Train, deploy, and run predictions with neural networks using Flow Nexus and E2B sandboxes, isolated computing environments. Use existing model templates, distribute training across multiple environments, and check training progress and model performance.
- Train a model to classify images
- Analyze text sentiment with an existing template
- Train an LSTM model for time series forecasting
ruvnet/RuVector☆ 4.6KUpdated Oct 21, 2026
Use when evaluating crypto networks through a Placeholder-style lens: open networks, token investment frameworks, network participation, and long-duration crypto network ownership.
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questflowai/investorskills☆ 1.9KUpdated Aug 23, 2026
Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.
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GPTomics/bioSkills☆ 1.2KUpdated Aug 15, 2026
Offers functions for plotting split (or implicit) networks (unrooted, undirected) and explicit networks (rooted, directed) with reticulations extending. 'ggtree' and using functions from 'ape' and 'phangorn'. It extends the 'ggtree' package [@Yu2017] to allow the visualization of phylogenetic networks using the 'ggplot2' syntax. It offers an alternative to the plot functions already available in 'ape' Paradis and Schliep (2019) <doi:10.1093/bioinformatics/bty633> and 'phangorn' Schliep (2011) <d
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bioMate-AI/biomate-bioconductor-kb☆ 797Updated Jun 21, 2026
Aruba Networks integration. Manage data, records, and automate workflows. Use when the user wants to interact with Aruba Networks data.
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membranedev/application-skills☆ 269Updated Apr 28, 2026
Networking fundamentals as they apply to cloud infrastructure — virtual networks, subnets, routers, NAT, floating IPs, security groups, VLAN/VXLAN segmentation, load balancing, SDN concepts, and how Neutron-style cloud networks sit on top of physical topology. Covers TCP behavior at scale, congestion control, tail latency, overlay networks, and the operational gotchas that come from network layering. Use when designing cloud network topology, debugging cross-AZ latency, or reviewing a proposed VPC/SG design.
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Tibsfox/gsd-skill-creator☆ 71Updated Jul 20, 2026
CatColab Regulatory Networks - signed graphs for molecular biology modeling gene regulatory networks with positive (activating) and negative (inhibiting) edges.
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plurigrid/asi☆ 70Updated Jul 10, 2026
Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.
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lilinji/GeneTind-Life-Skills☆ 15Updated Aug 21, 2026
Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.
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peacezha/TRACE☆ 3Updated Oct 20, 2026
Infer gene regulatory networks from bulk or general expression data with mutual-information (ARACNe) and tree-ensemble (GENIE3, GRNBoost2) methods, and infer transcription-factor protein activity from regulons with VIPER and msVIPER. Covers the activity-not-edges paradigm, the undirected-association caveat, the DREAM5 wisdom-of-crowds and method-complementarity result, AUPRC-over-AUROC evaluation, and gold-standard incompleteness. Use when inferring a regulatory network from a bulk expression matrix, finding master regulators, or scoring TF activity from a signature. For single-cell motif-pruned regulons see scenic-regulons; for co-expression modules see coexpression-networks.
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peacezha/HPClaw☆ 3Updated Oct 11, 2026
Investigate BGP, the protocol for exchanging routes between networks, when connections fail or expected routes are missing. Collect read-only evidence, inspect connection states and routing policies, and review AS paths showing which networks a route passes through.
- Investigate a failed or unstable BGP connection
- Find why expected routes are missing
- Inspect route filters and AS paths
affaan-m/ECC☆ 285.4KUpdated Oct 21, 2026
Analyze networks of people, web pages, or systems using PageRank, a method that scores importance from connections. Identify influential nodes and communities, generate personalized recommendations, and examine communication bottlenecks to guide network improvements.
- Finding influential users and communities on social networks
- Analyzing links and page importance on a website
- Generating recommendations based on user preferences
ruvnet/ruflo☆ 78.1KUpdated Oct 21, 2026
Train, deploy, and manage neural networks with Flow Nexus in isolated E2B environments. Use custom architectures or ready-made templates, run predictions, monitor distributed training across multiple environments, and evaluate models before sharing them.
- Train an image classification model
- Forecast time series with LSTM
- Deploy a sentiment analysis template
ruvnet/ruflo☆ 78.1KUpdated Oct 21, 2026
Audit the titles, descriptions, images, and URLs shown when web pages are shared on social networks or messaging apps. Check Open Graph metadata, the tags that define link previews, for missing values and incorrect settings.
- Check link previews before publishing a page
- Find missing or empty Open Graph tags
- Review sharing images and URL settings
thedaviddias/Front-End-Checklist☆ 74.5KUpdated Oct 6, 2026
Build graph neural networks, models that learn from connected nodes, with PyTorch Geometric. Supports node and graph classification, link prediction, custom datasets, and training approaches for large graphs or graphs containing multiple types of nodes and connections.
- Build node and graph classification models
- Predict links between nodes
- Train models on graphs with multiple node and connection types
K-Dense-AI/scientific-agent-skills☆ 53.5KUpdated Oct 19, 2026
Clean and compare tandem mass spectra, measurements used to study molecular fragments, with matchms. Standardize metadata, filter peaks, search reference libraries, and export ranked candidate matches with similarity scores and matched-peak counts.
- Clean MS/MS spectra and standardize metadata
- Search reference libraries for compound candidates
- Compare spectra using similarity scores and matched peaks
K-Dense-AI/scientific-agent-skills☆ 53.5KUpdated Oct 19, 2026
Process satellite imagery and map data for geospatial research. Helps align coordinate systems and image grids, calculate vegetation and terrain measures, summarize data by area, and analyze road networks while preserving masks and source information.
- Align coordinate systems and raster grids
- Calculate vegetation indices from satellite imagery
- Analyze slope and aspect from elevation data
K-Dense-AI/scientific-agent-skills☆ 53.5KUpdated Oct 19, 2026
Monitor Modbus traffic in industrial control networks to identify unauthorized writes, unusual commands, and departures from normal communication. Use packet analysis and intrusion detection rules to investigate suspected controller changes and produce reports with recommended actions.
- Investigating unauthorized changes to PLC registers
- Setting up Modbus intrusion detection rules
- Checking communication patterns after a suspected compromise
mukul975/Anthropic-Cybersecurity-Skills☆ 34.8KUpdated Aug 31, 2026
Build normal traffic profiles for industrial control networks and identify unusual connections, polling intervals, and protocol operations. Use machine learning and process records to investigate deviations and produce anomaly reports with severity levels.
- Establishing normal traffic profiles for industrial networks
- Detecting unusual connections and polling intervals
- Investigating Nozomi Guardian or Dragos alerts
mukul975/Anthropic-Cybersecurity-Skills☆ 34.8KUpdated Aug 31, 2026
Configure pfSense to separate internal, guest, DMZ, and IoT networks. Set firewall rules, port forwarding, VPN connections, and bandwidth policies, then check traffic logs and test that each network can reach only its intended destinations.
- Separate staff, guest, server, and IoT networks
- Set port forwarding rules for externally accessible services
- Configure VPN access and bandwidth policies
mukul975/Anthropic-Cybersecurity-Skills☆ 34.8KUpdated Aug 31, 2026
Official · GoogleFreeSummary Configure how GKE applications receive traffic from the internet or internal networks. The skill provides configuration templates for routing, load balancing, certificates, and access protection, and guides diagnosis of failed health checks, dropped requests, and TLS connection problems.
- Expose a GKE application through Gateway API or Ingress
- Split traffic between application versions during a rollout
- Configure Cloud Armor protection and HTTPS certificates
google/skills☆ 21.5KUpdated Oct 21, 2026
Assess smart-home and commercial mesh networks, where devices relay communications. Covers Zigbee, Thread, and Matter, with methods for observing radio traffic, reviewing device setup and key handling, and testing exposure of lighting, locks, and sensors.
- Planning a smart-home mesh security assessment
- Reviewing Zigbee lighting and lock controls
- Checking Thread network credential handling
SnailSploit/Claude-Red☆ 7.7KUpdated Oct 13, 2026
Assess LoRaWAN and sub-GHz wireless devices by capturing signals, examining packet formats, and checking key reuse and replay weaknesses, where recorded messages are resent. It covers IoT networks, fixed-code remotes, and proprietary radio protocols.
- Reviewing LoRaWAN keys and frame counters
- Testing fixed-code remotes for replay weaknesses
- Analyzing proprietary wireless packet formats
SnailSploit/Claude-Red☆ 7.7KUpdated Oct 13, 2026
Assess WPA3 wireless security by reviewing mixed WPA2/WPA3 settings and SAE, its password authentication method. Examine implementation weaknesses, H2E support, and 6 GHz requirements to determine relevant tests and detection signals.
- Reviewing security settings on WPA3 wireless networks
- Assessing downgrade risks in mixed WPA2/WPA3 deployments
- Organizing SAE implementation and H2E checks
SnailSploit/Claude-Red☆ 7.7KUpdated Oct 13, 2026