
Manages the temperature state of an object by placing it into an appropriate appliance (fridge for cooling, microwave for heating). Use when the task requires modifying an object's temperature property, such as "cool some bread" or "heat some food". Takes the object identifier, temperature-modifying receptacle, and final target receptacle as inputs, and outputs the object at the target location with its temperature state changed.
Use when the agent needs to apply a tool to a target object in ALFWorld to accomplish an interaction such as cleaning, heating, cooling, or examining. This skill handles locating both the tool and target object, then executing the correct environment action (e.g., `clean`, `heat`, `cool`, `use`) to progress the task.
This skill verifies and prepares a target receptacle for receiving an object. It is triggered before placing an item into a receptacle (e.g., a garbage can) to ensure the receptacle is accessible and suitable. The skill involves navigating to the receptacle, observing its state (e.g., open/closed, occupied), and performing any necessary preparatory actions like opening it, resulting in a ready-to-use target location.
This skill opens or closes a receptacle (like a fridge, cabinet, or microwave) to access its interior or secure it. It should be triggered when an object needs to be placed inside/retrieved from a closed container, or when an open container should be closed (e.g., for energy efficiency or task cleanliness). The skill decides the appropriate 'open' or 'close' action based on the receptacle's current state and the task context.
Use when the agent needs to check whether an ALFWorld task objective has been met after completing a sub-action (e.g., placing an object). This skill parses the task goal, evaluates the latest environment observation, and outputs a verification decision — task complete, task incomplete, or action ineffective — to guide the next step.
Closes an open receptacle to maintain environment tidiness after inspection. Use when you have finished searching a container (drawer, cabinet, fridge) and no longer need it open. Takes a receptacle identifier as input and outputs confirmation that the receptacle is closed, preventing clutter during multi-step search tasks.
Plans and executes movement to a target receptacle within the ALFWorld environment. Use when the agent needs to reach a specific location before interacting with objects there (e.g., go to fridge to cool an item, go to garbagecan to dispose of an item). Takes a receptacle identifier as input, executes the `go to` action, and outputs confirmation of arrival at the destination.
Use when the agent is holding an object and needs to place it into a target receptacle in ALFWorld. This skill checks receptacle suitability, opens closed containers if needed, and executes the `put` command to store the object. It handles both open surfaces (countertops, beds) and closed containers (drawers, cabinets).
Use when the agent needs to find a specific object in ALFWorld that is not currently in inventory and whose location is unknown. This skill parses the environment observation, ranks receptacles by likelihood of containing the target object using common-sense reasoning, and outputs a navigation action to the most promising location.
Cools a held object using an appropriate cooling appliance such as a fridge or freezer. Use when the task requires reducing the temperature of an object (e.g., "cool some pot", "chill the mug") and the agent is already holding the object. Performs the ALFWorld `cool` action and outputs the cooled object ready for subsequent placement or serving steps.
Use when the agent must collect and track multiple instances of the same object type in ALFWorld (e.g., "put two cellphone in bed"). This skill maintains a count of collected versus needed objects, guides systematic searching through receptacles, and ensures each found object is placed at the target before searching for the next.
Navigates to a suspected location and identifies a target object. Use when your goal requires finding a specific object (e.g., "potato", "plate") and its location is not immediately known. Moves to a relevant receptacle (like a fridge or cabinet), checks its contents, and outputs the object's location or confirms its absence.
Uses a heating appliance (microwave, stoveburner, oven) to apply heat to a specified object. Use when the task requires warming or cooking an item (e.g., "heat some egg", "warm the mug") and a heating appliance is available. Takes the object name and appliance name as input and outputs the object in a heated state, ready for placement at the task's target location.
Searches for a specified tool or device (e.g., a desklamp, knife, or sponge) within the ALFWorld environment by checking relevant surfaces. Use when you need a tool to interact with another object as part of a task but the tool is not in your inventory or immediate vicinity. Takes a tool name as implicit input and navigates to likely storage spots (sidetables, shelves, countertops) until the tool is found.
Infer gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). Use when analyzing transcriptomics data (bulk RNA-seq, single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions. Supports distributed computation for large-scale datasets.
Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct REST control, use individual database skills (uniprot-database, kegg-database).
Query ChEMBL's bioactive molecules and drug discovery data. Search compounds by structure/properties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.
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.
Performs an initial scan of the ALFWorld environment to identify all visible objects and receptacles. Use when you first enter an environment and need to build a mental map for task planning. Processes raw observation text into a structured list of entities, categorizing them as objects or receptacles.
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
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.
Places a held object onto or into a target receptacle. Trigger this skill when the agent is carrying an object and needs to deposit it at a specific location to complete a task, such as installing an item on a holder or storing it in a container. It takes the object and destination receptacle as inputs, performing a 'put' action to finalize the object's positioning according to the task goal.
Plans a path to move the agent between receptacles to search for target objects. Use this when you need to traverse the environment to reach a specific location or systematically explore multiple areas. It takes the current location and destination receptacle as input, and outputs the next 'go to' action to approach the target.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
This skill picks up a target object from a specified receptacle after the object has been visually confirmed. Use this skill when the agent has located an object in a receptacle and needs to acquire it. It requires the object and source receptacle as inputs and results in the object being added to the agent's inventory.
Uses an appliance to change the state of an object (cooling, heating, or cleaning). Use when the task requires altering an object's temperature or cleanliness using a specific device (e.g., cooling with a fridge, heating with a microwave, cleaning with a sinkbasin). Takes the object, target state, and appliance as inputs and executes the corresponding modifier action.
This skill searches for a specific receptacle (e.g., garbage can, cabinet) by systematically exploring the environment, checking multiple locations until found. Trigger when the target receptacle is not initially visible, often after scanning nearby areas. It outputs the receptacle's location and may involve navigating through multiple waypoints.
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
Query NCBI Gene via E-utilities/Datasets API. Search by symbol/ID, retrieve gene info (RefSeqs, GO, locations, phenotypes), batch lookups, for gene annotation and functional analysis.
CLI/Python toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl/UniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, genome downloads. For advanced BLAST/batch processing, use biopython. For multi-database integration, use bioservices.
Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies.
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.
Parses the natural language task goal to extract actionable sub-objectives and required objects. Trigger this skill whenever a new task is assigned to break down complex instructions into clear, sequential targets. It interprets phrases like 'look at X under Y' to identify target objects (pillow), reference objects (desklamp), and spatial relationships (under).
Inspects a receptacle's contents by navigating to it and reading the observation. Use when you need to check what is on or inside a receptacle (e.g., "what's on the shelf", "is the holder empty", "check the table for items"). Executes `go to {receptacle}`, parses the observation listing items present, and decides whether to take an item, search elsewhere, or proceed.
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.
Picks up a target object from its current receptacle and moves it to a specified destination receptacle. Use when you have located an object and need to relocate it to complete a task (e.g., moving a laptop to a desk). Takes the object identifier, source receptacle, and destination receptacle as inputs and outputs the action sequence to take, transport, and place the object.
This skill opens a closed receptacle to access its contents. It should be triggered when an agent needs to interact with items inside a closed container (e.g., fridge, microwave, drawer). The skill takes a receptacle identifier as input, performs the open action, and outputs the observation of the interior, enabling subsequent item retrieval or placement.
Searches for a suitable empty or appropriately occupied receptacle (like a shelf or table) to place an object. Use when you are holding an object that needs to be stored or placed and must find a receptacle that meets the placement criteria. Examines candidate receptacles by navigating to and inspecting each one until a suitable location is found.
This skill opens a closed receptacle to allow access to its contents or to enable interaction with it. It should be triggered when the agent encounters a closed receptacle (e.g., 'The fridge 1 is closed.') that must be opened to proceed with a task, such as cooling an item with a fridge or taking an object from inside a cabinet. The skill outputs the 'open' action on the specified receptacle.
Systematically searches a sequence of likely locations for a target object based on common sense. Use when you need to find a specific object and know which receptacles to check but not which one contains it. Takes a list of candidate receptacles, orchestrates navigation and inspection, and outputs when the target is found or all locations are exhausted.
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
Re-examines previously visited locations to confirm the absence of a target object or to check for overlooked items. Use when an initial search fails to find enough objects or when double-checking is required before concluding task failure. Systematically revisits receptacles, re-opens closed containers, and re-inspects contents to ensure no viable location was missed.
Primary Python toolkit for molecular biology. Preferred for Python-based PubMed/NCBI queries (Bio.Entrez), sequence manipulation, file parsing (FASTA, GenBank, FASTQ, PDB), advanced BLAST workflows, structures, phylogenetics. For quick BLAST, use gget. For direct REST API, use pubmed-database.
Query NCBI ClinVar for variant clinical significance. Search by gene/position, interpret pathogenicity classifications, access via E-utilities API or FTP, annotate VCFs, for genomic medicine.
Access COSMIC cancer mutation database. Query somatic mutations, Cancer Gene Census, mutational signatures, gene fusions, for cancer research and precision oncology. Requires authentication.
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python workflows with multiple databases, prefer bioservices. Use this for direct HTTP/REST work or KEGG-specific control.
Access RCSB PDB for 3D protein/nucleic acid structures. Search by text/sequence/structure, download coordinates (PDB/mmCIF), retrieve metadata, for structural biology and drug discovery.
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.
Systematically explores storage receptacles (drawers, cabinets, shelves) to find an appropriate placement location for an object. Use when the agent needs to store an item but the exact target receptacle is unknown or ambiguous. Opens, inspects, and closes candidate receptacles to assess suitability, then places the object in the best match.
Moves the agent to a specified receptacle or object location within the Alfworld environment. Use this skill when the agent needs to physically approach a target to inspect or interact with it, such as when checking an object's state or preparing for pickup. The skill takes a target location name as input and executes the 'go to' action, resulting in the agent being positioned at the destination for subsequent operations.
This skill disposes of an object by placing it into a disposal receptacle like a garbage can. It should be triggered when the task requires discarding an object (e.g., 'put it in garbagecan') and the agent is at the disposal location with the object in hand. The skill executes the 'put' action to place the object in/on the target receptacle, completing the disposal subtask.
Picks up a specified object from a given receptacle. Use this skill when the agent has located a required object and needs to acquire it for later use, such as taking an item from a surface or container. The skill requires the object and source receptacle as inputs, executing a 'take' action to transfer the object into the agent's inventory, enabling further manipulation like placement or usage.
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
Use when you need to connect to the SciGraph SCP server for PertKGE (knowledge graph for compound-protein interaction inference using perturbation transcriptomics + regulatory network; cold-start CPI prediction) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for PhySci (physics scientific-publication ontology with Linked Data metadata concepts like methods/problems/solutions) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for Otter-UBC (IBM Research Otter-Knowledge multimodal knowledge graphs for drug discovery) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for Otter-DUDe (IBM Research Otter-Knowledge multimodal knowledge graphs for drug discovery) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for OPB (Ontology of Physics for Biology; reference ontology applying physics/thermodynamics concepts to biological systems across scales) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for OM (ontology for units of measurement, dimensions, and unit conversion/dimensional analysis) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for MatMechOnto (MaterioMiner dataset linking a materials mechanics ontology with fine-grained literature entity annotations; supports NER and CPMP relation extraction) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for MKG-FENN (multimodal knowledge graph fused end-to-end neural network for drug-drug interaction prediction) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for KGNN (Knowledge Graph Neural Network; drug-drug interaction prediction dataset/framework) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for MarineExpert (marine expert management knowledge graph integrating experts/publications/institutions/collaborations/fields from multi-source marine info) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
--- name: scp-kg-mtl description: Use when you need to connect to the SciGraph SCP server for KG-MTL (knowledge graph enhanced multi-task learning framework for molecular interaction prediction: DTI/CPI) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples. --- # SCP-KG-MTL (SciGraph) MCP client ## What this SCP is KG-MTL is a knowledge graph enhanced m
Use when you need to connect to the SciGraph SCP server for KG-FM (framework-material knowledge graph dataset; see npj Computational Materials 2025 paper) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for E-Coli (antibiotic resistance knowledge graph; KIDS framework) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for KCL (knowledge graph of microscopic chemical associations among common elements; supports knowledge-guided augmentation/message passing for molecular graphs) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for iKraph (large-scale biomedical knowledge graph from PubMed + external databases like Hetionet) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for GraPhysics (physics equation-centered knowledge graph of laws/constants with bridge relations between equations) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for IDP-KG (Intrinsically Disordered Protein Knowledge Graph) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for ClinicalKG/CKG (Clinical Knowledge Graph database dump for building Neo4j; proteomics + clinical decision support) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for Biomedical-Drug (DREAMwalk multi-layer heterogeneous biomedical knowledge graph for drug repurposing) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for CISREG (genomic regulation knowledge graph about enhancers, TADs, genes, proteins, phenotypes) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for BEACON (Bridging Chemical Structure and Conceptual Knowledge; CPI prediction) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for AEQG-Physics (AEQG-MCQ-Distractors-Physics high-school physics concept-map knowledge graph for MCQ generation and distractors) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for ADR-Graph (drug–adverse reaction knowledge graph dataset for link prediction/side-effect discovery, used with EdgePrediction) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Workflow orchestrator that chains existing skills for feature development
Use when the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.
Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio/video, extract text from recordings, or label speakers in interviews or meetings.
Build agentic applications with GitHub Copilot SDK. Use when embedding AI agents in apps, creating custom tools, implementing streaming responses, managing sessions, connecting to MCP servers, or creating custom agents. Triggers on Copilot SDK, GitHub SDK, agentic app, embed Copilot, programmable agent, MCP server, custom agent.
Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
OpenAI's general-purpose speech recognition model. Supports 99 languages, transcription, translation to English, and language identification. Six model sizes from tiny (39M params) to large (1550M params). Use for speech-to-text, podcast transcription, or multilingual audio processing. Best for robust, multilingual ASR.
Expert guide for using n8n-mcp MCP tools effectively. Use when searching for nodes, validating configurations, accessing templates, managing workflows, or using any n8n-mcp tool. Provides tool selection guidance, parameter formats, and common patterns.
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support
Transcribe audio via OpenAI Audio Transcriptions API (Whisper).
Research-to-implement pipeline chaining 5 MCP tools with graceful degradation
Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.
Complete workflow for building, implementing, and testing goal-driven agents. Orchestrates building-agents-* and testing-agent skills. Use when starting a new agent project, unsure which skill to use, or need end-to-end guidance.
Prepares a household appliance (microwave, oven, toaster, fridge) for use by ensuring it is in the correct open/closed state. Use when the agent needs to heat, cool, or cook an item and must first open or close the appliance before placing an object inside. Takes an appliance identifier as input and outputs a confirmation that the appliance is ready for the next action.
Operates a device or appliance (like a desklamp, microwave, or fridge) to interact with another object. Use when the task requires using a tool on a target item (e.g., "look at laptop under the desklamp", "heat potato with microwave"). Locates both the device and target object, co-locates them, and executes the appropriate use action (toggle, heat, cool, or clean).
Instructions for creating MCP (Model Context Protocol) servers that expose tools and resources for the agent to use. Use when the user asks to create a new MCP server or add MCP capabilities.
Implement real-time streaming transcription with Deepgram. Use when building live transcription, voice interfaces, or real-time audio processing applications. Trigger with phrases like "deepgram streaming", "real-time transcription", "live transcription", "websocket transcription", "voice streaming".
Automate GitHub workflows with AI assistance. Includes PR reviews, issue triage, CI/CD integration, and Git operations. Use when automating GitHub workflows, setting up PR review automation, creating GitHub Actions, or triaging issues.
Design LLM applications using LangChain 1.x and LangGraph for agents, memory, and tool integration. Use when building LangChain applications, implementing AI agents, or creating complex LLM workflows.
Transcribe audio via OpenAI Audio Transcriptions API (Whisper).
Use when you need to connect to the SciGraph SCP server for BioActivity/TarIKGC (semantics-enhanced KG for target identification in small-molecule drug discovery) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for the DDKG biomedical knowledge graph (drug–drug interaction prediction) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for DTINet (Drug-Target Interaction Network knowledge graph for predicting novel drug–target interactions from heterogeneous biomedical networks) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for Healx (rare disease drug repositioning biomedical knowledge graph by Healx Ltd.) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for InstructProteinKG (UniProt/Swiss-Prot derived protein knowledge graph for sequence-text alignment and instruction learning) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for KANO-ChEBI / ElementKG-CHEBI (knowledge graph integrating element properties and functional group hierarchies from Periodic Table/Wikipedia/ChEBI to support KANO molecular representation learning and functional prompting) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for KnowDDI (knowledge subgraph learning for accurate + interpretable drug-drug interaction prediction; supports zero-shot DDI) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for Material (materials-science knowledge graph built from ~150k papers via an LLM-based NLP pipeline; contains materials, formulas, properties, synthesis conditions) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for MatKG (autonomously generated materials-science knowledge graph extracted from >5M papers; entities include materials/properties/applications/synthesis) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for MEKG (Materials Experiment Knowledge Graph capturing provenance of synthesis/processing/characterization/performance experiments) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for MGEDKG / MGED-KG (bilingual Chinese-English materials terminology knowledge graph with ~8660 terms built from Comprehensive Dictionary of Materials via NLP) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for MiKG (microbiota–gut–brain axis biomedical knowledge graph) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for NAFLDkb (drug development knowledge base for non-alcoholic fatty liver disease) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for OceanGraph (Historical Marine Biodiversity Knowledge Graph / OceanGraphRAG KG built with Microsoft GraphRAG) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for Otter-PrimeKG (IBM Research Otter-Knowledge multimodal knowledge graphs for drug discovery) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for PPIKG (BioGRID+UniProt protein–protein interaction knowledge graph for target deconvolution) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for ProteinKG25 (GO + protein sequence knowledge graph) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for PSPP / PSPP-KG (process-structure-property-performance materials design knowledge graph with influence relations extracted from literature) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for RNA-KG (RNA-centered heterogeneous biomedical knowledge graph with contextual properties for RNA interaction contexts) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for SciMKG (multimodal K-12 science education knowledge graph; packaged as SciMKG.ttl RDF/Turtle view) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for SpaTalk (LRT-KG spatial transcriptomics knowledge graph for ligand-receptor-transcription factor-target gene hierarchies) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for Theoria (curated theoretical physics knowledge graph with JSON entries containing equations, derivations, assumptions, references, and verification code) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Local text-to-speech via sherpa-onnx (offline, no cloud)
Spawn Agentica multi-agent patterns
Activate multi-agent orchestration mode
Build Python agents with Agentica SDK - @agentic decorator, spawn(), persistence, MCP integration
Expert in building voice AI applications - from real-time voice agents to voice-enabled apps. Covers OpenAI Realtime API, Vapi for voice agents, Deepgram for transcription, ElevenLabs for synthesis, LiveKit for real-time infrastructure, and WebRTC fundamentals. Knows how to build low-latency, production-ready voice experiences. Use when: voice ai, voice agent, speech to text, text to speech, realtime voice.
Expert in CrewAI - the leading role-based multi-agent framework used by 60% of Fortune 500 companies. Covers agent design with roles and goals, task definition, crew orchestration, process types (sequential, hierarchical, parallel), memory systems, and flows for complex workflows. Essential for building collaborative AI agent teams. Use when: crewai, multi-agent team, agent roles, crew of agents, role-based agents.
Use when you need to connect to the SciGraph SCP server for ElementKG 2.0 / InstructProteinKG and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including configuration (streamableHttp + SCP-HUB-API-KEY) and Python 3.10+ client usage examples.
Navigates the agent to a target appliance (microwave, stove, fridge, or sinkbasin) needed for object processing. Use when you are holding an object that needs heating, cooling, or cleaning and must move to the correct appliance station. Identifies the required appliance from the task context and executes the movement action.
Cleans a specified object using an appropriate cleaning receptacle (e.g., sinkbasin). Use when a task requires an object to be in a clean state (e.g., "clean potato", "wash apple") before proceeding. Navigates to the cleaning location, performs the clean action, and confirms the object is now clean.
Local speech-to-text with the Whisper CLI (no API key).
Create a minimal working Deepgram transcription example. Use when starting a new Deepgram integration, testing your setup, or learning basic Deepgram API patterns. Trigger with phrases like "deepgram hello world", "deepgram example", "deepgram quick start", "simple transcription", "transcribe audio".
Implement speech-to-text transcription workflow with Deepgram. Use when building pre-recorded audio transcription, batch processing, or implementing core transcription features. Trigger with phrases like "deepgram transcription", "speech to text", "transcribe audio", "audio transcription workflow", "batch transcription".
Implement Deepgram reference architecture for scalable transcription systems. Use when designing transcription pipelines, building production architectures, or planning Deepgram integration at scale. Trigger with phrases like "deepgram architecture", "transcription pipeline", "deepgram system design", "deepgram at scale", "enterprise deepgram".
Goal-based workflow orchestration - routes tasks to specialist agents based on user goals
Use when you need to connect to the SciGraph SCP server for TxGNN (large-scale biomedical knowledge graph for drug repurposing and therapeutic relationship prediction, Neo4j-imported) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for SSC-CoT (knowledge graph for stepwise self-consistent mathematical reasoning on trigonometric functions) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for YaSAScore (reaction knowledge graph for compound synthesis accessibility; USPTO/Pistachio reactions as directed reactant→product edges) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for TransFOL (multimodal biomedical knowledge graph for drug-drug interaction prediction and complex relational/logical reasoning) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
Use when you need to connect to the SciGraph SCP server for ReaKE (reaction-centered knowledge graph for self-supervised contrastive molecular representation learning; reactions as hyperedges linking reactants/products/templates) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for RegionalGeoTime (regional geologic time standards knowledge graph integrating 17 regional standards with the international time scale) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for PyBioMart (Pythonic interface for querying BioMart/Ensembl; supports genomic data extraction for biomedical knowledge graphs and disease-gene association prediction via KG embeddings) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for QUDT (quantities/units/dimensions/systems/prefixes/constants/datatypes ontology; based on QUDT-all-in-one-OWL.ttl) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.
Use when you need to connect to the SciGraph SCP server for Otter-STITCH (IBM Research Otter-Knowledge multimodal knowledge graphs for drug discovery) and call its MCP tools (query_cypher, get_kg_statistics, get_entity_details, get_experiment_workflow), including streamableHttp configuration with SCP-HUB-API-KEY and Python 3.10+ usage examples.