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 TransluceAI/claude-code-plugins --skill first-experimentgit clone --depth 1 https://github.com/TransluceAI/claude-code-pluginsWrote 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/transluceai/claude-code-plugins/first-experiment)<a href="https://agentmods.dev/skills/transluceai/claude-code-plugins/first-experiment"><img src="https://agentmods.dev/badge/skills/transluceai/claude-code-plugins/first-experiment/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/transluceai/claude-code-plugins/first-experiment"><img src="https://agentmods.dev/badge/skills/transluceai/claude-code-plugins/first-experiment.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 | $0.00127 | $0.04674 |
| Opus 5.5 | $0.00051 | $0.01870 |
| Sonnet 5.5 | $0.00025 | $0.00935 |
| Haiku 4.5 | $0.00013 | $0.00467 |
Grade A, and why
first-experiment 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 6d 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 — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Your first experiment
You are guiding someone through their first fxtr experiment. They bring the question; you help them turn it into an experiment that calls language models, build it, run it, and read what it found, explaining the fxtr ideas as each one comes up. By the end they should have results they care about and know where each part of the experiment lives, so they can change it themselves.
This skill sets the order of the work and when to check in with the user. How to write each part is in the adjacent fxtr skill and behaviors skill: read the pages they point to for each task, and follow their best practices. Your first experiment shows an example of a first experiment. By default you should follow along with this example, but if the user has a different question they want to study or decides to deviate from the example in some other way, you should follow their lead and just use this example as a guide for the pace and size of a good first experiment. Even if you do follow this example, you should guide the user to customize the experiment design as you go; don't just copy the code exactly!
Before you start reading the docs and testing out their workflow, please send the user a short message like this:
Welcome to fxtr's "Your first experiment" guide! I'll start by reading the documentation and checking your environment. Then we'll decide what question to study, and I'll guide you through how to set up your first experiment with fxtr.
How to guide
- Guide the user through each choice and unit of work. The point of this guided walkthrough is not to just write an experiment, it is to use this experiment as a way to teach the user about the pieces of fxtr and how they work. By default, you should pause often, explaining what you are about to do and pointing out the relevant concepts and guides that the user can read.
- Ask a few questions at a time, and offer concrete options with a recommended default. Someone new to fxtr can choose between options more easily than they can fill in a blank.
- Teach in passing. When a fxtr idea first matters (a step, a dimension, the cache), explain it in a sentence or two in terms of their experiment, and link the page that covers it. Don't front-load concepts they haven't needed yet.
- Keep the first version small. Aim for an experiment of about one screen of design that answers one question: a few conditions, ten to fifty items, and one measurement. Record bigger ideas as next steps rather than building them now.
- Ask before spending money. Before any launch that calls a paid API, say how many model calls it makes (conversations times turns, plus judge calls) and get the user's go-ahead. Repeat this for the full run.
- Report plainly. Say what ran, what was reused, and what failed, with the job ID. Make sure to ground your summaries in the actual results of the experiment (e.g. the report entity) rather than by manually adding up printouts.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 308 lines · 127 tokens per session scan A 5b66f22fc1cd
first-experiment is a skill published in the GitHub repository TransluceAI/claude-code-plugins (7 stars, last pushed 6d ago), licensed Apache-2.0. It adds 127 tokens to every session and 4,674 once invoked, about $0.0005 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 2026-10-08.
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