Interpret AP-XPS spectra and generate DTCS surface reaction networks.
npx skills add https://github.com/carbonscott/deploy-opencode --skill nano-isaac
Please help me install this Agent Skill. Skill: nano-isaac Source: https://github.com/carbonscott/deploy-opencode/tree/main/claude/skills/nano-isaac Command: npx skills add https://github.com/carbonscott/deploy-opencode --skill nano-isaac
nano-isaac provides an AI catalysis research assistant for AP-XPS spectroscopy, enabling researchers to interpret XPS binding energies, run surface chemistry simulations, parse VAMAS data, fetch reaction-parameter data, and orchestrate DTCS CRN workflows on metal surfaces such as Ag and Cu.
Launch the nano-isaac environment and run a minimal CRN example to verify the end-to-end workflow.
High-intent search queries and answers about installing and using this skill.
AP-XPS binding energies are interpreted by looking up peak assignments and calculating environment-aware shifts for metal surfaces such as Ag and Cu. This enables researchers to accurately assign spectral peaks and refine mechanistic hypotheses for catalytic systems.
Generating a CRN for surface chemistry simulations involves using DTCS integration to construct and simulate reaction networks on metal surfaces. This produces reproducible simulations and synthesizes corresponding XPS spectra for comparison against experimental data.
VAMAS data parsing is supported to extract and process AP-XPS spectroscopy results. This allows researchers to ingest standardized experimental data formats and directly compare them with DTCS-generated simulated spectra.
Catalysis research on Ag and Cu metal surfaces is fully supported for AP-XPS-enabled workflows. The system provides binding energy lookups, DTCS CRN generation, and spectrum synthesis specifically tailored to these metal surface environments.
XPS spectra are synthesized from a chemical reaction network by running DTCS simulations on the defined CRN. This generates reproducible spectral data that can be directly compared against experimental AP-XPS results to validate mechanistic hypotheses.
BrightBlockCreate a git worktree and initialize dependencies for branch review.
youyinnnGenerate structured scientific hypotheses with mechanistic explanations and testable predictions.
ya-waySearch bioRxiv preprints by keywords, authors, dates, and categories, returning JSON metadata and PDFs.
letta-aiFit Raman spectra peaks using Lorentzian, Gaussian, or Voigt models with constrained parameters.
