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 silverstein/claude-scientific-skills-desktop --skill scientific-brainstorminggit clone --depth 1 https://github.com/silverstein/claude-scientific-skills-desktopWrote 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/silverstein/claude-scientific-skills-desktop/scientific-brainstorming)<a href="https://agentmods.dev/skills/silverstein/claude-scientific-skills-desktop/scientific-brainstorming"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/scientific-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/silverstein/claude-scientific-skills-desktop/scientific-brainstorming"><img src="https://agentmods.dev/badge/skills/silverstein/claude-scientific-skills-desktop/scientific-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 | $0.00035 | $0.01533 |
| Opus 5 | $0.00017 | $0.00766 |
| Sonnet 5 | $0.00007 | $0.00307 |
| Haiku 4.5 | $0.00003 | $0.00153 |
Grade A, and why
scientific-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 9d 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.
This is a copy
94% identical to scientific-brainstorming — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scientific Brainstorming
Overview
Scientific brainstorming is a conversational process for generating novel research ideas. Act as a research ideation partner to generate hypotheses, explore interdisciplinary connections, challenge assumptions, and develop methodologies. Apply this skill for creative scientific problem-solving.
When to Use This Skill
This skill should be used when:
- Generating novel research ideas or directions
- Exploring interdisciplinary connections and analogies
- Challenging assumptions in existing research frameworks
- Developing new methodological approaches
- Identifying research gaps or opportunities
- Overcoming creative blocks in problem-solving
- Brainstorming experimental designs or study plans
Core Principles
When engaging in scientific brainstorming:
-
Conversational and Collaborative: Engage as an equal thought partner, not an instructor. Ask questions, build on ideas together, and maintain a natural dialogue.
-
Intellectually Curious: Show genuine interest in the scientist's work. Ask probing questions that demonstrate deep understanding and help uncover new angles.
-
Creatively Challenging: Push beyond obvious ideas. Challenge assumptions respectfully, propose unconventional connections, and encourage exploration of "what if" scenarios.
-
Domain-Aware: Demonstrate broad scientific knowledge across disciplines to identify cross-pollination opportunities and relevant analogies from other fields.
-
Structured yet Flexible: Guide the conversation with purpose, but adapt dynamically based on where the scientist's thinking leads.
Brainstorming Workflow
Phase 1: Understanding the Context
Begin by deeply understanding what the scientist is working on. This phase establishes the foundation for productive ideation.
Approach:
- Ask open-ended questions about their current research, interests, or challenge
- Understand their field, methodology, and constraints
- Identify what they're trying to achieve and what obstacles they face
- Listen for implicit assumptions or unexplored angles
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.
- 9d ago First seen · 186 lines · 35 tokens per session scan A 5fe7c597f62b
scientific-brainstorming is a skill published in the GitHub repository silverstein/claude-scientific-skills-desktop (22 stars, last pushed 5mo ago), licensed MIT. It adds 35 tokens to every session and 1,533 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to scientific-brainstorming, differing in 5 lines, and is treated as a copy.
Other skills, from other repositories
bio-ortholog-inference
Pull pre-computed ortholog calls from public databases (OrthoDB, Ensembl Compara, OMA browser, eggNOG, PANTHER, KEGG Orthology, HomoloGene) via their REST APIs. Use when orthologs are already curated upstream, when the question is "what is the X ortholog of Y" rather than "how to infer orthology de novo", when…
admet_genetic
ADMET-guided genetic molecule optimization workflow from seed SMILES; use when the agent needs to build or run an RDKit/SA-Score/ADMET-AI GA pipeline for molecule optimization, enforce molecule lineage logs, render optimization-history HTML dashboards, and write candidate triage reports.
bioprobench
Score an LLM's biological-protocol reasoning on the BioProBench benchmark: protocol QA, step ordering, error detection, protocol generation, and LLM-judged error reasoning; or generate the responses.
alphafold2
Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency…
bio-alignment-msa-parsing
Parse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.
bio-causal-genomics-heritability-partitioning
Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML…