201 skills · sorted by relevance
Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.
Configure Google Cloud logs across multiple projects, either by storing them centrally or querying them together where they already reside. The skill guides routing, access permissions, and log views, and helps diagnose missing logs and permission failures.
Review BigQuery capacity, table layouts, storage retention, and SQL to improve cost efficiency. Compare billing options, plan partitioned tables, and evaluate search indexes, stored aggregation results, and BI Engine for suitable workloads.
Write, debug, and optimize SQL queries for data analysis and database design. The skill covers aggregations, rankings, missing values, and indexes, using execution plans to understand performance and adapting syntax to the database in use.
Answer business data questions with Wren CLI by checking model definitions, reusing past queries, and executing SQL or predefined aggregations. The workflow also covers query verification, error diagnosis, and saving successful queries for future use.
Check a paper’s numbers, comparisons, and evaluation scope against raw experiment files. A fresh reviewer receives only source evidence and paper files, then reports discrepancies in rounding, aggregation, settings, and reported improvements.
Check paper numbers, comparisons, and evaluation scope against raw experiment results. A fresh reviewer from a different model family examines only source evidence, then produces reports identifying rounding issues, inconsistent settings, aggregation errors, and unsupported claims.
Build Apache Spark pipelines for large datasets, including transformations, aggregations, and streaming analysis. Get PySpark or Scala code, partitioning strategies for dividing data across workers, and configuration advice to investigate slow jobs and resource usage.
Investigate slow SQL, write complex joins and summaries, and design database structures. Uses execution plans, which show how queries run, to guide index choices and query rewrites, with before-and-after performance measurements.
Audit how advertising platforms, GA4, and app measurement tools assign credit for conversions. Compare event definitions, reporting windows, and duplicate handling to explain discrepancies and produce an evidence-based measurement improvement plan.
Translates Splunk SPL queries into Axiom APL, the query language used to search and analyze data in Axiom. Covers command and function equivalents, time filters, type conversions, and common patterns for migration or learning.
Diagnose Python spreadsheet failures through staged checks of the interpreter, library imports, file access, and basic operations. Minimal test scripts help separate environment problems from errors in the full processing script.
Evaluate older Wi-Fi clients and embedded devices for KRACK and FragAttacks vulnerabilities. Covers test suites, likely patch status, practical impact, and reporting findings by device, firmware version, and vulnerability identifier.
Design charts and dashboards that show data distributions, uncertainty, missing values, and sample sizes clearly. Choose visual forms that preserve meaningful variation and explain where averages, aggregation, or interval choices could mislead readers.
Write JavaScript for n8n Code nodes to aggregate, filter, and transform workflow data. Guidance covers data access, output formats, execution modes, common errors, and performance choices when processing large numbers of records.
Manage a project’s execution plan and lifecycle across multiple agents. Track task dependencies or repeated work cycles, identify tasks ready to start, handle pauses and completion, and prepare project status reports and final results.
Organize Excel analysis from reading multiple sheets through cleaning, filtering, aggregation, and export. Choose processing methods based on row counts, and use supporting skills to produce tables, charts, and Excel or CSV reports.
Analyze an organization's sub-transaction patterns in TRES Finance, identify groups of repetitive transactions, and propose data-driven rollup rules that consolidate them into one aggregated entry per interval (daily or monthly) for cleaner ERP integration. Also lists and deletes rollup rules. Trigger this skill whenever the user asks about rollup rules, transaction aggregation, consolidating transactions, reducing transaction noise, rolling up gas fees, rolling up staking rewards, rolling up micro-transactions, or collapsing many small ledger entries into fewer summary entries. Also trigger on "create a rollup rule", "suggest rollup rules", "what should I roll up", "show my rollup rules", "delete a rollup rule", "aggregate transactions", "too many transactions", "reduce transaction count", "roll up fees", "daily rollup", "monthly rollup". If a wallet has too many transactions, high-frequency activity, or noisy ledger entries, proactively suggest rollup rules. For computing the EXACT impact of a single already-specified rule or reviewing submitted/pending rules, use the tres-rollup-review skill instead.
The English summary is being prepared. Showing the original description.
Processes and analyzes data with resident-kernel engines (DuckDB, Polars) and one-shot tools. Use for CSV/parquet/JSON analysis, group-by/join/aggregation, time series, distributions, cleaning, or plotting a dataset.
The English summary is being prepared. Showing the original description.
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
The English summary is being prepared. Showing the original description.
Set up the public CodonFM v1 repository and download public Encodon checkpoints. Use for requests to build or launch the CodonFM development container, configure local data/checkpoint mounts, verify GPU access, or download public Encodon 80M, 600M, 1B, or Cdwt-1B weights. Do not use for Decodon, Encodon 5B/10B, missense-aggregation, or codon-optimization setup because those implementations are not in the public repository.
The English summary is being prepared. Showing the original description.
Operate NVIDIA DOCA Management Service on a BlueField, Arm/x86 host, or Kubernetes pod: from DOCA 3.5 the local `dms-cli` (one command per YANG path, no server), and the `dmsd` + `dmspe` service: choose deployment and authentication, configure `-allowed_users` and `dmsgroup`, use gNMI Get/Set/Subscribe, run supported gNOI workflows, and debug frontend/backend failures. Trigger even without "DMS" for "manage a remote BlueField over gRPC", "gNOI reboot from orchestrator", or fleet-management requests. SAFETY: reboot, OS install, factory-reset, and managed-file deletion are destructive and require target-bound explicit confirmation; never invoke them speculatively. Route installation and library/API build questions elsewhere, and route turnkey aggregation to the externally-productized DOCA Telemetry Service.
The English summary is being prepared. Showing the original description.
Idiomatic Golang error handling — creation, wrapping with %w, errors.Is/As, errors.Join, custom error types, sentinel errors, panic/recover, the single handling rule, structured logging with slog, HTTP request logging middleware, and samber/oops for production errors. Built to make logs usable at scale with log aggregation 3rd-party tools. Apply when creating, wrapping, inspecting, or logging errors in Go code. For samber/oops specifics → See `samber/cc-skills-golang@golang-samber-oops` skill; for slog handler ecosystem → See `samber/cc-skills-golang@golang-samber-slog` skill.
The English summary is being prepared. Showing the original description.
Query the Genome Aggregation Database (gnomAD). Use when determining the rarity or allele frequency of specific genetic variants, retrieving gene constraint metrics (pLI, LOEUF) to assess loss-of-function intolerance, finding variants in a genomic region or gene, or querying structural variants. Don't use for analyzing individual patient genomes, tracking somatic mutations in cancer (use COSMIC), or requesting raw sequencing reads (use ENA).
The English summary is being prepared. Showing the original description.