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 itallstartedwithaidea/agent-skills --skill anthropic-tool-masterygit clone --depth 1 https://github.com/itallstartedwithaidea/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/itallstartedwithaidea/agent-skills/anthropic-tool-mastery)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/anthropic-tool-mastery"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/anthropic-tool-mastery/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/itallstartedwithaidea/agent-skills/anthropic-tool-mastery"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/anthropic-tool-mastery.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 | $0.00028 | $0.02127 |
| Opus 5.5 | $0.00011 | $0.00851 |
| Sonnet 5.5 | $0.00006 | $0.00425 |
| Haiku 4.5 | $0.00003 | $0.00213 |
Grade A, and why
anthropic-tool-mastery 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 1mo 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anthropic Tool Mastery
Part of Agent Skills™ by googleadsagent.ai™
Description
Anthropic Tool Mastery is the definitive skill for leveraging Claude's native tool_use capability to its fullest potential. Claude's tool system is not merely a function-calling interface — it is a structured reasoning protocol that enables the model to decompose problems, dispatch parallel operations, handle streaming results, and chain tool outputs through extended thinking. Mastering these patterns is the difference between an agent that awkwardly calls one tool at a time and one that orchestrates complex multi-tool workflows with the fluency of a senior engineer.
This skill codifies patterns proven across the Buddy™ agent at googleadsagent.ai™, where tool orchestration handles concurrent Google Ads API calls, web searches, file operations, and analysis computations within single reasoning turns. The techniques cover tool definition design (schema quality directly affects call accuracy), parallel dispatch (multiple independent tool calls in a single turn), result composition (combining outputs from parallel calls), and error recovery (handling partial failures in multi-tool batches).
Advanced patterns include streaming tool results for real-time feedback, extended thinking integration (using thinking blocks to plan tool sequences before execution), and tool result caching to avoid redundant calls. These techniques apply directly to Claude Code's built-in tools and extend to custom MCP server tools.
Use When
- Building agents that need to call multiple tools per reasoning turn
- Tool call accuracy is below acceptable thresholds (wrong parameters, wrong tool selected)
- You need real-time streaming feedback from long-running tool operations
- Extended thinking should inform tool selection and parameter construction
- Multi-tool workflows require coordination and result composition
- Custom tools need to be designed for maximum model compatibility
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.
- 1mo ago First seen · 211 lines · 28 tokens per session scan A 9726b7818c0c
anthropic-tool-mastery is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (44 stars, last pushed 6mo ago), licensed MIT. It adds 28 tokens to every session and 2,127 once invoked, about $0.0001 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.
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