claude-api-helper
Skill106 starsChanged 7 months agoMITView on GitHub
Build applications with the Claude API and Anthropic SDKs. Covers Messages API, streaming, tool use, vision, and best practices.
In this repository13 other files it gives its agents.
- dashboard/AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- dashboard/CLAUDE.mdCLAUDE.md
- business-news-monitorSkill
- commsSkill
- cron-managementSkill
- idea-groomingSkill
- mcp-integrationSkill
- multi-perspective-grillingSkill
- prompt-engineeringSkill
- Task SystemSkill
- tool-use-patternsSkill
- web-researchSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 70
Use system prompts for consistent behavior
- Distinctive: line 73
Use streaming for long responses
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Claude API Helper
Reference skill for building with the Claude API (Messages API, Python/TypeScript SDKs).
Quick Start
import anthropic
client = anthropic.Anthropic()
message = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": "Hello, Claude"}]
)
print(message.content[0].text)Key Patterns
Streaming
with client.messages.stream(
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[{"role": "user", "content": "Write a story"}]
) as stream:
for text in stream.text_stream:
print(text, end="", flush=True)Tool Use
tools = [{
"name": "get_weather",
"description": "Get current weather for a location",
"input_schema": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "City name"}
},
"required": ["location"]
}
}]Vision
message = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
messages=[{
"role": "user",
"content": [
{"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": base64_data}},
{"type": "text", "text": "What's in this image?"}
]
}]
)Best Practices
- Use system prompts for consistent behavior
- Set appropriate max_tokens (don't over-allocate)
- Handle rate limits with exponential backoff
- Use streaming for long responses
- Cache system prompts with prompt caching for cost savings
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