ReddRadar
Agent skill
claude-api-patterns
Install this agent skill to your Project
npx add-skill https://github.com/pyramidheadshark/claude-scaffold/tree/main/.claude/skills/claude-api-patterns
SKILL.md
Claude API Patterns
When to Load This Skill
Load when working with: Anthropic SDK, anthropic package, Claude API, tool use, streaming responses, message batches, MessageCreate, @anthropic-ai/sdk.
SDK Setup
import anthropic
client = anthropic.Anthropic(api_key=settings.ANTHROPIC_API_KEY)
Never hardcode the API key. Always use environment variables validated at startup.
Basic Message
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[{"role": "user", "content": prompt}],
)
return message.content[0].text
System Prompt
message = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=2048,
system=system_prompt,
messages=[{"role": "user", "content": user_message}],
)
Keep system prompts in separate .txt or .md files, not inline strings. Version them.
Streaming
with client.messages.stream(
model="claude-sonnet-4-6",
max_tokens=1024,
messages=[{"role": "user", "content": prompt}],
) as stream:
for text in stream.text_stream:
yield text
Use streaming for: long responses, real-time UX, progress indication.
Tool Use (Function Calling)
from pydantic import BaseModel
class SearchInput(BaseModel):
query: str
max_results: int = 10
tools = [{
"name": "search",
"description": "Search for information",
"input_schema": SearchInput.model_json_schema(),
}]
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=1024,
tools=tools,
messages=[{"role": "user", "content": "Find recent papers on RAG"}],
)
Always define tool schemas with Pydantic — never write raw JSON schemas by hand.
Tool Result Loop
messages = [{"role": "user", "content": user_message}]
while True:
response = client.messages.create(
model="claude-sonnet-4-6",
max_tokens=4096,
tools=tools,
messages=messages,
)
if response.stop_reason == "end_turn":
break
if response.stop_reason == "tool_use":
tool_results = []
for block in response.content:
if block.type == "tool_use":
result = dispatch_tool(block.name, block.input)
tool_results.append({
"type": "tool_result",
"tool_use_id": block.id,
"content": result,
})
messages.append({"role": "assistant", "content": response.content})
messages.append({"role": "user", "content": tool_results})
Observability
Log every call:
- model, max_tokens, stop_reason
- input_tokens, output_tokens (from
response.usage) - latency (wall time)
- cost estimate (use token counts × price per token)
import time
start = time.monotonic()
response = client.messages.create(...)
latency_ms = (time.monotonic() - start) * 1000
logger.info("claude_call", extra={
"model": response.model,
"input_tokens": response.usage.input_tokens,
"output_tokens": response.usage.output_tokens,
"stop_reason": response.stop_reason,
"latency_ms": round(latency_ms, 1),
})
Rate Limits and Retry
import anthropic
client = anthropic.Anthropic(
max_retries=3,
)
The SDK retries on 429 and 529 by default. For production, add exponential backoff via httpx transport or a wrapper. Never swallow anthropic.APIError silently.
Model Selection
| Task | Model |
|---|---|
| Code, architecture, tests | claude-sonnet-4-6 |
| Complex reasoning, long docs | claude-opus-4-6 |
| Fast classification, routing | claude-haiku-4-5-20251001 |
Routing must be explicit in code — never auto-escalate based on vague heuristics.
See Also
resources/streaming-patterns.md— advanced streaming with SSE + FastAPIresources/eval-design.md— building evals for prompt changes
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