hypothesis-first

hypothesis-first is a skill for Claude Code from jeonnoin-alt/Eureka. It costs 30 tokens per session (4,790 once invoked), scanned A, original, MIT.

A research workflow that requires registering a hypothesis and analysis plan before looking at results. It compares this practice with test-driven development, where tests are written before implementation.

In plain words
What is it for?
Use it before experiments, statistical analyses, model comparisons, pilots, feasibility studies, or reanalyses to record predictions and interpret results against them.
Why use it?
It reduces the risk of changing a prediction after seeing the data or presenting a chance finding as if it had been expected.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents.

Part of the Eureka plugin — 16 skills, 1 agent, 1 hook shipped together

Good fit Use it before experiments, statistical analyses, model comparisons, pilots, feasibility studies, or reanalyses to record predictions and interpret results against them.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jeonnoin-alt/eureka/hypothesis-first
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 jeonnoin-alt/Eureka --skill hypothesis-first
Clone the repo
git clone --depth 1 https://github.com/jeonnoin-alt/Eureka

Made for: Claude Code.

Or install Eureka, the plugin that ships this one along with the rest of its 16 skills, 1 agent, 1 hook.

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 hypothesis-first

README.md
[![agentmods](https://agentmods.dev/badge/skills/jeonnoin-alt/eureka/hypothesis-first/github.svg)](https://agentmods.dev/skills/jeonnoin-alt/eureka/hypothesis-first)
Your own site
<a href="https://agentmods.dev/skills/jeonnoin-alt/eureka/hypothesis-first"><img src="https://agentmods.dev/badge/skills/jeonnoin-alt/eureka/hypothesis-first/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 hypothesis-first

Your own site · 80×15
<a href="https://agentmods.dev/skills/jeonnoin-alt/eureka/hypothesis-first"><img src="https://agentmods.dev/badge/skills/jeonnoin-alt/eureka/hypothesis-first.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,790 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 $0.00030 $0.04790
Opus 5 $0.00015 $0.02395
Sonnet 5 $0.00006 $0.00958
Haiku 4.5 $0.00003 $0.00479

Measured 12d ago against content hash 706ddbd6c7ad, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

hypothesis-first 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 12d 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/hypothesis-first/SKILL.md · 378 lines

How it starts

The opening of the file, as written. The whole thing — 378 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Hypothesis-First (Scientific TDD)

Overview

Register the hypothesis first. Run the analysis. Interpret against the prediction.

Core principle: If you didn't commit your hypothesis before seeing the data, you don't know if you're doing science or storytelling.

Violating the letter of the rules is violating the spirit of the rules.

When to Use

Always:

  • Any statistical analysis
  • Any model comparison or validation
  • Any experiment, pilot, or feasibility study
  • Any reanalysis of existing data
  • Any "quick look" at results

Exceptions (discuss with your PI or collaborator):

  • Purely exploratory analyses — permitted, but MUST be labeled as exploratory in all outputs and the manuscript
  • Computational benchmarking with no inferential claim

Thinking "skip registration just this once"? Stop. That's rationalization.

The Iron Law

NO DATA ANALYSIS WITHOUT A REGISTERED HYPOTHESIS FIRST

Ran the analysis before registering? Delete the results. Start over.

No exceptions:

  • Don't keep results as "reference"
  • Don't "adjust" your hypothesis to match what you found
  • Don't peek at effect sizes to calibrate your prediction
  • Don't label post-hoc HARKing as "hypothesis-driven"

Register first, then run. Period.

REGISTER-EXECUTE-INTERPRET

digraph hypothesis_first_cycle {
    rankdir=LR;
    register [label="REGISTER\nState H1, H0, outcome,\ntest, threshold.", shape=box, style=filled, fillcolor="#ffcccc"];
    verify_register [label="Inline verify\nregistration\nis complete", shape=diamond];
    subagent_review [label="Dispatch\nregistration-reviewer\n(fresh subagent)", shape=box, style=filled, fillcolor="#ffaaaa"];
    commit [label="git commit\n(only if Approved)", shape=box, style=filled, fillcolor="#ff8888"];
    execute [label="EXECUTE\nRun pre-specified\nanalysis exactly.\nNo deviations.", shape=box, style=filled, fillcolor="#ccffcc"];
    verify_execute [label="Any deviation\nfrom plan?", shape=diamond];
    document_deviation [label="Document as\nEXPLORATORY\nlabel deviation", shape=box, style=filled, fillcolor="#ffffcc"];
    interpret [label="INTERPRET\nCompare to prediction.\nReport ALL results.\nNo selective reporting.", shape=box, style=filled, fillcolor="#ccccff"];
    next [label="Next\nhypothesis", shape=ellipse];

    register -> verify_register;
    verify_register -> subagent_review [label="complete"];
    verify_register -> register [label="incomplete:\nfinish it"];
    subagent_review -> register [label="Issues Found:\nfix and re-review"];
    subagent_review -> commit [label="Approved"];
    commit -> execute;
    execute -> verify_execute;
    verify_execute -> document_deviation [label="yes"];
    verify_execute -> interpret [label="no"];
    document_deviation -> interpret;
    interpret -> next;
    next -> register;
}

Read the full file on GitHub · 378 lines

Files

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.

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. 12d ago First seen · 378 lines · 30 tokens per session scan A 706ddbd6c7ad

Subscribe to this mod's changes

hypothesis-first is a skill published in the GitHub repository jeonnoin-alt/Eureka (2 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 4,790 once invoked, about $0.0002 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 2026-08-31.

Related

Other skills, from other repositories

binder-design-campaign

Gated protein design campaign: each expert judgement is a checkpoint a human signs off before compute is spent. Branches: de novo binder design (epitope choice, generation, co-folding ensemble ranking, ranked order sheet); structure and complex prediction with calibrated confidence; protein engineering (stability…

SPYfighting/my-academic-skills · 148 tokens

scientific-illustrator-local

Rebuild scientific schematic images as editable SVG and Adobe Illustrator paths with live text, or draw new scientific diagrams from explicit content. Use for raster-to-vector reconstruction, editable paper figures, and local Illustrator drawing. Separates labels from tracing and preserves existing artwork through a…

SPYfighting/my-academic-skills · 82 tokens

training-plan

Create personalized training plans for 5K, 10K, Half Marathon, and Marathon races. Use when the user requests a training plan for any of these distances, wants to prepare for a race, or asks for a structured running program. This skill generates comprehensive markdown training plans that include race goals, training…

ColinEberhardt/claude-running-coach · 78 tokens

class-generation

Generate openEHR class documentation — class-definition tables, effective views, and UML class/package diagrams — from BMM schemas with the bmm-publisher tool, for specifications-XX repos and their docs/UML/ content. This skill should be used when the user asks to regenerate class tables, run bmm-publisher, generate…

openEHR/ai-plugins · 124 tokens

scientific-figure-prompt-compiler

Transform concise scientific or technical descriptions into structured visual specifications and high-quality image-generation prompts. Use for scientific figures, technical diagrams, system architectures, mechanisms, workflows, spatial models, timelines, layered systems, and information-dense visuals. Model-agnostic…

basejb/skills · 72 tokens

pkpd-modeling

Pharmacokinetic and pharmacodynamic modelling and simulation - non-compartmental analysis, compartmental and population PK, PK/PD and exposure-response, TMDD, PBPK orientation, bioequivalence, allometric scaling and first-in-human dose, drug interaction prediction, and Bayesian therapeutic drug monitoring. Use when…

K-Dense-AI/scientific-agent-skills · 273 tokens