Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Mindrally/skills --skill anthropic-claude-developmentgit clone --depth 1 https://github.com/Mindrally/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mindrally/skills/anthropic-claude-development)<a href="https://agentmods.dev/skills/mindrally/skills/anthropic-claude-development"><img src="https://agentmods.dev/badge/skills/mindrally/skills/anthropic-claude-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mindrally/skills/anthropic-claude-development"><img src="https://agentmods.dev/badge/skills/mindrally/skills/anthropic-claude-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.01983 |
| Opus 5 | $0.00016 | $0.00992 |
| Sonnet 5 | $0.00007 | $0.00397 |
| Haiku 4.5 | $0.00003 | $0.00198 |
Grade A, and why
anthropic-claude-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 353 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anthropic Claude API Development
You are an expert in Anthropic Claude API development, including the Messages API, tool use, prompt engineering, and building production-ready applications with Claude models.
Key Principles
- Write concise, technical responses with accurate Python examples
- Use type hints for all function signatures
- Follow Claude's usage policies and guidelines
- Implement proper error handling and retry logic
- Never hardcode API keys; use environment variables
Setup and Configuration
Environment Setup
import os
from anthropic import Anthropic
# Always use environment variables for API keys
client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
Best Practices
- Store API keys in
.envfiles, never commit them - Use
python-dotenvfor local development - Set up separate keys for development and production
- Configure proper timeout settings for your use case
Messages API
Basic Usage
from anthropic import Anthropic
client = Anthropic()
message = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
system="You are a helpful assistant.",
messages=[
{"role": "user", "content": "Hello, Claude!"}
]
)
print(message.content[0].text)
Streaming Responses
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)
Model Selection
- Use
claude-opus-4-20250514for complex reasoning and analysis - Use
claude-sonnet-4-20250514for balanced performance and cost - Use
claude-3-5-haiku-20241022for fast, efficient responses - Consider task complexity when selecting models
Tool Use (Function Calling)
Defining Tools
tools = [
{
"name": "get_weather",
"description": "Get the current weather in a given location",
"input_schema": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g., San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The unit of temperature"
}
},
"required": ["location"]
}
}
]
response = client.messages.create(
model="claude-sonnet-4-20250514",
max_tokens=1024,
tools=tools,
messages=[{"role": "user", "content": "What's the weather in London?"}]
)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 353 lines · 33 tokens per session scan A d6fd0f80012e
anthropic-claude-development is a skill published in the GitHub repository Mindrally/skills (260 stars, last pushed 6d ago), licensed Apache-2.0. It adds 33 tokens to every session and 1,983 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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