skills/jeremylongshore/claude-code-plugins-plus-skills/anth-migration-deep-dive/SKILL.md

Anthropic Migration Deep Dive

Overview

Migration strategies for switching to Claude from OpenAI, Google, or other LLM providers, including API mapping, prompt translation, and multi-provider abstraction.

OpenAI to Anthropic API Mapping

OpenAI	Anthropic	Notes
`openai.ChatCompletion.create()`	`anthropic.messages.create()`	Different response shape
`model: "gpt-4"`	`model: "claude-sonnet-4-20250514"`	Different model IDs
`messages: [{role, content}]`	`messages: [{role, content}]`	Same format
`functions` / `tools`	`tools`	Similar but different schema key names
`function_call`	`tool_choice`	Different naming
`response.choices[0].message.content`	`response.content[0].text`	Different access path
`stream: true` → yields chunks	`stream: true` → SSE events	Different event format
System message in `messages[]`	`system` parameter (separate)	Claude separates system prompt
`n` (multiple completions)	Not supported	Use multiple requests
`logprobs`	Not supported	N/A

Side-by-Side Code Comparison

# === OpenAI ===
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are helpful."},
        {"role": "user", "content": "Hello"}
    ],
    max_tokens=1024,
    temperature=0.7
)
text = response.choices[0].message.content

# === Anthropic ===
import anthropic
client = anthropic.Anthropic()
response = client.messages.create(
    model="claude-sonnet-4-20250514",
    system="You are helpful.",           # System prompt is separate
    messages=[
        {"role": "user", "content": "Hello"}
    ],
    max_tokens=1024,                     # Required (not optional)
    temperature=0.7
)
text = response.content[0].text

Tool Use Migration

# OpenAI tools format
openai_tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "parameters": {"type": "object", "properties": {"city": {"type": "string"}}}
    }
}]

# Anthropic tools format — flatter structure
anthropic_tools = [{
    "name": "get_weather",
    "description": "Get weather for a city",  # Required in Anthropic
    "input_schema": {"type": "object", "properties": {"city": {"type": "string"}}}
}]

Multi-Provider Abstraction

from abc import ABC, abstractmethod

class LLMProvider(ABC):
    @abstractmethod
    def complete(self, prompt: str, system: str = "", **kwargs) -> str: ...

class AnthropicProvider(LLMProvider):
    def __init__(self):
        import anthropic
        self.client = anthropic.Anthropic()

    def complete(self, prompt: str, system: str = "", **kwargs) -> str:
        msg = self.client.messages.create(
            model=kwargs.get("model", "claude-sonnet-4-20250514"),
            max_tokens=kwargs.get("max_tokens", 1024),
            system=system,
            messages=[{"role": "user", "content": prompt}]
        )
        return msg.content[0].text

class OpenAIProvider(LLMProvider):
    def __init__(self):
        from openai import OpenAI
        self.client = OpenAI()

    def complete(self, prompt: str, system: str = "", **kwargs) -> str:
        messages = []
        if system:
            messages.append({"role": "system", "content": system})
        messages.append({"role": "user", "content": prompt})
        resp = self.client.chat.completions.create(
            model=kwargs.get("model", "gpt-4"),
            messages=messages,
            max_tokens=kwargs.get("max_tokens", 1024)
        )
        return resp.choices[0].message.content

Migration Checklist

Map model names (GPT-4 → Claude Sonnet, GPT-3.5 → Claude Haiku)
Move system prompts from
```
messages[]
```
to
```
system
```
parameter

Update response access path (

.choices[0].message.content

→

.content[0].text

)

Make
```
max_tokens
```
explicit (required in Anthropic, optional in OpenAI)
Update tool definitions to Anthropic format
Test prompt behavior (Claude may respond differently to same prompts)
Update error handling for Anthropic error types

Resources

Next Steps

For advanced debugging, see

anth-advanced-troubleshooting