Arcanum: Skill for Claude Code

.claude/skills/experiment/SKILL.md

experiment is a skill for Claude Code from cyberAlchemyAI/Arcanum. It costs 0 tokens per session (2,878 once invoked), scanned A, original, no licence file.

A workflow for proposing and recording a testable experiment before running it. A success or failure rule is fixed in advance, then checked by a separate reviewer.

In plain words
What is it for?
Use it to design experiments with clear, falsifiable criteria and produce a fixed criterion.md file before execution.
Why use it?
It prevents changing the standard of success after seeing the results. It also separates designing the test from running and judging it.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is cyberAlchemyAI/Arcanum's own configuration. It tells Claude Code how to work on Arcanum itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything Arcanum configures →

Reuse

Borrowing it

Nothing to install: this file belongs to cyberAlchemyAI/Arcanum. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/cyberAlchemyAI/Arcanum/main/.claude/skills/experiment/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/cyberAlchemyAI/Arcanum

Made for: Claude Code.

Wrote 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.

agentmods badge for experiment

README.md
[![agentmods](https://agentmods.dev/badge/skills/cyberalchemyai/arcanum/experiment.svg)](https://agentmods.dev/skills/cyberalchemyai/arcanum/experiment)
Your own site
<a href="https://agentmods.dev/skills/cyberalchemyai/arcanum/experiment"><img src="https://agentmods.dev/badge/skills/cyberalchemyai/arcanum/experiment.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,878 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.02878
Opus 5 $0.00000 $0.01439
Sonnet 5 $0.00000 $0.00576
Haiku 4.5 $0.00000 $0.00288

Measured 8d ago against content hash 529efb5549e7, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

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 8d 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.

.claude/skills/experiment/SKILL.md · 194 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

Changes

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.

  1. 8d ago First seen · 194 lines · 0 tokens per session scan A 529efb5549e7

Subscribe to this mod's changes

experiment is a skill published in the GitHub repository cyberAlchemyAI/Arcanum (25 stars, last pushed 7d ago), with no licence file. It costs nothing until one of its globs matches a file; then it loads 2,878 tokens. 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.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens