synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 skills add synthetic-sciences/openscience --skill hypothesesgit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/hypotheses)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/hypotheses"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/hypotheses/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.
<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/hypotheses"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/hypotheses.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00099 | $0.01210 |
| Opus 5.5 | $0.00040 | $0.00484 |
| Sonnet 5.5 | $0.00020 | $0.00242 |
| Haiku 4.5 | $0.00010 | $0.00121 |
Grade A, and why
hypotheses 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 29d 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.
How it starts
The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hypotheses
A hypothesis is a statement the world could contradict, paired with the observation that would contradict it. Everything else is a theme. The work here is turning a direction into statements of that kind, and designing the smallest experiment that tells them apart.
Rules
- Falsifiable, specific, mechanistic. "Method A improves accuracy" is a theme. "Under label noise above 20%, A's margin over B grows with noise because A's loss saturates on mislabeled examples" is a hypothesis: direction, condition, mechanism, and a prediction that could fail.
- Always at least two. The hypothesis and the best competing explanation for the same observation (confound, artifact, simpler mechanism). An experiment that cannot separate them is not yet designed.
- Predictions before data. For each hypothesis, the observable pattern if it is true and if it is false, written down before the experiment. Pre-specify the primary outcome, the analysis and the decision rule.
- Power the study. Seeds, samples or replicates chosen from the expected effect size and variance, not from habit. Three seeds detect only large effects; say what the study can and cannot detect.
- Control what could explain the result otherwise. Randomize assignment, block on known nuisance factors, include the positive and negative controls that show the measurement works.
- Cheap discriminating experiments first. Order experiments by information per unit cost; the first one should be able to kill at least one hypothesis.
Workflow
- State the observation or question and what is already known about it.
- Write two to four hypotheses with mechanisms; include the null and the confound.
- For each, the predictions if true and if false; mark which predictions differ.
- Design the discriminating experiment; check it against
references/experimental-design.md. - Choose n; write the analysis plan; define the decision rule.
- Hand over as a study plan, or start it with autoresearch.
What ships with it
3 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.
- 29d ago Changed · +5 lines 1dc6450df35c
- 1mo ago First seen · 98 lines · 99 tokens per session scan A 4c918ccf8cf9
hypotheses is a skill published in the GitHub repository synthetic-sciences/openscience (4,004 stars, last pushed 3d ago), licensed Apache-2.0. It adds 99 tokens to every session and 1,210 once invoked, about $0.0004 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 2026-09-14.
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