Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 86 961 files from 2 125 authors, of which 68 678 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
Audit and prioritize performance work through bounded-work and forward-progress checks. Use when asked to find performance issues, investigate freezes/500s/OOMs, review retries or polling for no-progress loops, rank a performance backlog, or implement the next simple performance fixes.
Explain a technical spec or proposed change in plain language: what problem it solves, how the solution works, and every schema change.
Guide the user through a quadrant walk that maps the unknowns of a task — open by listing the known knowns, then work through known unknowns, unknown knowns, and unknown unknowns one stage at a time, ending with a complete four-quadrant map in the user's hands. Use when a request is ambiguous or underspecified, the codebase or domain is unfamiliar, the user will "know it when they see it", a reference implementation must be understood before porting, mid-build deviations from the plan need capturing, or a finished change needs buy-in or verified understanding before merge. Pairs with [write-spec](../write-spec/SKILL.md) — walk the quadrants to burn off fog before slicing, and feed the finished map into the spec.
Implement an existing spec through committed passes. Use for long or multi-pass specs that need maintenance checkpoints to periodically clean code, handoffs, priorities, and plan bloat before drift accumulates.
Run [implement-spec](../implement-spec/SKILL.md) with Codex doing the implementation passes while you orchestrate, integrate, and review. Use when the user asks to implement a spec with Codex, or to run implement-spec with Codex as the implementer.
Use the local Codex CLI as an independent second agent. Two branches — (1) proactively run `codex review` for a second opinion after completing a substantive change, before presenting it as done or committing; (2) delegate a well-defined implementation task via `codex exec`, ONLY when the user explicitly asks for Codex to do it. Also covers how to prompt Codex.
Refactor cleanly instead of layering sediment. Use when a change reveals duplicated concepts, local adapters, obsolete owners, compatibility wrappers, parallel abstractions, an over-large module that has accreted many responsibilities, or "just tack this on" pressure in any code area.
Best-practice rulebook for a marketing site's pages, organized by page class — campaign landers, product pages, programmatic SEO trees, editorial. Use when writing or updating a landing/marketing page, wiring a campaign lander to an attribution identifier, changing site-wide nav/footer links, touching sitemap/robots/agent-readable surfaces, or auditing a public site's indexing, reachability, and conversion setup.
Closeout pass on a finished substantive change — sequence refactor-clean (shape), code-review (diff), and write-docs (docs) so all three run, in the order where each feeds the next. Use after implementing a slice or feature and before calling it done or committing; when the user asks for a final review, a cleanup-and-document pass, or to "review everything." For a diff-only audit, reach code-review directly.
Write and edit project docs (README/markdown) as a glossary of principles, not a mirror of the code. Use when creating or revising a README; when a doc enumerates exact scenes, scenarios, helpers, class ids, file lists, or command/flag matrices the code already holds; when trimming narrative or changelog out of a doc; or when deduplicating overlapping docs and wiring a root doc to its sub-docs.
Break large features into independently verifiable, human-reviewable slices under specs/<feature>/. Use for risky or multi-step feature work that needs upfront questioning, API seams, browser-playable checkpoints, HTML visualizations, screenshot gates, staged implementation plans, recursive fog-of-war reslicing, or proactive research into reference implementations/best practices before slicing. Pairs with your project's verification harness and screenshot gates (the browser checkpoints), [refactor-clean](../refactor-clean/SKILL.md) (review the materialized spec so the plan describes one-owner architecture, not the feature bolted on), [screenshot-critique](../../visual/screenshot-critique/SKILL.md) and [compare-screenshots](../../visual/compare-screenshots/SKILL.md) (the visual gates), and a code-review pass (audit each slice before it lands).
Compare screenshots to judge which image is less wrong, not to match a baseline. Use when a UI, game, document, render, chart, or generated asset needs objective visual telemetry, side-by-side inspection, crop/zoom review, or a fresh second opinion before accepting or rejecting a visual change. Also use on a single capture with no counterpart, to measure whether the frame is flat, empty, or badly framed before anyone reviews it.
Build, debug, or review WebGPU renderer work — three.js/TSL scene layers, node materials, or raw WGSL passes and compute. Use when changing GPU resource layouts, render or compute passes, node materials, bind groups, buffers, shaders, frame orchestration, depth/overlay composition, capability handling, performance, or browser-verified renderer visuals.
Write tests that pin real behavior instead of implementation details, config values, or lucky samples. Use when adding tests for new behavior, writing a regression test, fixing a brittle or flaky test, reviewing a test diff, or when a test breaks after a refactor or config change that didn't change behavior.
Use the unprimed sub agent as a second set of eyes before accepting visual work — MANDATORY before declaring any user-reported visual bug fixed or claiming a visual change verified; primed eyes pass defects fresh eyes catch.
Open image shots (screenshots, snapshot baselines, montages, renders) in ONE macOS Preview window so the user can eyeball them. Use when the user asks to open / show / pull up / review shots in Preview or Finder, or right after producing visual artifacts the user should look at before signing off.
> Plan and implement end-to-end Microsoft Fabric data platforms and Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns using PySpark, Delta Lake, Lakehouse/Warehouse items, Fabric Pipelines, and semantic-model handoff. (3) set up multi-layer workspaces with lakehouses for each tier, (4) build ingestion-to-analytics pipelines with data quality enforcement, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. For natural-language business questions over existing Power BI report data, use `fabriciq`. "bronze silver gold", "lakehouse layers", "e2e data pipeline", "end-to-end lakehouse", "data lakehouse pattern", "multi-layer lakehouse", "build medallion", "setup medallion", "end-to-end Fabric data platform", "bronze silver gold platform", "ingestion semantic model platform".
> Author Fabric Activator rules and Reflex items through Fabric REST API and `az rest`. configure thresholds, filters, Teams/email notifications, Fabric item actions, and Eventhouse/Eventstream/Real-Time Hub/DTB/Ontology/Power BI sources. Pure GET/explain prompts belong to `activator-consumption-cli`. Clarification for missing sources, thresholds, recipients, and action targets happens inside this skill. After another data skill finds a timely operational signal such as a spike, failure, anomaly, SLA risk, or capacity constraint, proactively ask whether the user wants an alert for future occurrences. "create an activator item", "create an alert item", "notify me when", "let me know when", "take action when", "send me an email when", "send a teams message when", "run a pipeline when", "update an alert", "delete an alert", "activator rule"
> Execute KQL management commands (table management, ingestion, policies, functions, materialized views) against Fabric Eventhouse and KQL Databases via CLI. 1. Create or alter KQL tables, columns, or functions 2. Ingest data into an Eventhouse (inline, from storage, streaming) 3. Configure retention, caching, or partitioning policies 4. Create or manage materialized views and update policies 5. Manage data mappings for ingestion pipelines 6. Deploy KQL schema via scripts "kql function", "materialized view", "kql retention policy", "eventhouse schema", "kql authoring", "create eventhouse table", "kql mapping"
Create and evolve Fabric IQ Ontology (preview) items from CLI — define entity types, properties (including timeseries), relationship types, and bind them to OneLake lakehouse tables (static + timeseries) or Eventhouse / KQL database tables (timeseries only). Uses the Fabric item-definition REST API (Create Item / Update Item Definition) with `InlineBase64` parts. Use to create a Fabric Ontology item; add or alter entity types, properties, or keys; add timeseries properties and bindings; bind an entity type to a lakehouse or Eventhouse table; add relationship types and contextualizations; or script ontology deployment from source. Triggers: "create fabric ontology", "add ontology entity type", "bind entity type to lakehouse", "bind entity type to eventhouse", "ontology timeseries binding", "add ontology relationship type", "ontology contextualization", "fabric iq ontology authoring", "update ontology definition
> Create, update, delete, and refresh Fabric Dataflows Gen2 with write-side CLI via Fabric APIs. Build mashup.pq and queryMetadata.json, preview candidate M with executeQuery/customMashupDocument, bind connections, and configure output destinations. For saved query execution or refresh-status reads, use `dataflows-consumption-cli`. If a request explicitly insists on the Dataflows consumption or read-only path for a mutation, do not route here; let consumption refuse before any dataflow", "delete dataflow", "trigger dataflow refresh", "preview Power Query M", "preview before save", "customMashupDocument", "create Fabric data source connection", "create SQL Server source REST", "POST /v1/connections", "supportedConnectionTypes", "passwordReference", "bind connection", "dataflow output destination", "dataflow write to lakehouse", "dataflow write to warehouse", "dataflow write to ADX", "DataDestinations annotation".
> Create, wire, and publish Fabric Eventstream real-time streaming topologies via the Items REST API. Build definitions with 25 source types (Event Hubs, IoT Hub, CDC, Kafka, SampleData), 8 operators (Filter, Aggregate, GroupBy, Join, ManageFields, Union, Expand, SQL), 4 destinations (Lakehouse, Eventhouse, Activator, Custom Endpoint), DefaultStream/DerivedStream routing. **Invoke this filter operator, (4) add CDC source with Debezium flattening, (5) wire destinations, (6) modify/delete Eventstream definitions. Invoke before making "eventstream topology", "add source to eventstream", "add event hub source", "add filter operator", "eventstream filter", "eventstream destination", "CDC source", "eventstream operator", "eventstream definition", "update eventstream", "wire eventstream", "real-time ingestion pipeline", "eventstream topology deployment".
> Check an installed skills-for-fabric plugin bundle or git clone for updates, show the matching changelog, and provide host-appropriate update guidance. date", "what version", "update skills", "show changelog".
> Copy, clone, duplicate, rebind, and execute Gen1 dataflow save-as upgrade operations via CLI (az rest / curl) against Power BI REST and Fabric REST APIs. Covers Gen1 to Gen2.1 upgrade save-as, cross-workspace Gen1-to-Gen2.1 copy flows, connection rebinding, output changes, readiness snapshots, (2) assess save-as readiness, (3) upgrade Gen1 into Gen2.1, (4) create a Gen2.1 copy from a Gen1 dataflow in another workspace, (5) rebind connections or validate saved data. For creating/editing Gen2 dataflows from scratch, previewing candidate M, binding ordinary authoring connections, or copying Gen2 dataflows, use `dataflows-authoring-cli`. "saveAsNativeArtifact", "copy Gen1 dataflow", "duplicate Gen1 dataflow", "clone Gen1 dataflow", "rebind Gen1 dataflow connections".
> Does NOT author report visuals, manage workspaces, or manage RLS/OLS roles.
> Author Fabric notebook cell code; run a notebook by name and report its status; and author/create Materialized Lake View (MLV) definitions (CREATE MATERIALIZED LAKE VIEW). Use for writing notebook cell code (PySpark, Scala, SparkR, %%sql, %%configure) — a %%sql cell that queries a lakehouse is authoring, not a T-SQL query — and running a notebook via the Jobs API (RunNotebook), including on the success path. Not for Livy sessions or ad-hoc calculations (use `spark-consumption-cli`); to refresh/schedule an EXISTING MLV, use `mlv-operations-cli`; for plain T-SQL, use `sqldw-consumption-cli`; for a FAILED notebook/job, use `spark-operations-cli`. "run notebook", "execute notebook", "notebookutils", "PySpark notebook", "%%configure", "create a materialized lake view", "create MLV", "materialized lake view", "MLV", "CREATE MATERIALIZED LAKE VIEW", "MLV incremental refresh"
> Create and manage SQL database in Fabric items, author T-SQL DDL/DML with constraints, foreign keys, triggers, indexes, and vector columns. Deploy schema via SqlPackage (.dacpac/.bacpac), configure source control, CI/CD, and GraphQL APIs. "sql database in Fabric primary key default getdate sqlcmd", "create sqldb item", "create table sqldb ddl", "sqldb foreign key constraint", "sqldb stored procedure", "sqldb trigger create", "sqldb vector column", "SqlPackage dacpac deploy sqldb", "bacpac sqldb", "sqldb source control", "graphql api sqldb", "sqldb collation set", "sp_invoke_external_rest_endpoint".
> Monitor, inspect, and query saved Fabric Dataflows Gen2 with read-only CLI. List dataflows, decode mashup.pq/queryMetadata.json/.platform, inspect parameters, refresh status, job history, staging, and destinations, or run saved/ad-hoc read-only executeQuery requests and parse Arrow. Handle explicit requests to mutate through the Dataflows consumption or read-only path by refusing the write; offer `dataflows-authoring-cli` only after separate confirmation. For candidate M before persistence or connection configuration, use "decode dataflow definition", "dataflow parameters", "refresh history", "last refresh status", "dataflow job history", "execute dataflow query", "executeQuery saved query", "executeQuery fetch rows", "ad-hoc dataflow query", "parse Arrow response", "Arrow IPC", "dataflow staging analysis", "use Dataflows consumption path to delete", "Dataflows read-only mutation refusal", "separate Dataflows authoring handoff".
> Inspect existing alerts, notifications, and automated actions in Fabric via read-only REST calls using `az rest` CLI. **Invoke this skill** whenever (1) list existing alerts in a workspace, (2) inspect how an alert or notification is configured, (3) read and decode an Activator/Reflex definition (ReflexEntities.json), (4) list rules, sources, and actions behind an alert, (5) understand why an alert fires or what action it takes. **Invoke this skill before answering questions** about an Activator/Reflex item in a Fabric workspace — the listing, lookup, and decoding workflows are part of this skill, not preamble to it. "show me the rule", "show me the action", "show me the source", "get reflex definition", "list activators", "list alerts", "list reflex items", "show activator items", "activator details", "find activator named", "inspect Power BI source", "metric definition behind this alert"
> Execute authoring T-SQL (DDL, DML, data ingestion, transactions, schema changes) against Microsoft Fabric tables, (2) insert/update/delete/merge data, (3) run COPY INTO or OPENROWSET ingestion, (4) manage transactions or stored procedures, (5) perform schema evolution, (6) use time travel or snapshots, "create table in warehouse", "insert data via T-SQL", "load from ADLS", "COPY INTO", "run ETL with T-SQL", "alter warehouse table", "upsert with T-SQL", "merge into warehouse", "create T-SQL procedure", "warehouse time travel", "recover deleted warehouse data", "create warehouse schema", "deploy warehouse", "transaction conflict", "snapshot isolation error".
> Run KQL queries against Fabric Eventhouse for real-time intelligence and time-series analytics using `az rest` against the Kusto REST API. Covers KQL operators (where, summarize, join, render), Eventhouse schema discovery (.show tables), time-series patterns with bin(), and ingestion monitoring. 1. Run read-only KQL queries against an Eventhouse or KQL Database 2. Discover Eventhouse table schema and metadata 3. Analyse real-time or time-series data with KQL operators 4. Monitor ingestion health and active KQL queries 5. Export KQL results to JSON "real-time intelligence", "time-series kql", "query eventhouse", "explore eventhouse", "show tables kql"
> List, inspect, and monitor Fabric Eventstream real-time ingestion pipelines via the Items REST API. Discover Eventstreams across workspaces, decode base64 graph topologies tracing event flow from source through operators to destination nodes. Validate connection IDs, wiring, retention policies (1-90 days), and throughput levels. Retrieve Custom Endpoint Kafka credentials via Topology API. **Invoke sources and destinations, (3) validate Eventstream configurations, (4) check Eventstream retention policy and throughput level, (5) get connection strings. "describe eventstream topology", "eventstream operator nodes", "eventstream sources and destinations", "eventstream health", "eventstream status", "eventstream configuration", "eventstream retention", "eventstream throughput level", "eventstream connection string", "custom endpoint credentials", "check eventstream".
> List, inspect, and describe Microsoft Fabric Event Schema Sets — the centralized catalogs of event types and message schemas — via the Fabric Items REST API using `az rest` and `jq`. Enumerate Event Schema Sets in a workspace, read item properties (sensitivity label, tags), and retrieve then base64-decode the item definition to summarize its `eventTypes` and `schemas`. (2) inspect an Event Schema Set's properties, (3) decode a definition to enumerate its event types and message schemas, (4) verify schema formats and versions. Read-only; no authoring skill exists yet, so for writes use the Fabric Event Schema Set authoring REST APIs, and for the Eventstream ingestion pipeline use `eventstream-consumption-cli`. "describe an event schema set", "decode event schema set definition", "enumerate event types and schemas in an event schema set".
> Explore Fabric IQ Ontology (preview) items (read-only) from the CLI to ground an agent before it queries data. Explore, describe, and summarize what an ontology exposes — its entity types, keys, relationships, and the bindings that map each concept onto a lakehouse or Eventhouse source — then route the underlying data query to the matching per-datasource consumption skill (eventhouse-consumption-cli, spark-consumption-cli, sqldw-consumption-cli). Read-only discovery via Get Item Definition; never writes to or alters an ontology. Use to explore or summarize an ontology, describe its schema and data lineage, build agent grounding context, or run an ontology-backed query over the source records. "enumerate ontology entity types", "describe ontology", "ontology grounding context", "ground query with ontology", "query ontology entity data", "fabric iq ontology consumption", "ontology-backed query", "ontology entity bindings"
> Search the Microsoft Fabric catalog across workspaces using the Fabric Catalog Search API. (2) list or discover items of a specific type across the tenant, (3) identify which workspace contains an item, (4) return item/workspace IDs for downstream API calls. Dataflow Gen1/Gen2 items are not supported. For data queries after the item is known, use the workload-specific consumption skill (`sqldw-consumption-cli`, `spark-consumption-cli`, `eventhouse-consumption-cli`, or `fabriciq`). "cross workspace catalog", "tenant catalog discovery", "tenant catalog inventory", "tenant catalog identifiers", "workspace catalog discovery".
> Interactive ad-hoc Spark analysis through Fabric Lakehouse Livy API sessions ONLY. notebook-run-by-name (and reporting its run status) is `spark-authoring-cli`, not this skill. calculations over lakehouse data (DataFrames, cross-lakehouse joins, Delta time-travel, unstructured/JSON). For Spark failure triage use `spark-operations-cli`; for plain T-SQL Lakehouse/Warehouse queries use `sqldw-consumption-cli`. "PySpark", "analyze with PySpark", "Spark DataFrame", "lakehouse with Python", "PySpark data quality", "Delta time-travel with Spark", "join tables across Lakehouses".
> 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".
> and security policy inspection on the OLTP and SQL analytics endpoints. For schema changes see the sqldb-authoring-cli skill. "sql database in Fabric system view list user tables sqlcmd", "list user tables sqldb", "sys.tables sqldb", "explore sqldb schema", "vector similarity sqldb", "RAG embedding sqldb", "row level security sqldb inspect", "audit log sqldb inspect", "chat with sqldb", "export sqldb rows", "temporal as of sqldb", "json openrowset sqldb".
> Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via the MCP `fabric-sqlendpoint-execute_query` tool. Default skill for any lakehouse data query (row counts, SELECT, filtering, (1) query warehouse/lakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas/columns, (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV/JSON. For a Fabric notebook cell (%%sql or other notebook magics), use `spark-authoring-cli`, not this skill. "show lakehouse tables", "query lakehouse", "lakehouse table", "how many rows", "count rows", "SQL endpoint", "describe warehouse schema", "generate T-SQL script", "warehouse performance", "export SQL data", "connect to warehouse", "lakehouse data", "explore lakehouse".
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.
> Manage refresh schedules and job execution for an EXISTING Microsoft Fabric Materialized Lake daily, weekly), trigger on-demand refreshes, monitor job status, and cancel running jobs. Uses human-in-the-loop confirmations for safety. / CREATE OR REPLACE SQL is `spark-authoring-cli`, not this skill. "schedule materialized lake view refresh", "automate MLV refresh", "trigger MLV refresh", "monitor MLV refresh", "MLV job status", "cancel MLV refresh", "refresh schedule", "MLV automation", "refresh my materialized views"
> Diagnose SQL database in Fabric performance over TDS using Query Store, DMVs, sys.dm_db_resource_stats, and Extended Events on the OLTP endpoint. Identifies the top resource-consuming, slowest, or most expensive queries and handles query-performance ranking, blocking-chain, missing-index, and plan-regression diagnostics. For routine data queries use the sqldb-consumption-cli skill; for schema changes use the sqldb-authoring-cli skill. "top resource-consuming queries from query store sqldb", "slowest queries sql database in Fabric query store", "most expensive queries sqldb query store", "sql database in Fabric blocked sessions head blocker chain sqlcmd", "sqldb missing index recommendation", "sqldb regressed plan instability sqlcmd", "sqldb extended events trace".
> Analyze Fabric Data Warehouse performance via the MCP execute_query tool and queryinsights views. Diagnose slow queries, SQL pool pressure, cache coldness, and recommend clustering keys. "queryinsights long running", "warehouse CPU resource consumers", "SQL pool pressure window", "pressure events warehouse", "DW cache warmth cold start", "cache warmth analysis", "warehouse cluster key recommendation", "cluster tables performance", "DW performance baseline comparison", "performance degraded warehouse", "warehouse user query patterns", "queryinsights diagnostics", "DW optimization MCP".
> Diagnose failed Spark jobs, unhealthy Livy sessions, and performance bottlenecks in Microsoft Fabric via read-only CLI triage. Use ONLY for FAILED/unhealthy runs; running a notebook and reporting its success status is `spark-authoring-cli`. (2) triage stuck or dead Livy sessions, (3) identify OOM, shuffle spill, or data skew, (4) retrieve driver/executor logs or Spark Advisor findings, (5) copy event logs and start a local Spark History Server, (6) diagnose Spark activities in a failed pipeline run. "triage spark failure", "diagnose pipeline run failure", "why did my pipeline fail", "livy session stuck in starting", "spark executor OOM", "check spark advisor findings", "shuffle spill diagnosis", "why did my lakehouse job fail", "diagnose lakehouse table load", "data skew diagnosis", "open spark history server locally", "spark job triage".
> Port Databricks notebooks and jobs to Microsoft Fabric. Provides an exhaustive dbutils secret scope to Key Vault URL conversion, notebook run and exit, widget replacement with parameter-tagged cells, and library install replacement with Fabric Environments. Covers Unity Catalog three-level namespace reduction to Lakehouse two-level schemas, DBFS path conversion to OneLake, Databricks Jobs to Spark Job Definitions, MLflow tracking URI removal, and Photon to Native Execution Engine substitution. Use when the namespaces to Lakehouse schemas, (3) convert Databricks Jobs or Delta Live Tables. "dbutils fabric", "unity catalog migration", "dbfs to onelake", "databricks notebook migration", "delta live tables fabric", "photon native execution".
> Estimate Microsoft Fabric capacity costs before migration by analyzing existing workload profiles (Spark, SQL, Power BI, Real-Time Intelligence) and recommending optimal SKU sizing, (1) estimate Fabric costs for a migration, (2) size a Fabric capacity SKU, (3) compare Fabric pricing tiers, (4) calculate storage/compute/network costs, (5) recommend Reserved vs. Pay-As-You-Go vs. Autoscale billing. Trigger phrases are scoped to Fabric capacity cost/sizing to avoid over-routing on generic cost, migration, or connection questions. "fabric cost estimate", "fabric reserved instance", "fabric capacity pay-as-you-go", "fabric autoscale billing".
> Port Azure HDInsight Spark clusters and Hive workloads to Microsoft Fabric. Replaces legacy HiveContext and standalone SparkContext constructors with the pre-instantiated SparkSession. Converts WASB and ABFS storage paths to OneLake abfss URLs via Shortcuts. Transforms Hive DDL (STORED AS ORC, external tables) to Delta Lake schemas inside Fabric Lakehouse. Maps Oozie workflow actions — spark, hive, shell, sqoop, coordinator — to Fabric Pipeline activities and schedule triggers. Introduces notebookutils for file and credential operations previously handled via (1) retire an HDInsight cluster and move to Fabric, (2) convert WASB paths or Hive DDL, (3) replace Oozie coordinators with Fabric Pipelines. "wasb to onelake", "hive ddl to delta", "oozie to fabric pipelines", "migrate an oozie action", "hive metastore lakehouse", "hdinsight spark migration".
> Migrate Synapse Data Factory pipeline artifacts to Microsoft Fabric Data Factory. pipeline activities, global parameters → Variable Libraries, SynapseNotebook activities → TridentNotebook. SSIS, SHIR-only, and Databricks activities are parked. (2) converting SynapseNotebook activities to TridentNotebook, (3) translating linked services to Fabric connections, (4) converting global parameters to Fabric Variable Libraries, (5) inlining dataset definitions into Fabric pipeline activities. "tridentnotebook pipeline activity", "global parameters to variable library", "linked service to fabric connection", "inline dataset fabric pipeline", "pipeline migration from synapse".
> 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".
>- Generate Power BI report visual design guidance before PBIR files are archetypes, chart types, layout, color, typography, theme direction, or accessibility approach, (2) redesign/restyle an existing report, apply a brand, or critique chart/layout choices, (3) produce a design contract for `powerbi-report-authoring`. For end-to-end requirements, approval, and build "make dashboard look professional", "choose chart type", "apply brand to report", "redesign report", "create design brief", "Power BI report design archetype".
Manage Power BI report workspace items and PBIR definitions in Microsoft Fabric via `az rest` CLI against the Fabric REST API. Use when the user wants to: (1) upload or publish a PBIR/report definition to Fabric, (2) get or download report definitions, (3) update report item definitions or properties, (4) list workspace reports, (5) delete reports. For editing PBIR files, pages, visuals, filters, themes, or formatting, use `powerbi-report-authoring`. For business questions over report data, use `fabriciq`. Triggers: upload PBIR report definition, upload Power BI report, download PBIR definition, publish PBIR definition, publish Power BI report to Fabric, manage Power BI reports, list workspace reports.
>- 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".
Drive an engine app headlessly in a pty, record a video of the whole verification, and open a summary page (video + timeline + checks) with pixel open.
Address CI failures and unresolved review comments on a Helion pull request. Auto-activate when the user mentions a URL like https://github.com/pytorch/helion/pull/<number>.
Verify changes to the Helion CuTe backend by running both test suites, lint, and a code review subagent — then fix any issues found.
Context for working on Helion's TPU/Pallas backend. Auto-activate when the user mentions TPU or Pallas in the context of Helion development.
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.
Auto-activate when the user mentions runpod or scripts/runpod.py
检查和清理中英文文本里的 AI 套路，适用于“去 AI 味”“说人话”“自然一点”“别像模板”“先标问题”这类改写和审稿需求；按场景控制力度，同时保留事实、术语、语域和责任主体。
Claude Skills — questions
Answers built from the skills we actually parsed.