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 G1Joshi/Agent-Skills --skill claudegit clone --depth 1 https://github.com/G1Joshi/Agent-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/g1joshi/agent-skills/claude)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/claude"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/claude/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/g1joshi/agent-skills/claude"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/claude.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00046 | $0.01433 |
| Opus 5.5 | $0.00018 | $0.00573 |
| Sonnet 5.5 | $0.00009 | $0.00287 |
| Haiku 4.5 | $0.00005 | $0.00143 |
Grade A, and why
claude 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 23d 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 — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Claude
Claude (by Anthropic) is OpenAI's main competitor. It is famous for its large context window (200k+), low hallucination rates, and "Artifacts" UI.
When to Use
- Advanced Reasoning & Complex Analysis: Claude 3.5 Sonnet / Opus for intricate coding, mathematics, and logic.
- Prompt Caching on Large Contexts: Slashing latency and cost by up to 90% for repeated documentation, codebases, and books.
- Reliable Tool & Function Calling: Multi-turn agentic workflows invoking APIs and external tools with strict schema adherence.
- Vision & Document Intelligence: Transcribing, analyzing, and extracting data from charts, screenshots, and PDFs.
Quick Start
import anthropic
client = anthropic.Anthropic()
message = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
system="You are an expert software architect.",
messages=[
{"role": "user", "content": "Explain event sourcing in 2 sentences."}
]
)
print(message.content[0].text)
Core Concepts
Tool Use (Function Calling) with Anthropic SDK
Exposing structured tools to Claude for agentic execution:
import os
from anthropic import Anthropic
client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))
tools = [
{
"name": "lookup_user_balance",
"description": "Retrieve current account balance and active currency for a customer.",
"input_schema": {
"type": "object",
"properties": {
"customer_id": {"type": "string", "description": "The unique customer UUID"},
},
"required": ["customer_id"],
},
}
]
response = client.messages.create(
model="claude-3-5-sonnet-20241022",
max_tokens=1024,
tools=tools,
messages=[
{"role": "user", "content": "What is the balance for customer cust_98234?"}
]
)
# Inspect tool use request
for content in response.content:
if content.type == "tool_use":
print(f"Tool Requested: {content.name}")
print(f"Arguments: {content.input}")
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.
- 23d ago Changed · +5 lines 00a568882c8b
- 27d ago Changed · +136 lines · +28 tokens per session 7f30fe4ebc1a
- 1mo ago First seen · 47 lines · 18 tokens per session scan A f8ec51995ae8
claude is a skill published in the GitHub repository G1Joshi/Agent-Skills (15 stars, last pushed 24d ago), licensed MIT. It adds 46 tokens to every session and 1,433 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
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Expert guide for scaling test-time compute, dynamic reasoning token allocation (Gemini 4 Pro, Claude 5.5, GPT Astra 6), Monte Carlo Tree Search (MCTS), and Process Reward Models / Panduan ahli penskalaan komputasi waktu uji (test-time compute), alokasi dinamis budget penalaran, MCTS, dan Process Reward Models.
ai-prompt-engineering-expert
Expert guide for Prompt Engineering, Chain-of-Thought, few-shot prompting, structured output, prompt injection defense, and automated AI evaluations & regression benchmarking (Promptfoo, DeepEval) / Panduan ahli rekayasa prompt dan evaluasi otomatis AI.
kv-cache-prefix-optimizer
Expert guide for deterministic prompt prefix engineering, KV-cache locking for Gemini 4 Pro / Claude 5.5 / GPT Astra 6, canonical tool sorting, and achieving 90%+ prompt cache hit rates / Panduan ahli rekayasa prefix prompt deterministik, penguncian KV-cache untuk Gemini 4 Pro / Claude 5.5 / GPT Astra 6, pengurutan…
hallucination-prevention
Detects and mitigates LLM hallucinations in production pipelines. Validates AI-generated facts, code, and decisions before they reach end users or downstream systems.
rag-and-memory
Patterns for Retrieval-Augmented Generation (RAG) and agent memory systems. Retrieves only relevant context, prevents context bloat, and maintains coherent state across sessions.
ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.