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 agentmods add skills/evoscientist/evoskills/experiment-craftnpx skills add EvoScientist/EvoSkills --skill experiment-craftgit clone --depth 1 https://github.com/EvoScientist/EvoSkillsWrote 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/evoscientist/evoskills/experiment-craft)<a href="https://agentmods.dev/skills/evoscientist/evoskills/experiment-craft"><img src="https://agentmods.dev/badge/skills/evoscientist/evoskills/experiment-craft.svg" alt="Measured on agentmods" 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 | $0.00172 | $0.01586 |
| Opus 5 | $0.00086 | $0.00793 |
| Sonnet 5 | $0.00034 | $0.00317 |
| Haiku 4.5 | $0.00017 | $0.00159 |
Grade A, and why
experiment-craft 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- experiment-craft — 94% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Experiment Craft
A systematic approach to running, debugging, and iterating on research experiments. The critical skill is not running more experiments — it's understanding WHY experiments fail.
When to Use This Skill
- User's experiment is not working or producing unexpected results
- User needs help diagnosing why a method fails on certain data
- User wants to organize their experiment process with structured logging
- User asks about debugging research code or iterating on approaches
- User mentions "experiment debugging", "why doesn't this work", "experiment log", "results are wrong"
This skill is typically loaded from within
experiment-pipelinewhen a stage attempt fails. After debugging, return to the pipeline's stage-gate structure to continue. Can also be used standalone for any experiment debugging.
The Debugging Mindset
Finding WHY experiments fail is the most critical research skill. Not analyzing results leads to two failure modes:
- Slow progress: Running random experiments without understanding failure causes
- Wasted time: Abandoning good approaches because activation tricks were missed
The goal is not to run more experiments. The goal is to run the RIGHT experiments — ones that isolate causes and test specific hypotheses.
5-Step Diagnostic Flow
When an experiment fails or produces unexpected results, follow these five steps:
Step 1: Collect Failure Cases
Gather concrete examples of bad results. Look at the actual outputs, not just aggregate metrics. What specifically went wrong? Are the failures systematic or random?
Step 2: Find a Working Version
You need a baseline that works. Two ways to find one:
- Simplify the task: Reduce data complexity, relax the task setting, add more supervision, use easier inputs
- Remove your changes: Start from the baseline method and remove your algorithmic improvements one by one
If you can't find any working version, simplify further until something works. There is always a simple enough version that works.
What ships with it
2 files 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 · 131 lines · 172 tokens per session scan A a56d30cbb5a1
experiment-craft is a skill published in the GitHub repository EvoScientist/EvoSkills (434 stars, last pushed 5d ago), licensed Apache-2.0. It adds 172 tokens to every session and 1,586 once invoked, about $0.0009 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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