676 skills · sorted by relevance
Download PDFs or process local files, extract their text, and turn processed data into structured PDF reports with ReportLab. Includes alternative extraction methods and text output when PDF generation fails.
Continue Python tasks after repeated sandbox failures by saving code as a script and running it through the shell, a command interface. The workflow covers execution checks, result retrieval, and optional cleanup.
Continue Python tasks when the e2b sandbox, an isolated execution environment, is unavailable. Guides switching from execute_code_sandbox to run_shell, preparing dependencies, checking execution results, and saving intermediate data between calls.
Debug repeated execution failures by saving Python code to a script file and running it through run_shell. Keep the script available for inspection, review intermediate output and errors, and edit it for another run.
Recover from failed code execution by simplifying code, switching to shell commands, or handing complex tasks to shell_agent. Explicit working directories and output checks help keep generated files in the expected location.
Routes bulk RNA-seq requests to workflows for generating expression counts and checking quality, or comparing gene expression between groups. Checks input data, sample information, and analysis requirements before directing the next step.
Retrieve concise summaries from GWAS Catalog, a resource for studies linking genetic variants to traits. Look up studies, associations, genes, variants, and publications, with JSON output or saved full responses when needed.
Check whether a single tool outage, invalid input, or repeated retry can disrupt an entire agent workflow. Use dialogue tests to identify spreading errors, missing fallbacks, and uncontrolled retries, then document confirmed findings.
Compare running models or tasks on a device, nearby edge infrastructure, or in the cloud. Break down interaction delays, data transfers, energy use, costs, and failure recovery to assess deployment choices for multimodal and real-time agents.
Design or review Apache Hudi tables through questions about data volume, updates, and usage. Get architecture recommendations, a decision record explaining tradeoffs, and configuration artifacts for supported processing paths.
Extract a webpage’s content from its URL and convert it to Markdown, a plain-text format that preserves document structure. Use it to collect articles, blog posts, tutorials, or documentation for reading and further processing.
Turn mathematical modeling reports into DrawIO diagrams showing solution steps, model structures, and data processing flows. Produce editable source files, export PDFs for papers when DrawIO is available, and record diagram choices and export results.
Clean Excel columns by removing unwanted prefixes from numeric values and extracting Chinese characters from text. Add the cleaned results as new columns, then save an Excel file and provide a download link.
Turn a recently solved research problem into searchable Markdown documentation. Capture symptoms, failed attempts, root causes, working code, and prevention checks, then organize the solution by category and link it to related records.
Compare Zephyr changes using the same prepared dataset and matching execution settings. Analyze Finelog metrics for each processing stage to distinguish CPU efficiency, elapsed time, and peak memory changes, then report improvements, regressions, or tradeoffs.
Audit sensitive code and dependencies for security weaknesses, then verify fixes with tests and repeat checks. Reports identify severity, file locations, and remediation steps for issues involving authentication, data protection, injection attacks, and configuration.
Search biomedical literature in PubMed using combined conditions and MeSH, its standardized subject terms. Build search strategies, retrieve abstracts and citation records through the REST API, and export results for reference management or analysis.
Choose the appropriate NVSHMEM performance workflow for collecting benchmark evidence, selecting communication between nodes, assigning network adapters to processing elements, or assessing TMA transfers between peer GPUs. This skill routes requests to specialists that perform the technical work.
Generate videos from DEFT media samples using PAIDF Cosmos Predict. Prepare configuration, run generation in Docker, avoid repeated processing of duplicate media paths, and produce JSONL records linking each original sample ID to successful or missing video outputs.
Reactive streams and event-driven programming in Golang using samber/ro — ReactiveX implementation with 150+ type-safe operators, cold/hot observables, 5 subject types (Publish, Behavior, Replay, Async, Unicast), declarative pipelines via Pipe, 40+ plugins (HTTP, cron, fsnotify, JSON, logging), automatic backpressure, error propagation, and Go context integration. Apply when using or adopting samber/ro, when the codebase imports github.com/samber/ro, or when building asynchronous event-driven pipelines, real-time data processing, streams, or reactive architectures in Go. Not for finite slice transforms (→ See `samber/cc-skills-golang@golang-samber-lo` skill).
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Extract text, tables, and structured data from documents using Azure Document Intelligence (@azure-rest/ai-document-intelligence). Use when processing invoices, receipts, IDs, forms, or building custom document models.
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Azure Event Hubs library for Rust. Send and receive events for streaming data ingestion and batch processing. Triggers: "event hubs rust", "ProducerClient rust", "ConsumerClient rust", "send event rust", "streaming rust", "eventhub rust".
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Azure AI Document Intelligence SDK for .NET. Extract text, tables, and structured data from documents using prebuilt and custom models. Use for invoice processing, receipt extraction, ID document analysis, and custom document models. Triggers: "Document Intelligence", "DocumentIntelligenceClient", "form recognizer", "invoice extraction", "receipt OCR", "document analysis .NET".
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Taxonomic classification of ASVs using reference databases like SILVA, GTDB, or UNITE. Covers naive Bayes classifiers (DADA2, IDTAXA) and exact matching approaches. Use when assigning taxonomy to ASVs after DADA2 amplicon processing.
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