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 jnPiyush/AgentX --skill anthropic-claudegit clone --depth 1 https://github.com/jnPiyush/AgentXWrote 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/jnpiyush/agentx/anthropic-claude)<a href="https://agentmods.dev/skills/jnpiyush/agentx/anthropic-claude"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/anthropic-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/jnpiyush/agentx/anthropic-claude"><img src="https://agentmods.dev/badge/skills/jnpiyush/agentx/anthropic-claude.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00067 | $0.02309 |
| Opus 5 | $0.00034 | $0.01154 |
| Sonnet 5 | $0.00013 | $0.00462 |
| Haiku 4.5 | $0.00007 | $0.00231 |
Grade A, and why
anthropic-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 9d 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anthropic Claude
WHEN: Writing implementation code against Anthropic Claude models directly via the Messages API, or via Amazon Bedrock / GCP Vertex AI, or via the Claude Agent SDK.
When to Use
- Calling Claude models directly with the Anthropic Messages API
- Deploying Claude on AWS Bedrock or GCP Vertex AI
- Building tool-using Claude agents with the Claude Agent SDK
- Adding prompt caching, extended thinking, or vision to a Claude app
- Migrating from another LLM provider to Claude and preserving behavior
Decision Tree
Need Claude in production?
+- Direct Anthropic API?
| - Use anthropic SDK (python or typescript)
+- AWS-hosted workload?
| - Use Claude on Amazon Bedrock via boto3 / AWS SDK
+- GCP-hosted workload?
| - Use Claude on Vertex AI via google-cloud-aiplatform
+- Agentic loop with tools, files, shell?
| - Use Claude Agent SDK
+- Framework already chosen (LangChain, MAF, LangGraph)?
- Use the provider adapter rather than raw SDK
Core Rules
- Always send a
systemprompt separately from themessagesarray -- Claude separates system instruction from the turn history. - Pin model IDs explicitly (for example
claude-opus-4.5,claude-opus-4.8,claude-haiku-4.5). Do not rely on aliases in production. - Reserve output tokens deliberately. Claude context is 200K total; treat
max_tokensas a required cost and latency lever. - Use prompt caching for any prompt prefix reused across turns -- system prompt, tool schemas, long docs, few-shot examples.
- Prefer structured tool use over freeform JSON in prose. Let Claude emit tool_use blocks and validate on the server side.
Model Selection (April 2026)
| Model | Best For | Context / Output | Notes |
|---|---|---|---|
claude-opus-4.8 |
Deep reasoning, coding, computer use, complex agents | 200K / 64K | AgentX default Claude model |
claude-opus-4.5 |
Prior high-capability Opus generation | 200K / 64K | Use when pinned deployments require the prior Opus line |
claude-haiku-4.5 |
High-volume, low-latency, simple classification | 200K / 8K | Cheapest, fastest |
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.
- 9d ago First seen · 210 lines · 67 tokens per session scan A 17545282f46c
anthropic-claude is a skill published in the GitHub repository jnPiyush/AgentX (15 stars, last pushed yesterday), licensed Apache-2.0. It adds 67 tokens to every session and 2,309 once invoked, about $0.0003 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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