874 skills · sorted by relevance
Analyze datasets in R or Python, from exploring distributions and missing values to fitting regressions. Produce tables and figures for publication, save scripts and model results, and review the analysis for common problems.
Find datasets for a research question and compare whether they support the planned study. Receive ranked candidates with feasibility grades, coverage, access requirements, methodological limitations, and recommended next steps for obtaining the most suitable data.
Apply machine learning methods to estimate intervention effects and explore how effects vary across groups. Provides implementation examples, diagnostic checks, and reporting guidance for Double Machine Learning, causal forests, and variable selection.
Find arXiv papers by topic, author, category, or ID and retrieve abstracts and publication details. Create BibTeX citation entries, then use Semantic Scholar to explore citation counts, references, related papers, and author profiles.
Search academic papers, researchers, institutions, and journals through the OpenAlex CLI. Explore citations and related work, examine publication trends, export reference records, and download available open access PDFs, which are freely accessible papers.
Explore a codebase to find tightly coupled or fragmented modules, then compare interface designs that hide complexity behind simpler entry points. Produces a GitHub issue proposing a refactor, including dependency handling, testing impact, and design trade-offs.
Explore four new design options for a subject, with each option presented as a separate rendered image. Use the images to compare possible appearances and consider improvements to an existing design.
Manage a project's stored materials through inventory, migration, archiving, and ranking based on verified source reads. Changes follow owner approval, preserve material references, and include validation records and rollback procedures.
Explore code through structured summaries of functions, calls, branches, and variable usage. Use the tldr commands to locate relevant files, trace change impacts, find affected tests, and identify potentially unused code before inspecting the implementation.
Explore research ideas through conversation, question existing assumptions, and connect methods across disciplines. The skill helps identify research gaps, compare ways to combine papers, and summarize promising directions with practical next steps.
Test changed screens or explore an entire app in a real browser. Check interactions, accessibility, and mobile layouts, then document failures with screenshots, reproduction steps, expected results, and suggested fixes.
Entry point for an AI agent's ethos.md: a durable snapshot of what the agent is for, what it must not do, and what success looks like. Routes a request to explore and capture intent (ethos-explore), or write or edit the file (ethos-write), including renaming an Eval Author 0.1.0 ETHOS.md. Owns the standard every ethos sub-flow follows. Use when the user asks "help me write an ethos.md", "does my agent have an ethos.md?", "update my agent's ethos.md", or when you need to pick between the ethos sub-flows.
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Captures what an AI agent is supposed to do — its purpose, boundaries, and success criteria — as a durable snapshot for anyone building, evaluating, reviewing, or changing it. Explores the user's codebase and docs first, then asks one intent question at a time for what source cannot supply. Hands its answers to ethos-write, which writes ethos.md. Use over generic brainstorming when the goal is an agent's ethos.md.
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Enter explore mode - a thinking partner for exploring ideas, investigating problems, and clarifying requirements. Use when the user wants to think through something before or during a change.
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Deep-dive analysis of GitHub projects. Use when the user mentions a GitHub repo/project name and wants to understand it — triggered by phrases like "help me look at this project", "learn about XXX", "what is this project like", "analyze this repo", or any request to explore/evaluate a GitHub project. Covers architecture, community health, competitive landscape, and cross-platform knowledge sources.
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Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection.
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Build an NFT discovery workflow with Alchemy NFT API v3 and isolate typed on-chain reads through viem. Use when creating collection explorers or ownership checks. Trigger with "Alchemy NFT API", "NFT collection explorer", or "read an NFT contract".
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Extract durable ktx knowledge and semantic-layer contribution proposals from staged Looker runtime dashboard, Look, and explore JSON. Load for WorkUnits whose raw files are under explores/, dashboards/, or looks/.
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Query and analyze data in Azure Data Explorer (Kusto/ADX) using KQL for log analytics, telemetry, and time series analysis. WHEN: KQL queries, Kusto database queries, Azure Data Explorer, ADX clusters, log analytics, time series data, IoT telemetry, anomaly detection.
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Deep research and discovery before building something new. Explores local projects for reusable code, researches competitors, reads forums and reviews, analyses plugin ecosystems, investigates technical options, and produces a comprehensive research brief. Three depths: focused (30 min), wide (1-2 hours), deep (3-6 hours). Triggers: 'research this', 'discovery', 'explore the space', 'what should I build', 'competitive analysis', 'before I start building', 'research before coding'. Not for cited fact-checking research reports (a separate harness does those); this is pre-build product discovery.
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Use when the user wants to think through an idea before committing to a plan, e.g. /gsd-explore auth strategy. Runs a Socratic session, optionally spawns research, then routes output to notes, seeds, requirements, or new phases.
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Expert knowledge for Azure Synapse Analytics development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Synapse SQL pools, Spark pools, Delta Lake, Synapse Link (Cosmos/SQL), or ELT via PolyBase, and other Azure Synapse Analytics related development tasks. Not for Azure Data Factory (use azure-data-factory), Azure Data Explorer (use azure-data-explorer), Azure Databricks (use azure-databricks), Azure Stream Analytics (use azure-stream-analytics).
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Expert knowledge for Azure Stream Analytics development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building ASA jobs with Event Hubs/Kafka, Cosmos/SQL outputs, autoscale, VNets/private endpoints, or edge/IoT workloads, and other Azure Stream Analytics related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Event Hubs (use azure-event-hubs).
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Expert knowledge for Azure Open Datasets development including limits & quotas. Use when handling non-Spark dataset downloads, throttling behavior, quota limits, retry logic, or rate-limit workarounds, and other Azure Open Datasets related development tasks. Not for Azure Data Explorer (use azure-data-explorer), Azure Synapse Analytics (use azure-synapse-analytics), Azure Databricks (use azure-databricks), Azure Machine Learning (use azure-machine-learning).
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