Data & Analytics

novainsilico Bundle

Create or inspect Jinkō data tables via the jinko-sdk. Use this skill whenever the user wants to upload observed data for trial overlays or calibration objectives from CSV, SQLite, or pandas DataFrame; check data-table schema columns; inspect existing data tables; or verify metadata.public.validForFitnessFunction. Do not use this skill for output sets; use jinko-output-set for that.

novainsilico Bundle

Export a Jinkō computational model to SimBiology, apply its exported simulation settings in MATLAB, and compare selected time series with Jinkō simple_solve results. Use this skill whenever the user wants to transfer a Jinkō model to MATLAB/SimBiology, validate an Excel-based SimBiology export against Jinkō, reproduce a Jinkō simple solve in SimBiology, or investigate divergence between the two solvers. Use it even when the user gives only a model short ID or link and asks for a SimBiology comparison.

ooiyeefei Bundle

Set up Google Analytics 4 (GA4) on a website and wire a real conversion event (form submit, signup, purchase) end to end. Guides the user through account/property/data-stream creation, installing the gtag.js tag, firing a conversion event, marking it as a Key Event, and verifying it in DebugView BEFORE spending on ads. Use whenever someone wants to add GA4, "install Google Analytics", track a form submission or signup, set up conversion tracking, find their Measurement ID (G-XXXXXXXXXX), debug why GA4 events aren't showing, or decide how many GA4 properties/streams they need. Meets the user wherever they are — always asks what they've already set up first.

paulgrape Skill

Privacy and consent essentials for a website — privacy policy, cookie consent, Global Privacy Control, third-party script auditing, privacy-respecting analytics, and data minimisation. Use when handling personal data, consent flows, analytics, or third-party scripts.

pubnub Bundle

Builds real-time analytics and automation with PubNub Illuminate. Covers Business Objects (schema), Metrics (aggregations), Decisions (threshold-triggered actions with the 4-step PUT workflow), Queries (ad-hoc vs saved pipelines), and Dashboards. Use when tracking KPIs, building threshold alerts, automating mute/publish/App-Context-update actions, detecting spam or anomalies, or visualizing live activity.

fnaith Bundle

Tool for listing all videos from a YouTube channel with video id, name, and duration. Use when asked to query video data from a YouTube channel, generate CSV reports, or analyze channel content.

igapyon Bundle

Use when the user asks import-state-handler to process an import job. Do not activate for generic CSV questions.

arc-mcp Skill

Find the root cause of a slow ABAP SQL or Fiori-Elements OData request and propose the cheapest fix — GUI-free via ARC-1 (odata_perf sap-statistics timing split, cds_sql "Show SQL", SAPQuery execution metrics, ST05 SQL-trace control, ABAP profiler traces), escalating to SAP GUI / HANA only for trace records and execution plans. Use when a report, transaction, Fiori list, CDS view, or OData entity is slow, a query times out under load, or someone asks "why is this SQL/OData so slow" or "where is the time going".

arc-mcp Skill

Generate an analytical CDS query (transient projection view with PROVIDER CONTRACT ANALYTICAL_QUERY) on top of an existing analytical cube. Use when asked to "create an analytical query", "build a KPI query on a cube", "generate an ANALYTICAL_QUERY view", or "expose a cube for analytics/embedded analytics".

arc-mcp Skill

Generate a CDS analytical model (star schema — cube + dimension + text views) on top of a RAP business object or a DDIC table. Use when asked to "create a star schema", "generate an analytical cube and dimensions", "make a RAP BO analytical", "build a CDS cube from a table", or "create an analytical model".

jiayaoqijia Skill

Research cryptocurrency tokens, verify smart contracts, check perpetual futures data, governance proposals, and on-chain analytics. Use when asked about token research, contract verification, is this token safe, rug pull check, Hyperliquid perps, funding rates, governance vote, Dune analytics, token due diligence, or security audit status.

goldsky-io Bundle

Write SQL, TypeScript, and dynamic table transforms for Turbo pipelines. Covers: decoding EVM logs with _gs_log_decode, filtering/casting blockchain data, UNION ALL for combining events, TypeScript/WASM transforms (invoke function), dynamic lookup tables (dynamic_table_check), transform chaining, and Solana decoding. Triggers on: 'decode Transfer events', 'write a SQL transform', 'filter by contract', 'TypeScript transform', 'dynamic table', 'UNION ALL'. For pipeline YAML structure, use /turbo-pipelines. For end-to-end building, use /turbo-builder.

inclusionai Bundle

Parse workspace files, HTTP(S) file URLs, or supported source URLs such as YouTube into Markdown, inspect source routing, and inspect resumable PDF batch status with the FileX CLI inside an AWorld sandbox. Use for reading, extracting, transcribing, inspecting, summarizing, or answering questions about PDF, Word, PowerPoint, Excel, CSV, text, Markdown, image, audio, or video files.

inclusionai Skill

Lightweight TikHub YouTube search and video-detail workflow. Prioritizes single-request usage with curl or minimal Python, saves raw API JSON by default, and includes a small stdlib post-processor for CSV and simplified JSON. Use when the user wants YouTube comprehensive search results, continuation-token pagination, or structured video metadata from TikHub without a heavy wrapper.

inclusionai Skill

Single-file TikTok/Douyin video download and traffic metrics via TikHub API using only httpx; optional persisted raw API JSON plus a stdlib post-processor emitting CSV and simplified JSON. Supports one URL or concurrent batch (max 10 workers). No dependency on any project codebase.

inclusionai Skill

Lightweight TikHub Xiaohongshu image-search workflow. Prioritizes single-request usage with curl or minimal Python, saves raw API JSON by default, and includes a small stdlib post-processor for CSV and simplified JSON. Use when the user wants Xiaohongshu keyword image search, page-based pagination, or structured note/image metadata from TikHub without a heavy wrapper.

big-emotion Bundle

Measures what a site's audience actually does and writes the dated report the downstream comms skills consume. Crosses privacy-analytics data with the repository's own URL inventory to give every visited page one verdict — Keep, Improve, Merge, Create — and to surface dead ends, cannibalised pages, unattributed channels and the mobile-desktop gap. Read-only on source; writes exactly one Markdown file. Use for "que dit le trafic", "quelles pages marchent", "audit de contenu", "rapport d'audience", "content audit", or /audience-audit.

posthog Bundle

Score CRM leads (HubSpot/Salesforce) by recent product usage and surface the warmest leads sales hasn't contacted yet, a product-qualified-lead list saved as a reusable PostHog insight. Use this whenever someone wants product-qualified leads (PQLs), a lead score based on product usage, the warmest leads to prioritize, engaged signups sales should call, or a usage-based ranking of leads joined to the CRM. Triggers on phrasings like 'which leads should sales call', 'product qualified leads', 'score my leads by usage', 'hottest leads we have not contacted', 'PQL list', or 'which signups are sales-ready'. Boundary: this ranks individual leads to action now. For analyzing which onboarding STEPS drive trial-to-paid conversion in aggregate, use posthog-onboarding-conversion. It sets up the HubSpot/Salesforce source if needed and builds the insight end-to-end.

posthog Bundle

Build reusable revenue models — MRR, ARR, gross revenue, new/expansion/contraction/churn, ARPU, LTV, and per-customer/per-account revenue — on either PostHog data-warehouse views (HogQL) or an external dbt project. Use when the user wants to model, define, or compute recurring revenue, monthly/annual recurring revenue, churn or retention of revenue, lifetime value, average revenue per user, or revenue by customer, cohort, product, or currency. On PostHog, build on the managed revenue_analytics_* views (revenue_item, mrr, customer, subscription, charge, product) fed by Stripe or custom revenue events — not raw Stripe tables — and normalize money with convertCurrency(). In dbt, stage the payment source and compute fct_mrr / fct_revenue_item / dim_customer marts with tests. Covers picking the right source, the subscription-config gotcha that leaves MRR empty, currency handling, and linking revenue to persons/groups. Read modeling-warehouse-foundations first for the view-vs-dbt mechanics.

posthog Bundle

Compare each account's ARR (from Salesforce/HubSpot) against its PostHog engagement score and flag the high-ARR, low-engagement accounts, saved as a reusable PostHog insight. Use this whenever someone asks whether their biggest accounts are actually engaged or happy, wants to compare account value against product usage, find at-risk revenue, spot big accounts that barely use the product, or map ARR against engagement. Triggers on phrasings like 'are our biggest customers actually using us', 'ARR vs engagement', 'which big accounts are at risk', 'high value low usage accounts', 'value-engagement matrix', or 'are our biggest accounts our happiest'. Boundary: this measures CRM-ARR versus engagement health. For expansion/upsell based on usage against PLAN LIMITS, use posthog-upsell-ready-accounts. It sets up the Salesforce/HubSpot source if needed and builds the insight end-to-end.

posthog Bundle

Rank the onboarding and activation steps that best predict a trial becomes a paying customer: build the onboarding funnel, flag who reached a first Stripe payment, and compare payers versus non-payers, saved as a reusable PostHog insight. Use this whenever someone asks which onboarding or activation steps lead to paid conversion, what converting trials do differently, which signup steps predict revenue, or where trials stall before paying. Triggers on phrasings like 'what makes trials convert', 'onboarding steps that lead to payment', 'activation actions that predict conversion', 'compare payers vs non-payers by onboarding', or 'where do trials stall'. Boundary: this analyzes the onboarding PATH to first payment in aggregate. For scoring and prioritizing which individual LEADS sales should contact, use posthog-lead-scoring. It sets up the Stripe source if needed and builds the insight end-to-end.

posthog Bundle

Test whether support tickets predict churn with a three-way join of PostHog engagement, Zendesk/Intercom tickets and topics, and Stripe churn, ranking the support issues that precede the most lost revenue, saved as a reusable PostHog insight. Use this whenever someone asks whether support contact or tickets predict churn, which ticket topics lead to cancellations, which support issues cost the most revenue, or how tickets correlate with retention. Triggers on phrasings like 'do tickets predict churn', 'which support issues cause churn', 'tickets vs retention', 'support topics that lose revenue', or 'does contacting support mean they will leave'. Boundary: this requires support-tool data (Zendesk/Intercom). For churn warning signs from PRODUCT USAGE alone with no tickets, use posthog-pre-cancellation-behavior. It sets up the Zendesk/Intercom and Stripe sources if needed and builds the insight end-to-end.

posthog Bundle

Find expansion-ready accounts by comparing each account's real product usage against its current Stripe plan limits and flagging accounts consistently near or over their limits while still on a lower plan, saved as a reusable PostHog insight. Use this whenever someone asks who to upsell, which accounts are outgrowing their plan, which accounts are hitting usage limits, upgrade or expansion candidates, or accounts on the wrong (too-small) plan. Triggers on phrasings like 'who should we upsell', 'which accounts are hitting their limits', 'expansion candidates', 'accounts on the wrong plan', 'usage vs plan limit', or 'accounts ready to upgrade'. Boundary: this flags expansion by usage-versus-plan-limits. For account health measured as ARR versus engagement (big accounts that barely use the product), use posthog-value-vs-engagement. It sets up the Stripe source if needed and builds the insight end-to-end.

posthog Bundle

Shared foundations for building reusable data models in PostHog, on either of two stacks: PostHog-native data-warehouse views / materialized views (HogQL, via the view-* MCP tools), or an external dbt project (sources.yml + staging/marts + schema tests) run against your own or PostHog's managed warehouse. Read before authoring any specific business model — covers the PostHog-vs-dbt decision, the view-create → view-materialize → sync_frequency workflow and the HogQL column-aliasing rule, the dbt project skeleton and the honest "no native dbt integration" picture, warehouse joins and star-schema dimensions, currency conversion with convertCurrency(), and checking/registering models in the data catalog for reuse. Companion to the domain skills modeling-revenue-metrics, modeling-conversion-metrics, modeling-activation-metrics, modeling-product-usage-metrics, and modeling-dimension-tables. Use when the user asks how to build a view, materialized view, or dbt model in PostHog, or which of the two stacks to use.

posthog Bundle

Measure whether a specific feature rollout moved revenue: split users on feature-flag or experiment exposure and compare downstream Stripe revenue and retention between the exposed cohort and control, saved as a reusable PostHog insight. Use this whenever someone wants the revenue impact of an experiment or a feature they shipped, wants to tie a feature flag or A/B test to revenue and retention, or asks whether a rollout paid off. Triggers on phrasings like 'did the feature we shipped move revenue', 'revenue impact of our experiment', 'did the rollout pay off', 'compare exposed vs control on revenue', 'feature flag revenue lift', or 'A/B test revenue'. Boundary: this is a before/after causal test tied to a specific flag or experiment. For ranking which EXISTING features high-revenue customers already use, use posthog-features-drive-revenue. It sets up the Stripe source if needed and builds the insight end-to-end.

posthog Bundle

Rank a PostHog user's product features by the revenue of the customers who use them (joins Stripe MRR/invoices to feature-usage events) and save it as a reusable PostHog insight. Use this whenever someone asks which features their highest-revenue or best customers use most, wants revenue-weighted feature adoption, wants to tie existing feature usage to MRR/ARR, or wants to decide which features to invest in based on paying customers. Triggers on phrasings like 'which features drive revenue', 'what do our best customers use', 'revenue by feature', 'connect Stripe revenue to feature usage', or 'which features should we double down on'. Boundary: this ranks EXISTING features by adopter revenue, it is not a before/after test. For whether a specific rollout, experiment, or feature flag moved revenue use posthog-feature-revenue-impact; for revenue by acquisition channel or ad spend use posthog-acquisition-channels-retention. It sets up the Stripe warehouse source if needed and builds the insight end-to-end.

posthog Bundle

Find the shared product-usage warning pattern before churn: pull cancellation dates from Stripe or Chargebee and examine the roughly 30 days of activity before each one, saved as a reusable PostHog insight. Use this whenever someone wants an early-warning churn signal from behavior, asks what churning customers do (or stop doing) before they leave, how usage or activity changes before cancellation, or the usage drop that precedes churn. Triggers on phrasings like 'what do customers do before they cancel', 'pre-cancellation behavior', 'churn warning signs', 'how does usage change before churn', or 'leading indicators of churn'. Boundary: this is behavioral/usage-based churn prediction with no support data. If the question is specifically about SUPPORT TICKETS or Zendesk/Intercom predicting churn, use posthog-support-tickets-churn. It sets up the Stripe/Chargebee source if needed and builds the insight end-to-end.

kshyam Skill

[ YOUR NAME ]'s Excel Style

agentscope-ai Bundle

Use this skill when the task involves converting or reformatting structured data between file formats such as CSV, JSON, XML, or YAML. Suitable for tasks like "convert this CSV to JSON", "reformat my data file", or "change the file format of this dataset". Do NOT use for statistical analysis, aggregation, or report generation.

agentscope-ai Skill

Answer a quantitative business question by writing a SQL query against the data warehouse, validating it, and presenting the result. Use when the user asks "how many...", "what's the trend of...", "compare X vs Y over...", "what's our top N...", or anything that resolves to a query against tabular data. Produces a small result table plus the underlying query.

avivsinai Skill

Use when the operator asks for a weekly business report, Monday-morning business digest, revenue/support/CRM/analytics roundup, or wants to schedule workflow

rldyourmnd Skill

Write and optimize SQL across major dialects (PostgreSQL, MySQL, BigQuery, Snowflake, Databricks, SQL Server). Use when user asks for query authoring, debugging, optimization, translation between dialects, or analytical SQL patterns.

biraj2004 Bundle

All-in-one data analysis and productivity assistant. Covers end-to-end workflows for data processing, analytical insights, report writing, PPT creation, and data visualisation. Always approaches tasks from an expert perspective — thinks one step ahead for the user. Proactively confirms with the user when uncertain. Supports: Excel data analysis, advertising data review, ROI calculation, data visualisation, report generation, PPT creation, formula generation. Use when the user mentions "analyse data", "create a report", "make a PPT", "Excel", "advertising analysis", "ROI", "retrospective", "weekly report", "monthly report", "data processing", "chart", "visualisation", "presentation", "table", or "formula".

justinjchen-cornell Bundle

Excel/表格审计——公式级检查（错误值/硬编码/不一致公式）+ 财务模型完整性检查（资产负债表平衡/现金流勾稽/逻辑合理性）。MUST BE USED 当审核 BP 财务模型/被投公司财务包/任何表格数据时。

kirkruglov Bundle

Generates a preliminary timeline and budget estimate for an IT project (Phase 1 — Initiation). Reads available project files (client questionnaire, project charter, constraints, team list), applies analogy-based estimation using built-in agent knowledge of typical IT projects, and produces a three-scenario (optimistic / realistic / pessimistic) preliminary-estimate.xlsx artifact. Covers task #4 of the project management agent. Mode B: agent prepares draft, PM reviews and corrects. Triggers RU: «сформируй оценку сроков», «подготовь предварительную оценку бюджета», «оцени сроки проекта», «сделай оценку по аналогам», «предварительная оценка». Triggers EN: 'generate estimate', 'preliminary estimate', 'estimate timeline and budget', 'create project estimate', 'ballpark estimate'.

kirkruglov Bundle

Generates a change impact assessment file (change-impact-CR-001.xlsx) for Phase 4 — Development. PM describes a scope change request; the agent classifies the change (scope / budget / timeline / resource), analyzes impact on project-plan.md and project-budget.xlsx, calculates timeline delta, budget delta, and risk level (Low / Medium / High), then produces a two-sheet xlsx artifact with a summary and detailed breakdown. PM reviews the draft and makes the final approval decision. Covers task #21 of the project management agent. Mode B: agent prepares draft, PM decides. If approved — updates project-plan.md via generate-project-plan. Triggers EN: 'scope change request', 'change impact assessment', 'generate change impact'.