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 brainstorminggit 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/brainstorming)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/brainstorming"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/brainstorming/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/brainstorming"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/brainstorming.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 | $0.00095 | $0.01153 |
| Opus 5.5 | $0.00038 | $0.00461 |
| Sonnet 5.5 | $0.00019 | $0.00231 |
| Haiku 4.5 | $0.00010 | $0.00115 |
Grade A, and why
brainstorming 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 1mo 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 — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brainstorming
Good research ideas are not found by asking for good ideas. They come from a clear map of what is known, a deliberate set of moves that produce many candidates, and a cold ranking that throws most of them away. Generation and selection are separate steps; mixing them kills the unusual candidates first.
Rules
- Map before you generate. Ten minutes on what is established, what is contested and what is assumed produces better candidates than an hour of free association. Use the user's notes and, when the field is unfamiliar, one research-lookup or a shallow literature-review round.
- Generate wide, by named moves. Aim for fifteen to thirty candidates across the moves below. Write each as one line: the change and the outcome it would show. Do not judge while generating.
- Rank cold. Score each candidate on value if it works, probability it works, cost to find out, and whether the user can do it with what they have. Prefer the candidate that is cheap to test and informative either way.
- Commit to a shortlist of three, each with the first experiment that would test it and the result that would kill it.
- Say what is already done. A candidate that exists in the literature is a lead to read, not an idea to pursue; mark it.
Moves
| Move | Question that produces candidates |
|---|---|
| Gap | What does every paper assume, measure or exclude without justification? |
| Transfer | Which method from an adjacent field has never been applied here, and why not? |
| Inversion | What if the accepted direction of the effect, or the usual objective, is reversed? |
| Constraint | What changes under a tenth of the data, compute, labels, or time? Under ten times? |
| Failure | Where does the best method fail, and what is common to those cases? |
| Measurement | Is the standard metric measuring the thing everyone claims? What would? |
| Composition | Which two known results, combined, predict something neither does alone? |
| Mechanism | The effect is established; what is the mechanism, and what would distinguish candidates? |
| Simplification | What is the simplest baseline nobody reports, and would it match? |
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.
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.
- 1mo ago Changed · +6 lines 9489bd5c6b41
- 1mo ago First seen · 101 lines · 95 tokens per session scan A 934d2ac61f68
brainstorming is a skill published in the GitHub repository synthetic-sciences/openscience (4,025 stars, last pushed today), licensed Apache-2.0. It adds 95 tokens to every session and 1,153 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.
Other skills, from other repositories
knowledge-extension-ecosystem-lsp-code-intelligence
A codebase guide to LSP, the standard protocol behind editor features such as symbol queries, file diagnostics, and language-aware code information.
github-explorer
Explore and summarize popular GitHub projects. Use when the user wants to discover trending GitHub repositories, analyze project details, or get summaries of popular projects.
google-workspace-setup
One-time setup for gws: install, OAuth, scopes, auto-approve.
google-sheets
Google Sheets via gws: read/write cells, append rows, structured batch edits.
attachments
Move bytes between Gini upload space, external URLs, and workspace files. Used by every attachment / file-upload / file-download flow regardless of the target system (Linear, GitHub, S3, Notion, etc.).
google-docs
Google Docs via gws: read, append text, structured batch edits.