ScienceClaw is an agent system for scientific work that completes tasks as typed, executable workflows and turns replay-verified improvements into reusable skills and operators. It is intended for AI-assisted research across natural and social science disciplines. The catalogue entries are its skills and instruction for use with the system.
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 beita6969/ScienceClaw --skill article-writinggit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/article-writing)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/article-writing"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/article-writing/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/beita6969/scienceclaw/article-writing"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/article-writing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00057 | $0.00649 |
| Opus 5.5 | $0.00023 | $0.00260 |
| Sonnet 5.5 | $0.00011 | $0.00130 |
| Haiku 4.5 | $0.00006 | $0.00065 |
Grade A, and why
article-writing 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- article-writing — 100% identical, 0 lines differ
- article-writing — 100% identical, 170 lines differ
- article-writing — 100% identical, 0 lines differ
- article-writing — 100% identical, 0 lines differ
- article-writing — 100% identical, 0 lines differ
- article-writing — 100% identical, 0 lines differ
- article-writing — 100% identical, 0 lines differ
- article-writing — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Article Writing
Write long-form content that sounds like a real person or brand, not generic AI output.
When to Activate
- drafting blog posts, essays, launch posts, guides, tutorials, or newsletter issues
- turning notes, transcripts, or research into polished articles
- matching an existing founder, operator, or brand voice from examples
- tightening structure, pacing, and evidence in already-written long-form copy
Core Rules
- Lead with the concrete thing: example, output, anecdote, number, screenshot description, or code block.
- Explain after the example, not before.
- Prefer short, direct sentences over padded ones.
- Use specific numbers when available and sourced.
- Never invent biographical facts, company metrics, or customer evidence.
Voice Capture Workflow
If the user wants a specific voice, collect one or more of:
- published articles
- newsletters
- X / LinkedIn posts
- docs or memos
- a short style guide
Then extract:
- sentence length and rhythm
- whether the voice is formal, conversational, or sharp
- favored rhetorical devices such as parentheses, lists, fragments, or questions
- tolerance for humor, opinion, and contrarian framing
- formatting habits such as headers, bullets, code blocks, and pull quotes
If no voice references are given, default to a direct, operator-style voice: concrete, practical, and low on hype.
Banned Patterns
Delete and rewrite any of these:
- generic openings like "In today's rapidly evolving landscape"
- filler transitions such as "Moreover" and "Furthermore"
- hype phrases like "game-changer", "cutting-edge", or "revolutionary"
- vague claims without evidence
- biography or credibility claims not backed by provided context
Writing Process
- Clarify the audience and purpose.
- Build a skeletal outline with one purpose per section.
- Start each section with evidence, example, or scene.
- Expand only where the next sentence earns its place.
- Remove anything that sounds templated or self-congratulatory.
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 First seen · 86 lines · 57 tokens per session scan A 6ab082c7850e
article-writing is a skill published in the GitHub repository beita6969/ScienceClaw (1,019 stars, last pushed 9d ago), licensed MIT. It adds 57 tokens to every session and 649 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
biopython
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scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and visualization. Best for exploratory scRNA-seq analysis with established workflows. For deep learning models use scvi-tools; for data format questions use…
structure-prediction
Protein structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.
biomcp
Search and retrieve biomedical data - genes, variants, clinical trials, diagnostic tests, articles, drugs, diseases, pathways, proteins, adverse events, pharmacogenomics, and phenotype-disease matching. Use for gene function, variant pathogenicity, trials, diagnostics, drug safety, pathway context, disease workups…
biomcp-research
Do biomedical literature and variant research with the BioMCP CLI, and file what you learn about the tool itself as issues in the biomcp repo.
biological-expert
Expert-level biology, biotechnology, genetics, bioinformatics, and computational biology. Use when the user mentions biology, biotechnology, genetics, bioinformatics, or genomics, or when the task involves Molecular Biology, Genomics & Bioinformatics, Systems Biology, or Data Analysis.