hypothesis-gen

hypothesis-gen is an agent for Claude Code from TaewoooPark/MagLab. It costs 40 tokens per session (425 once invoked), scanned A, original, MIT.

A research assistant that proposes possible explanations for a scientific question and checks whether each can be tested and fits known physics.

In plain words
What is it for?
Use it to generate, compare, and test candidate hypotheses using supplied context and material information.
Why use it?
It helps turn a broad research topic into a ranked set of testable ideas, while clearly marking them as AI-generated.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: model in frontmatter; mentions subagents.

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.

agentmods
npx agentmods add agents/taewooopark/maglab/hypothesis-gen
Clone the repo
git clone --depth 1 https://github.com/TaewoooPark/MagLab

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 hypothesis-gen

README.md
[![agentmods](https://agentmods.dev/badge/agents/taewooopark/maglab/hypothesis-gen.svg)](https://agentmods.dev/agents/taewooopark/maglab/hypothesis-gen)
Your own site
<a href="https://agentmods.dev/agents/taewooopark/maglab/hypothesis-gen"><img src="https://agentmods.dev/badge/agents/taewooopark/maglab/hypothesis-gen.svg" alt="Measured on agentmods" height="20"></a>
Per session 40 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 425 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00040 $0.00425
Opus 5 $0.00020 $0.00212
Sonnet 5 $0.00008 $0.00085
Haiku 4.5 $0.00004 $0.00042

Measured 6d ago against content hash 1dde9cb64bda, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

hypothesis-gen 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.

agents/hypothesis-gen.md · 50 lines

What it actually says

You are the hypothesis-generation subagent of MagLab (D1, §5.10).

① Single objective

Generate candidate research hypotheses for a given topic, rank them by an Elo-style pairwise tournament, and reflect each against known physics so that only plausible, falsifiable hypotheses survive.

② Input

A research topic or open question, optional context (prior results, materials of interest), and the requested number of hypotheses.

③ Output schema (structured JSON)

{"status": "success|partial|failed",
 "hypotheses": [
   {"statement": "...",
    "rationale": "...",
    "discriminating_test": "...",
    "elo": 0,
    "ai_generated": true}
 ],
 "warnings": ["..."]}

④ Tool budget

physics_check · physics_compute · material_lookup · provenance_query only. Maximum 10 turns.

⑤ Source guide

  • Read: the topic and context supplied in the task; material data via material_lookup.
  • Do not read: the D2 anomaly-explanation code path — hypothesis generation (D1) is independent of it.

⑥ Boundaries · ambiguity

  • Every hypothesis must be falsifiable and carry a concrete discriminating test.
  • Every hypothesis carries ai_generated: true — these are AI-proposed directions, never claimed results.
  • Reflect each hypothesis against the sanity oracle; drop any that violate known physics.
  • Do not fabricate numbers or citations. Quantitative claims must come from tools.
  • If the topic is too vague to ground, return status: partial and state what is missing.
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. 6d ago First seen · 50 lines · 40 tokens per session scan A 1dde9cb64bda

Subscribe to this mod's changes

hypothesis-gen is an agent published in the GitHub repository TaewoooPark/MagLab (11 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 425 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 2026-08-30.

Related

Other agents, from other repositories

fba-runner

FBA simulation agent. Runs standard FBA, parsimonious FBA, Flux Variability Analysis, and gene/reaction knockout simulations using COBRApy. Use after a validated metabolic model is available and the user wants to compute growth rates, flux distributions, or knockout phenotypes.

aiming-lab/AutoResearchClaw · 62 tokens

stat-comparison-analyst

Statistical comparison agent. Compares proposed methods against baselines, ablations, oracle references, and theoretical predictions, then identifies where evidence supports, weakens, or refutes the claims.

aiming-lab/AutoResearchClaw · 45 tokens

stat-quality-auditor

Statistical research quality auditor. Checks whether formulation, method, theory, experiments, comparisons, and final claims form a coherent and defensible statistical research chain.

aiming-lab/AutoResearchClaw · 37 tokens

stat-method-proposer

Statistical method proposal agent. Proposes candidate methods, baselines, variants, diagnostics, and ablations that directly address the formal problem formulation.

aiming-lab/AutoResearchClaw · 34 tokens

stat-result-synthesizer

Statistical result synthesis agent. Writes the final research narrative, combining formulation, proposed method, theory, experiments, comparisons, and limitations into a coherent report.

aiming-lab/AutoResearchClaw · 38 tokens

grounded-review-reviewer

Score, diagnose, and gate a research report draft for grounded-review. Prefer a model different from the writer when available.

gaotiexinqu/OneResearchClaw · 30 tokens