skills/skill-collections/alfworld/alfworld-object-heater/SKILL.md
Heats a specified object using an available heating appliance (e.g., microwave, stoveburner). Use when you are holding an object that requires heating and need to navigate to and operate the heating appliance. Takes the object and appliance as inputs and results in the object being in a heated state.
npx skillsauth add zjunlp/Skills alfworld-object-heaterInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Heat an object you are holding using a compatible heating appliance (microwave or stoveburner).
go to {appliance} (e.g., go to microwave 1)open {appliance} -- verify observation confirms it is now openheat {object} with {appliance} -- verify observation confirms "You heat the {object}"go to microwave 1 / go to stoveburner 1open microwave 1heat {object} with {appliance} (e.g., heat potato 1 with microwave 1)Scenario: You are holding potato 1 and need to heat it.
Thought: I need to heat the potato. I'll go to the microwave.
Action: go to microwave 1
Observation: The microwave 1 is closed.
Action: open microwave 1
Observation: You open the microwave 1. The microwave 1 is open. In it, you see nothing.
Action: heat potato 1 with microwave 1
Observation: You heat the potato 1 using the microwave 1.
Result: The potato is now heated. Proceed to place it at the destination if required by the task.
development
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
development
Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies.
tools
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
development
Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.