design-experiment

design-experiment is a skill for Claude Code, Codex from yogsoth-ai/de-anthropocentric-research-engine. It costs 36 tokens per session (3,269 once invoked), scanned A, original, Apache-2.0.

A method for turning a testable hypothesis into a statistically defensible experiment. It covers designs such as comparisons, removing one factor at a time, testing combinations of factors, scaling, and robustness checks.

In plain words
What is it for?
Use it to specify experimental conditions, variables, controls, constraints, and a preregistered analysis plan for scientific or technical investigations.
Why use it?
It helps ensure that an experiment has measurable outcomes, defined factors and controls, and a planned analysis rather than an improvised comparison. It is not applicable when the hypothesis or outcome cannot be measured.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to specify experimental conditions, variables, controls, constraints, and a preregistered analysis plan for scientific or technical investigations.

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Install with agentmods
npx agentmods add skills/yogsoth-ai/de-anthropocentric-research-engine/design-experiment
Install

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.

Any agent
npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-experiment
Clone the repo
git clone --depth 1 https://github.com/yogsoth-ai/de-anthropocentric-research-engine

Made for: Claude Code, Codex.

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 design-experiment

README.md
[![agentmods](https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/design-experiment/github.svg)](https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/design-experiment)
Your own site
<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/design-experiment"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/design-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.

agentmods 80×15 button for design-experiment

Your own site · 80×15
<a href="https://agentmods.dev/skills/yogsoth-ai/de-anthropocentric-research-engine/design-experiment"><img src="https://agentmods.dev/badge/skills/yogsoth-ai/de-anthropocentric-research-engine/design-experiment.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,269 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 original 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 $0.00036 $0.03269
Opus 5.5 $0.00014 $0.01308
Sonnet 5.5 $0.00007 $0.00654
Haiku 4.5 $0.00004 $0.00327

Measured 28d ago against content hash be1ae665521e, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

design-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 28d 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.

skills/design-experiment/SKILL.md · 162 lines

How it starts

The opening of the file, as written. The whole thing — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.

design-experiment

Purpose

Translate a falsifiable hypothesis into a statistically defensible experiment. Factorial, ablation, comparison, scaling, and robustness are modes.

When to use / not applicable

Use after a hypothesis has an operational construct and discriminating prediction. Not applicable when the hypothesis or measurable outcome is absent.

Input contract

mode_contracts:
  factorial: &experiment_input
    required: [falsifiable_hypothesis, outcome, factors, constraints]
    optional: [baseline, candidate_models, budget, robustness_axes]
    constraints: [analysis_plan_must_be_preregistered]
  ablation: *experiment_input
  comparison: *experiment_input
  scaling: *experiment_input
  robustness: *experiment_input

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

  1. You MUST load skill identify-variables to operationalize the outcome, factors, controls, and their functional roles. You MUST load skill enumerate-dimension-values to define admissible levels or perturbation values. If the resource envelope or feasibility constraints cannot support an executable design, consider analyze-constraints-readiness before committing to a mode.
  2. Choose factorial, ablation, comparison, scaling, or robustness mode.
  3. You MUST load skill specify-metrics to preregister metrics, estimands, directionality, and decision thresholds. You MUST load skill estimate-sample-size to derive the sample or repetition requirement. You MUST load skill select-statistical-method to select the inference or estimation method before observing outcomes; specify power and the stopping rule. If the proposed metric or validator may share artifacts, labels, or assumptions with the system under test, consider audit-validator-independence before freezing the analysis plan.
  4. You MUST load skill construct-design-matrix to construct the runnable matrix for the selected mode. You MUST load skill design-randomness-protocol to define seeds, repetitions, and propagation rules. You MUST load skill specify-execution-environment to capture interpretation-relevant hardware, software, data, configuration, and versions. You MUST load skill specify-reproducibility-protocol to define and test the intended reproduction level. You MUST load skill optimize-design-under-budget to select a feasible information-efficient design under the declared resource envelope; include resource and failure checks. Once the design has produced observations and the task changes from planning to inference, analyze-experiment-results may be the better next tactic.

Read the full file on GitHub · 162 lines

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. 28d ago First seen · 162 lines · 36 tokens per session scan A be1ae665521e

Subscribe to this mod's changes

design-experiment is a skill published in the GitHub repository yogsoth-ai/de-anthropocentric-research-engine (505 stars, last pushed yesterday), licensed Apache-2.0. It adds 36 tokens to every session and 3,269 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 2026-09-24.

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