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 beel-collab/presets.dev --skill tool-designgit clone --depth 1 https://github.com/beel-collab/presets.devWrote 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/beel-collab/presets.dev/tool-design)<a href="https://agentmods.dev/skills/beel-collab/presets.dev/tool-design"><img src="https://agentmods.dev/badge/skills/beel-collab/presets.dev/tool-design/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/beel-collab/presets.dev/tool-design"><img src="https://agentmods.dev/badge/skills/beel-collab/presets.dev/tool-design.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 | $0.00039 | $0.02979 |
| Opus 5.5 | $0.00016 | $0.01192 |
| Sonnet 5.5 | $0.00008 | $0.00596 |
| Haiku 4.5 | $0.00004 | $0.00298 |
Grade A, and why
tool-design 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.
This is a copy
89% identical to tool-design — 20 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use This Skill
Build tools that agents can use effectively, including architectural reduction patterns
Use this skill when working with build tools that agents can use effectively, including architectural reduction patterns.
Tool Design for Agents
Tools are the primary mechanism through which agents interact with the world. They define the contract between deterministic systems and non-deterministic agents. Unlike traditional software APIs designed for developers, tool APIs must be designed for language models that reason about intent, infer parameter values, and generate calls from natural language requests. Poor tool design creates failure modes that no amount of prompt engineering can fix. Effective tool design follows specific principles that account for how agents perceive and use tools.
When to Use
Activate this skill when:
- Creating new tools for agent systems
- Debugging tool-related failures or misuse
- Optimizing existing tool sets for better agent performance
- Designing tool APIs from scratch
- Evaluating third-party tools for agent integration
- Standardizing tool conventions across a codebase
Core Concepts
Tools are contracts between deterministic systems and non-deterministic agents. The consolidation principle states that if a human engineer cannot definitively say which tool should be used in a given situation, an agent cannot be expected to do better. Effective tool descriptions are prompt engineering that shapes agent behavior.
Key principles include: clear descriptions that answer what, when, and what returns; response formats that balance completeness and token efficiency; error messages that enable recovery; and consistent conventions that reduce cognitive load.
Detailed Topics
The Tool-Agent Interface
Tools as Contracts Tools are contracts between deterministic systems and non-deterministic agents. When humans call APIs, they understand the contract and make appropriate requests. Agents must infer the contract from descriptions and generate calls that match expected formats.
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 · 325 lines · 39 tokens per session scan A e838c07538d0
tool-design is a skill published in the GitHub repository beel-collab/presets.dev (3 stars, last pushed 5mo ago), licensed MIT. It adds 39 tokens to every session and 2,979 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to tool-design, differing in 20 lines, and is treated as a copy.
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