Data & Analytics

yyyyyzhao Bundle

lyt 系列电商业务数据分析 skill。用于用户提供文字指标、图片/截图、CSV/XLSX 表格、PDF/报告、竞品数据或后台导出后，先做数据体检和口径校验，再拆解 TikTok Shop、商品卡、短视频、直播、GMV Max、广告、成本、利润和店铺经营问题。适用于复盘、对比、找问题、放量/暂停决策、利润测算和报告输出；不替代从 0 选品、已有产品可做性验证、平台流量机制讲解或模糊经营问题追问。 触发方式：/lyt-data-analysis、/数据分析、/数据复盘、/经营复盘、「帮我看数据」「分析这个表格」「复盘这组截图」

yyyyyzhao Bundle

lyt 系列已有产品验证 skill。用于用户已经有具体产品、候选品清单、商品截图、1688/竞品链接、供应链资料、CSV/XLSX 候选品表格或后台测试数据，想判断某个产品或一批候选品是否值得进入测试池。输出「能测 / 暂缓 / 不建议做」、事实/推断/假设、7 维验证、最小验证方案和补数清单。仅处理跨境电商、TikTok Shop、电商商品销售、供应链、货盘、内容、广告、达人分销和店铺经营相关产品验证问题。 触发方式：/lyt-product-validation、/产品验证、/单品验证、/候选品筛选、「这个产品能不能做」「这些候选品怎么筛」

zekainie Skill

从学生上传的课件/大纲/老师勾的重点/真题，一键初始化并验证备考工作区：解析 PDF、DOCX、PPTX、 XLSX、常见独立图片与 txt/md，建立分章节 LLM Wiki、标准题库、结构化接管队列与进度状态；仅在 Python 确实无法运行时 明确降级为手动写盘。当工作区尚未建立、资料发生变化、或建库 readiness 被阻断时使用。

zhangloveyan Bundle

代码生成统一入口。生成 SQL、CRUD、API、枚举等代码。

leadmagic Bundle

LeadMagic bulk enrichment jobs, CSV uploaders, and synchronous mini-batches. Use when enriching any list of 50+ rows, submitting POST /bulk/submit, uploading a CSV, polling job status, pausing or resuming a job, pulling error rows, configuring callbacks, or budgeting per-row credits for any product.

leadmagic Bundle

Official LeadMagic product skill and router for the full API surface — email finder and validation, People Search v3, company and lookalike search, jobs and hiring intent, ads intelligence, bulk CSV jobs, credits, plans, and hosted MCP. Use when calling any api.leadmagic.io endpoint, budgeting credits, choosing the right product for an outbound task, or wiring LeadMagic into Claude Code, integration platforms, or no-code automation tools.

leadmagic Skill

LeadMagic account analytics — usage, credit spend, per-product breakdowns, daily history, found rates, data quality, and error reporting via the free GET /v1/analytics endpoints. Use when reporting credit spend, auditing what a workflow cost, checking match or found rates, tracking usage by product, or debugging error patterns in an integration.

flissel Skill

Erstellt Onboarding-Checkliste fuer neuen VibeMind-Mitarbeiter direkt als xlsx via openpyxl, oeffnet in Excel, validiert via openpyxl

nt-ninjatrader Bundle

This skill renders three static PNG charts, with Matplotlib. Candlestick charts, with VWAP, fill-marker, and price-level overlays. Horizontal volume-profile histograms, with POC, VAH, and VAL lines. Cumulative-P&L equity curves, with optional drawdown shading. Chart data comes from market_history bars, fill_history fills, market-context analytics, and trade-journal round-trip trades. Use it when the user asks to "chart", "plot", "draw", "visualize", or "render". Also use it when the user wants a picture of the bars, fills, equity, or profile. Also use it for a qualified "show", such as "show the chart", "show me the bars", or "show my equity curve". A bare "show me my positions" is NOT a chart request. Route it to position-watchdog or my_portfolio. It produces PNG output at a specified path. It is never interactive. This skill requires matplotlib (install with `pip install matplotlib`).

aitit-inc Skill

Use when the user asks to "import prospects", "upload a list", or has tabular contact data (CSV / Excel / SQLite / text) to load. Two modes: save as tenant-only assets (link later via /match-prospects) or link straight to a project.

ianalloway Skill

Create data visualizations from the command line. Generate charts, graphs, and plots from CSV/JSON data without leaving the terminal.

ianalloway Skill

Track your sports bets in a local CSV journal. Calculate ROI, CLV, win rate by sport/bet-type, and identify where you're actually making money.

nanoagentteam Bundle

Generate structured CSV literature survey tables. MUST activate this skill (instead of plain-text replies) when the user requests a literature review, paper collection, related work survey, or comparison of papers on a topic. 生成结构化 CSV 文献调研表格。当用户要求调研论文、收集文献、整理相关工作、对比论文时，必须激活此 skill 生成 CSV 输出，而非纯文字回复。 Triggers: survey, related work, literature review, paper list, find papers, collect papers, compare papers, 调研, 文献, 找论文, 相关论文, 收集论文, 整理文献, 论文列表, 帮我找论文. Output: multi-source search → metadata extraction → 8-column CSV (title, abstract, summary, relevance, authors, year, venue, link) → send to IM.

ertugrulakben Skill

Creates content calendars, writes platform-specific posts, tracks analytics, and manages social media presence across Instagram, Twitter/X, LinkedIn, Facebook, and TikTok.

jangviktor-web Bundle

基于东方财富数据库，支持自然语言查询全球宏观经济数据，涵盖国民经济核算、价格指数、货币金融、财政收支、对外贸易、就业民生、产业运行等多个领域，适配各类宏观经济研究、市场分析、政策解读等多元专业场景需求。返回结果包含数据说明及 csv 文件。Natural language query for macroeconomic data from financial databases, covering national economic accounting, price indices, monetary finance, fiscal revenue and expenditure, foreign trade, employment, industrial operation, and other fields. It supports diverse scenarios including macroeconomic research, market analysis, and policy interpretation.

jangviktor-web Bundle

基于东方财富数据库，支持自然语言查询金融数据，覆盖A港美、基金、债券等多种资产，含实时行情、公司信息、估值、财务报表等，可用于投资研究、交易复盘、市场监控、行业分析、信用研究、财报审计、资产配置等场景，适配机构与个人多元需求。返回结果包含 xlsx（优先 rawTable 完整数据）与 Markdown（接口 table 展示数据；与 rawTable 量不一致时会标注部分展示）。Natural language query for financial data across all markets, including A-shares, ETFs, bonds, Hong Kong and US stocks, and funds. It provides L1/L2 data, financial indicators, company profiles and valuation metrics. Ideal for investment research, strategy backtesting, market monitoring and industry analysis. It meets the needs of diverse institutions and individuals.

jangviktor-web Bundle

基于东方财富数据库，通过条件、排序或推荐筛选A港美股、基金、ETF、可转债和板块，支持技术面、基本面、消息面与市场情绪等复合指标，返回 CSV 与数据说明。适用于从多只标的中筛选或排名；仅查询一个或多个已知标的的指标值时不要使用，改用 mx-finance-data。Natural language screener for filtering, ranking or recommending multiple assets; not for metric lookup on known entities.

japboy Bundle

Apply documented architecture patterns to design and review decisions. Use for monorepo or workspace directory layout, package placement and topology, or route-state product analytics, URL-state contracts, measurement manifests, and warehouse semantic models. Select only matching catalogue patterns; unrelated architecture and build-tool configuration are outside this skill.

jorgemuza Bundle

Track AI token consumption, costs, and usage trends using the orbit CLI. Use this skill whenever the user asks about token usage, AI costs, Claude Code spending, how many tokens were used, cost breakdown by model, session history, or token analytics. Trigger on phrases like 'how much have I spent', 'token usage', 'show me costs', 'what's my AI spending', 'how many tokens today', 'cost per model', 'list sessions', 'track usage', 'token report', 'weekly usage', 'monthly costs', or any token/cost tracking task — even casual references like 'am I spending too much on Claude', 'what did that session cost', 'show me the dashboard', or 'how much is opus costing us'.

matrixorigin Skill

Use this skill to generate benchmark datasets (TPC-H, TPC-DS, etc.). Trigger when the user needs test data at a specific scale factor for benchmarking or testing. Supports parquet and duckdb output formats.

hao-ai-lab Skill

Planning prompt for Wild Loop V2 - iteration 0 phased planning with reflection, experiment ops, and analytics

hellotern Skill

You MUST use this skill before approving or merging any code that touches auth, input handling, permissions, or external data. Use when the review focus is specifically on security vulnerabilities, attack surface, or compliance — not general code quality. Stronger signals: "security review", "check for vulnerabilities", "is this safe", "audit this", "OWASP", "injection", "auth check", "XSS", "SQL injection", "pentest", "threat model". Can run standalone or as a follow-up after sextant:review-code. Use sextant:review-code instead when the goal is general code quality without a security focus.

classmethod Bundle

IPA「安全なウェブサイトの作り方 改訂第7版」「安全なSQLの呼び出し方」「ウェブ健康診断仕様」「セキュリティ実装チェックリスト」「安全なウェブサイトの運用管理に向けての20ヶ条」に基づき、ソースコードを静的に検査して脆弱性候補を検出する。発見した問題には IPA 原典の出典 (文書名・章・ページ・URL) を必ず付与する。

quan0715 Skill

Use when the QJudge TA agent must create, inspect, filter, or patch a CSV artifact, especially grade.csv.

wide-moat Bundle

Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Assistant needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas

wide-moat Bundle

Use this skill when a file has been uploaded but its content is NOT in your context — only its path at /mnt/user-data/uploads/ is listed in an uploaded_files block. This skill is a router: it tells you which tool to use for each file type (pdf, docx, xlsx, csv, json, images, archives, ebooks) so you read the right amount the right way instead of blindly running cat on a binary. Triggers: any mention of /mnt/user-data/uploads/, an uploaded_files section, a file_path tag, or a user asking about an uploaded file you have not yet read. Do NOT use this skill if the file content is already visible in your context inside a documents block — you already have it.

crag666 Bundle

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.

clamp-sh Skill

Build a refinable causal DAG before answering "did X cause Y" on observational data. Emits a Mermaid diagram of assumed causes, applies the back-door criterion to decide what to condition on, and forces confounders, mediators, and colliders to be named explicitly instead of "controlling for everything". The DAG is an artifact downstream skills read to pick the right adjustment set. Use whenever interpreting cohort comparisons, funnel drop-offs, or any analytics result where the user is reasoning causally without an experiment. Pairs with analytics-diagnostic-method as the causal-structure layer underneath the diagnostic tree. Triggers when Clamp MCP cohorts_compare or funnels_create is called with no experimental holdback, when a Clamp MCP result prompts a causal claim from observational data, or when via Clamp the user asks "why did this segment convert higher".

clamp-sh Skill

Author and maintain an event-schema.yaml file. A portable, typed declaration of every product analytics event the codebase fires (event names, properties, types, intent). The CLI generates a TypeScript type from it so tracking calls are autocompleted and type-checked at build time. Vendor-neutral; works with any analytics SDK (Clamp, GA4, Mixpanel, Amplitude, PostHog, Segment).

clamp-sh Skill

Always-valid sequential inference for honest peeking at running A/B tests. Applies mSPRT (mixture Sequential Probability Ratio Test) or confidence sequences so the false-positive rate stays at the nominal alpha even when the test is checked daily. Use this skill when the user asks if it's safe to call an A/B test early, or to peek-check a running test. Pairs with experiment-result-reader and bayesian-experiment-reader, and with analytics-diagnostic-method for the framing discipline. Triggers when Clamp MCP returns mid-experiment exposure and conversion counts, or when a user references peeking, early stopping, sequential testing, alpha-spending, mSPRT, or confidence sequences. Vendor-neutral methodology; works with any analytics source, with Clamp MCP as the canonical integration. Use whenever interpreting an in-flight experiment where the planned horizon has not been reached but the user wants a stop/continue decision.

clamp-sh Skill

One-time interview that captures the business context (industry, model, primary conversion, traffic range, ICP, data stack) into a local analytics-profile.md file. Every other analytics skill reads this file so its answers are calibrated to the right benchmarks and terminology instead of generic averages.

clamp-sh Skill

Pearl's three-rung causal hierarchy as a query classifier. Tags every analytics question as rung-1 (association, P(Y|X)), rung-2 (intervention, P(Y|do(X))), or rung-3 (counterfactual, P(Y_x|Y',X')) before answering. Refuses to escalate a rung-1 observational finding into a rung-2 ship/kill recommendation without naming an identification strategy (back-door, instrumental variable, DiD, RDD, synthetic control). Use this skill whenever interpreting an analytics question that asks why or what-if, to classify it on Pearl's causal hierarchy before answering. Pairs with analytics-diagnostic-method. Triggers when Clamp MCP returns a comparison or trend that the user is about to act on, so the agent labels the claim's rung explicitly instead of laundering correlation into causation. Works with any observational source; Clamp MCP is the canonical integration via traffic.compare, funnels.list, and cohorts.compare.

clamp-sh Skill

Bayesian counterpart to experiment-result-reader. Computes posterior P(variant beats control), credible intervals, and expected loss from per-variant exposure and conversion data. Beta-Binomial for proportion metrics (CVR), Normal-Normal for continuous metrics (revenue per user). Decision rule combines a confidence threshold with an expected-loss tolerance, so the ship decision reflects both "how likely is this better?" and "how bad is it if I'm wrong?". Use this skill alongside experiment-result-reader when reading any A/B test result. Pairs with analytics-diagnostic-method. Use whenever interpreting an A/B test result the user plans to ship from, when the question is "what's the chance variant wins?", or when a frequentist p-value is on the edge and the user wants the posterior view. Triggers when Clamp MCP returns experiment exposure and conversion data, or when any analytics source surfaces per-variant counts.

clamp-sh Skill

The spine of analytics investigation. Use whenever interpreting analytics numbers, answering "why did X change", reading funnels, comparing cohorts, or presenting findings. Teaches a five-step method (load profile, frame the question, build a MECE hypothesis tree, triangulate, present with Pyramid Principle), how to separate signal from noise, and how to spot Simpson's paradox before it misleads you.

clamp-sh Skill

Formal time-series methods that augment the hand-coded fingerprint library in traffic-change-diagnosis. Use this skill when traffic-change-diagnosis fingerprints overlap, when the user asks "is this real?", or when the change date is contested. Applies STL decomposition, Bayesian online changepoint detection, Prophet, quantile regression, sequential probability ratio test, and Granger causality. Use whenever interpreting a series where day-of-week confounds an eyeballed drop, where two candidate causes share a week, or where an alert needs to fire before an analyst sees the chart. Pairs with analytics-diagnostic-method for the surrounding investigation and with sequential-monitoring for the SPRT details. Triggers when Clamp MCP traffic_timeseries returns a series spanning more than 14 days, or when via Clamp the user shares a daily/hourly metric history that needs a non-eyeball verdict.

clamp-sh Skill

Interpret analytics metrics with correct context. Use when the user asks "is this good", "what's a normal X", or quotes a rate without denominator. Covers realistic ranges for bounce rate, engagement, session duration, pages per session, conversion rate by model type, SaaS unit economics (LTV:CAC, CAC payback, MRR churn, activation, retention), plus when each metric lies and minimum sample sizes.