Data Analysis Claude Skills
2 409 data analysis skills from 443 authors. They crunch numbers, pivot tables and turn both into something readable. Half of them fit into 1 952 tokens or less — that is what one costs your context window when the agent loads it. 549 ship runnable scripts rather than instructions alone. 31 of them cannot work without an MCP server, most often rube. We also found 386 copies of these same skills sitting in other people's repositories — counted once here, not 386 times.
2 409 unique 443 authors 5 updated this month 241 from vendors
Generate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.
Use when the user asks why a reported NVFLARE job failure signal occurred: the job failed, stalled, timed out, lost clients, ended with EXECUTION_EXCEPTION, or produced suspicious errors. Diagnose in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions.
Shared NVFLARE conversion references and templates used by the other NVFLARE agent skills (conversion workflow, validation ladder, dependency install, model exchange, metrics/artifact reporting, and the custom aggregator template). Not a user-triggered skill; loaded via references from the conversion skills.
Triage TestFlight crashes, beta feedback, and performance diagnostics using asc. Use when the user asks about TF crashes, TestFlight crash reports, beta tester feedback, app hangs, disk writes, launch diagnostics, or wants a crash summary for a build or app.
>- Create a visual Jira sprint dashboard from Jira project, space, sprint, board, filter, JQL, work item keys, or Jira URL data. Use when the user asks for a Jira sprint dashboard, standup dashboard, sprint review, delivery review, engineering manager dashboard, WIP review, planning view, closeout view, or a visual snapshot of Jira work that is more useful than a flat report. Use the richest dashboard format supported by the current agent, such as Cursor Canvas, an interactive artifact, HTML, or Markdown.
> Answer natural-language business questions over existing Power BI reports and dashboards through the FabricIQ MCP endpoint. Orchestrates artifact discovery, schema inspection, entity resolution, DAX generation, and query execution to return plain-language answers. Use when the user asks what, which, compare, rank, explain, or summarize questions about Power BI report or dashboard content. "dashboard data", "what are the top", "show me the power bi data", "which products sold", "compare sales in report", "which customers churned", "ask the Power BI report".
Onboard Azure Monitor / Application Insights observability data into Microsoft Fabric and guide business-impact insights by correlating telemetry with business data, Eventhouse external delta tables, verified schemas, an optional Real-Time (KQL) dashboard, and opt-in Operations Agent instructions. Triggers: onboard Azure Monitor into Fabric, correlate App Insights telemetry with business data, build a Real-Time KQL dashboard over telemetry, build an Operations Agent for business-impact alerting, determine if availability or latency impacted bookings orders or revenue, connect a Log Analytics workspace to Fabric.
> Port Azure Synapse Analytics Spark workloads to Microsoft Fabric. Translates mssparkutils calls to notebookutils (including the env→runtime namespace change), replaces Linked Services with Fabric Data Connections and OneLake Shortcuts. Covers Spark Pools, Lake Databases, Notebooks, and Spark Job Definitions. (1) port Synapse Spark notebooks to Fabric Lakehouse or Spark Job Definitions, (2) replace mssparkutils or Linked Services in Synapse code. "synapse linked service replacement", "port synapse notebooks", "synapse workspace migration".
>- Create and modify Power BI report files in PBIR/PBIP format using the `powerbi-report-author` and `powerbi-desktop` CLIs. Use when the user wants pages, visuals, filters, slicers, bookmarks, themes, or formatting, (3) validate PBIR and verify rendering in Power BI Desktop. For open-ended visual design, use `powerbi-report-design` first. For end-to-end requirements and "create Power BI report page", "add visual to PBIP", "format report visual", "validate Power BI report", "reload Desktop screenshot", "implement an approved PBIP report spec", "edit PBIR pages/visuals".
>- Build a guided requirements-to-implementation workflow for new Power BI reports and dashboards from semantic models, datasets, or PBIP projects. Use scope, page plan, design direction, dependencies, and delivery target, (3) create a locked report spec with approval before PBIR authoring. For direct edits to existing report files, use `powerbi-report-authoring`. For design-only dashboard", "create a new report", "plan then implement", "define and build Power BI report", "walk me through creating a report".
Investigate a Helion benchmark dashboard regression (helionlang.com/dashboard) — find the cause and classify it. Auto-activate when the user reports a perf drop/spike on a dashboard platform (e.g. b200 cute) around a given date.
> Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering. Use when the user provides CSV, Parquet, pandas, or database event data containing user, event, and timestamp columns, or asks why users convert, churn, loop, abandon a flow, or follow particular product paths. Do not use for qualitative journey-mapping workshops or aggregate website traffic without user-level event sequences.
Builds and edits Simulink, System Composer, Stateflow, and Simscape models. Use when modifying model structure, parameters, ports, connections, or Stateflow chart internals.
> Synthesize new waveforms from scratch for Simulink inports using createInputDataset. Use ONLY when the user asks to generate, create, or synthesize signals (step, ramp, sine, chirp, pulse, noise) to populate a Dataset for External Inputs or Signal Editor. Covers timeseries and timetable formats with correct data type, interpolation, units, and dimensions. Also covers function-call and trigger inport timing setup. Do NOT use when the user wants to load, import, or read existing data from files (MAT, CSV, spreadsheet) — even if that data will be used as model input. Do NOT use for running simulations or plotting outputs.
Create, populate, and manage safety analysis spreadsheets (FMEA, FHA, HARA, custom) and fault trees (FTA) in Safety Analysis Manager. Use when the user asks to perform FMEA, hazard analysis, fault tree analysis, safety analysis, or work with Safety Analysis Manager documents. Requires Simulink Fault Analyzer.
> Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor dashboard — cache hit rate (total_cache_read / (total_cache_read + total_input)), cache_write vs cache_read reuse, cache-read vs cache-write spend, and the sessions with the poorest reuse. Pulls token totals from /api/analytics, per-session detail from /api/sessions, and dollar splits from /api/pricing/cost. Use when diagnosing cache spend or deciding whether prompt caching is paying off.
> Break down Claude Code usage by model family (Opus / Sonnet / Haiku) from the Agent Monitor dashboard — each family's share of tokens, share of cost, and the spots where an expensive model is doing cheap work. Pulls per-model token and cost splits from /api/pricing/cost, current rates from /api/pricing, fleet token totals from /api/analytics, and per-session model assignment from /api/sessions. Use when deciding model routing or whether to downshift work to a cheaper tier.
> Calculate a productivity score using actual Agent Monitor metrics — session completion rates, cache efficiency (cache_read vs input), compaction pressure (baseline tokens), turn velocity (turn_count / total_turn_duration_ms), tool success ratio (PreToolUse vs PostToolUse), and the workflow intelligence API's complexity and effectiveness scores.
> Define a spend budget for Claude Code and, optionally, create a cost alert rule that fires when usage crosses the limit, via POST /api/alerts/rules on the Agent Monitor dashboard. Reads current spend from /api/pricing/cost to size the budget sensibly and explains every rule field before writing. Use when setting a spend limit or wiring up a budget guardrail.
> Review the configured cost alert rules and the alerts currently fired on the Agent Monitor dashboard, then explain exactly what tripped and why. Uses /api/alerts (fired feed) and /api/alerts/rules (definitions). Use when checking spend alerts or asking why a cost alarm went off.
> Run a daily spend check-in against a daily budget on the Agent Monitor dashboard — today's spend vs the daily target, pace through the day, and the projected overage if the current pace holds. Uses /api/pricing/cost and /api/sessions (grouped by started_at = today). Use for a quick daily spend check-in or a morning/evening budget pulse.
> Forecast Claude Code spend to the end of the week or month from the daily session trend on the Agent Monitor dashboard — moving average of daily spend × days remaining, added to spend-to-date. Uses /api/analytics daily_sessions, /api/pricing/cost, and /api/sessions for a per-day cost curve. Use when projecting cost or asking "where will my spend land".
> Quick dashboard health and status overview — checks the Agent Monitor API (port 4820), reports session/agent/event counts from /api/stats, confirms WebSocket connectivity, validates hook configuration in ~/.claude/settings.json, and shows data freshness (last event timestamp). Use to verify the monitoring system is operational.
> Probes each major Agent Monitor API route — /api/stats, /api/analytics, /api/sessions, /api/pricing/cost, /api/workflows/runs, /api/cc-config/overview — and reports each one's HTTP status, latency, and response shape, flagging which are reachable. Use to verify a dashboard install is wired up correctly.
> Polls the Agent Monitor /api/stats endpoint several times over a short window and reports the live deltas in active_sessions, active_agents, events_today, and ws_connections so you can see activity moving in real time. Use when watching the dashboard for live changes rather than a one-time snapshot.
> One-line summary of key Agent Monitor metrics — active sessions, total cost from the pricing engine, events today from daily_events, top tool from tool_usage, and current model from the most recent session. Use for a fast at-a-glance check without leaving the terminal.
> Export Claude Code session and analytics data in JSON, CSV, or Markdown formats. Supports exporting sessions, events, costs, and analytics for external analysis or reporting. Use for data backup or integration.
> Run comprehensive health checks on the Claude Code Agent Monitor system. Validates dashboard API, database, WebSocket, hooks, and disk usage. Use to verify the monitoring setup is working correctly.
> Diagnose Claude Code hook installation, delivery, and ingestion issues. Checks hook configuration, connectivity, event flow, and identifies common problems. Use when events are not appearing in the dashboard.
> Detect quality and efficiency regressions over time using Agent Monitor data — rising error rate (APIError events), falling cache hit rate, growing compaction frequency, and climbing cost-per-session. Splits history into an earlier baseline window and a recent window and reports which metrics are getting worse, by how much, and where. Use when checking whether things are degrading or trending in the wrong direction.
> Generate a daily standup summary from recent Claude Code sessions — completed work grouped by project (cwd), session costs from the pricing engine, tool invocations, error/compaction/APIError events, and turn velocity metrics from session metadata (turn_count, total_turn_duration_ms).
> Compile a month-over-month retrospective from Agent Monitor data — sessions, cost, token volumes, completion rate, top projects by working directory, and notable shifts versus the prior month. Uses daily_sessions/daily_events (365d) from analytics, the session list, and the pricing cost breakdown. Use when doing a monthly retrospective or planning the month ahead.
> Discover when you are most active and most productive with Claude Code by bucketing sessions and events into hour-of-day and day-of-week bins from their timestamps, then flagging peak versus low-output windows. Uses the session list, per-session events, and analytics daily trends. Use when planning a schedule or deciding when to do deep work versus lighter tasks.
> Compile a weekly productivity report using Agent Monitor data — daily_sessions and daily_events trends, per-session costs from pricing engine, token volumes (input/output/cache_read/cache_write + baselines), tool usage top 20, session completion rates by status, and workflow intelligence metrics.
> Produce a detailed report on APIError events from Agent Monitor data — counts over time, which sessions and models are affected, and the likely root cause (rate limits, overload/529, or context-window pressure) inferred from each event's summary and data payload. Use when API errors spike or when you need to explain why requests are failing.
> Report concurrency and parallelism for a session — how many agents ran in parallel, concurrency-lane utilization, peak parallel width, and serialization bottlenecks (sequential chains that could have run as parallel lanes) — using the Agent Monitor workflow intelligence API. Use when checking whether a multi-agent session used parallelism efficiently.
Use when the user wants to train, compare, or export machine learning models using Classification Learner or Regression Learner — including opening the app, loading data, training models, evaluating metrics, comparing results, visualizing plots, testing on held-out data, exploring model interpretability, and exporting trained models. Programmatic access to Classification Learner and Regression Learner apps via AppController.
Import recorded driving sensor data (GPS, camera, lidar, actor tracks, lanes) into scenariobuilder.* objects (GPSData, CameraData, LidarData, ActorTrackData, Trajectory, laneData) and run preprocessing — synchronize, offset correction, crop, normalizeTimestamps, convertTimestamps. Also: compute actor tracks from lidar when no annotations exist, attach camera/lidar mounting + intrinsics, export to MAT/workspace/timetable/script. Use for raw driving dataset files (KITTI, nuScenes, Waymo, Pandaset, ROS/ROS2 bags, .mat, .csv, .mp4) or driving/vehicle/sensor logs that need wrapping. drivingLogAnalyzer (DLA) is OPT-IN ONLY — invoke only on explicit user request ('DLA', 'open in DLA', 'inspect/explore/analyze the recording') or reported sensor problem (sync drift, timestamp mismatch, overlay misalignment). NEVER auto-launch DLA after wrapping (Rule 0). For 'build scenario / export to RoadRunner / drivingScenario / OpenSCENARIO / Unreal / simulate', hand off to matlab-scenario-builder.
Fit SimBiology model parameters to data — fitproblem, population NLME, virtual patients, and NCA. Use when asked to fit, estimate, calibrate, or compute PK metrics.
> Guide for accessing financial and economic data in MATLAB using the Datafeed Toolbox. Covers Bloomberg (market data via bloomberg/blp/bloombergHypermedia), FRED (Federal Reserve economic data via fredrs), and Haver Analytics (economic data via haver/haverdirect/haverview). Use when connecting to any of these data providers from MATLAB.
Exchange data between Excel and MATLAB using Spreadsheet Link VBA macros and worksheet functions. Use when writing Excel VBA macros that call MLPutMatrix, MLGetMatrix, MLPutVar, MLGetVar, MLPutRanges, MLEvalString, MLGetFigure, or MatlabRequest.
> Extract battery features for degradation analysis and health monitoring in MATLAB. Covers cycling test features, differential curves (IC/DV/DT), and measurement statistics. Use when working with battery cycling data, SOH estimation, RUL prediction, or any battery test data analysis in MATLAB. Triggers on battery* functions such as batteryTestDataParser, batteryTestFeatureExtractor, batteryMeasurementFeatures, batteryDifferentialCurves.
> End-to-end finite element analysis in MATLAB PDE Toolbox — geometry creation, model setup, solve, and post-processing in one skill. Use when building geometry from primitives or file import, setting up femodel with BCs/loads/materials, solving thermal/structural/EM problems, and extracting or visualizing results. Covers fegeometry, multicuboid, multicylinder, multisphere, decsg, boolean ops, mesh generation, femodel, all AnalysisTypes (thermalSteady, thermalTransient, structuralStatic, structuralTransient, structuralModal, structuralFrequency, electrostatic, magnetostatic, dcConduction, harmonic EM), materialProperties, faceBC, faceLoad, cellLoad, vertexLoad, solve, interpolation, von Mises stress, principal stress, reaction forces, heat flux, pdeplot3D visualization. analysis, electromagnetic analysis, femodel, mesh, boundary conditions, stress, displacement, heat transfer, post-processing.
> Build MATLAB apps from requirements to working code. Asks discovery questions (or skips them when the path is known), recommends UIFigure or UIHTML architecture, identifies layout archetype (Dashboard, Explorer, Tabbed, Wizard, Canvas), produces an implementation plan, and executes the build. For UIFigure apps, optionally serializes as App Designer (.mlapp or plain-text .m + .xml). Use when a user wants to build a MATLAB app, create a GUI, make an interactive tool, build a uifigure app, build a uihtml app, build an App Designer app, build a .mlapp app, build a plain-text App Designer app, or asks which approach panel, sidebar, tabs, wizard, stepper, canvas, workspace.
> Create and customize MATLAB charts and plots. Plot types (line, scatter, bar, histogram, heatmap, surface), axes configuration, annotations, data tips, interactive plots, animation, multiple axes with tiledlayout, colororder, and performance optimization. Works for standalone figures, Live Scripts, and uifigure apps. Use when plotting data, customizing axes, adding annotations or interactivity, uiaxes, annotation, data tips, animation, tiledlayout, colororder, heatmap, scatter, figure, visualization, export.
> Guide users or agents to the correct MATLAB tool for processing large tabular data in file-based formats (CSV, Parquet, delimited text, spreadsheets, MDF) that may not fit in memory. Use when a user or agent mentions large files, big data, out-of-memory errors, OOM, scaling up, tall arrays, datastores, or needs to process multiple tabular files. Covers the decision between datastore + tall, datastore + transform, and parallel execution. Also use when a user or agent has working in-memory code (readtable, parquetread) that runs out of memory and needs a migration path. Also covers building custom datastore classes for proprietary or non-standard formats — use when the task requires subclassing matlab.io.Datastore, implementing a custom reader, building an extensible datastore, or integrating a new file format with tall arrays or parallel computing. Do NOT use for MAT files (use matfile instead).
> Read or write data files in MATLAB. Use when the task involves tables, spreadsheets, delimited text, or structured files in CSV, Excel, Parquet, JSON, or XML format — including but not limited to importing, exporting, loading, parsing, converting, validating, configuring import options, reading from URLs, handling locales or encodings, diagnosing file errors, and modernizing legacy file I/O code. MATLAB provides built-in functions for these workflows with no additional products required.
> Speed up local parfor, parfeval, or spmd by switching to a thread-based parallel pool. Trigger when a user describes slow or disappointing local on a laptop/workstation is slower than expected or "only slightly faster than for"; parfor scales poorly with the number of workers; ticBytes/tocBytes, the Parallel Pool dashboard, mpiprofile, or system tools show large per-worker data transfer; large broadcast variables or sliced inputs make iterations slow; opening a process pool dominates a short workload; user mentions serialisation or data transfer overhead. Also trigger on any question about whether code or a function works on a thread pool. For non-pool MATLAB performance work (vectorisation, preallocation, profiling), defer to matlab-optimize-performance.