rowan
Skill4 starsChanged 2 months agoNo licenceView on GitHub
Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimisation, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.
- Reads credentials
- Installs packages
11 other projects use a version of this, 6 of them edited.
Adopted by
Most recently taken up by
- BrightGold70/skillsedited, first seen Nov 8, 2026
- patoangolano/bio-stacktheir own version, first seen Oct 15, 2026
- Luooops/skillstheir own version, first seen Oct 13, 2026
- garri333/Skillsedited, first seen Oct 11, 2026
- wu-yc/LabClawedited, first seen Oct 8, 2026
- K-Dense-AI/drug-discovery-agent-skillstheir own version, first seen Aug 16, 2026
Used with
Projects with it also use ncats-arax.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
K-Dense-AISkillncats-arax
Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries.
Also in 3 projects that use this
Other versions11 projects use a version of this.
Word for word · 0
None found.
Edited · 6
- wu-yc/LabClaw★ 1.1k97% same
- LeonChaoX/qinyan-academic-skills★ 91097% same
- qinyan-ai/qinyan-academic-skills★ 8097% same
- BrightGold70/skills★ 197% same
- garri333/Skills★ 197% same
- yf8578/claude-skills-collection★ 197% same
Wrote their own version · 5
- jaechang-hits/SciAgent-Skills★ 386same name
- K-Dense-AI/drug-discovery-agent-skills★ 54same name
- Luooops/skills★ 1same name
- uppifyagency/boilerplate-saas-2026★ 1same name
- patoangolano/bio-stack★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository249 other files it gives its agents.
- .github/copilot-instructions.mdCopilot instructions
- asset-frontmatter-checkSkill
- asset-self-containment-checkSkill
- markdown-link-auditorSkill
- agent-governance-patternsSkill
- agentic-evalSkill
- agent-owasp-complianceSkill
- agent-supply-chainSkill
- ai-readySkill
- copilot-cli-quickstartSkill
- first-askSkill
- generate-custom-instructions-from-codebaseSkill
- github-copilot-starterSkill
- nano-banana-pro-openrouterSkill
- noob-modeSkill
- prompt-builderSkill
- remember-interactive-programmingSkill
- rememberSkill
- tldr-promptSkill
- what-context-neededSkill
- acceptance-test-planSkill
- architecture-auditSkill
- gap-analysisSkill
- miro-boardSkill
- process-mapSkill
- requirements-documentSkill
- risk-analysisSkill
- stakeholder-mapSkill
- tradeoff-analysisSkill
- use-caseSkill
- write-epicSkill
- write-user-storySkill
- architecture-models-4plus1Skill
- create-architectural-decision-recordSkill
- technology-stack-blueprint-generatorSkill
- dashboard-beautifySkill
- dashSkill
- daskSkill
- data-validation-and-schema-contractsSkill
- experiment-tracking-and-reproducibilitySkill
- exploratory-data-analysisSkill
- feature-engineering-and-leakage-checksSkill
- forecastic-evaluationSkill
- gradient-boostongSkill
- hypothesis-testing-and-ab-evaluationSkill
- imbalanced-classificationSkill
- matplotlibSkill
- mlflowSkill
- model-recommendationSkill
- model-selections-and-cross-validationSkill
- networkxSkill
- numpy-scipy-statisticsSkill
- opencvSkill
- pandas-data-wranglingSkill
- plotlySkill
- polarsSkill
- powerbi-modellingSkill
- pymcSkill
- scikit-learn-pipelines-and-column-transformersSkill
- scikit-learnSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
4 rules. 0 are common, 2 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 11 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.
“Common” is measured: kept by at least 6 in 10 of the 11 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
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- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

