Data & Analytics

Automated comprehensive stock investment due diligence. This skill is used when users provide the stock code they want to research (e.g., "Research 2330, I want to know about TSLA"). It automatically performs an 8-stage research process, covering fundamentals, industry, financials, governance, market divergence, valuation, etc., and generates a Traditional Chinese report. Fully automated, no user intervention required.

This skill should be used when analyzing recent market-moving news events and their impact on equity markets and commodities. Use this skill when the user requests analysis of major financial news from the past 7 days, wants to understand market reactions to monetary policy decisions (FOMC, ECB, BOJ), needs assessment of geopolitical events' impact on commodities, or requires comprehensive review of earnings announcements from mega-cap stocks. The skill automatically collects news using WebSe...

Create personalized training plans for 5K, 10K, Half Marathon, and Marathon races. Use when the user requests a training plan for any of these distances, wants to prepare for a race, or asks for a structured running program. This skill generates comprehensive markdown training plans that include race goals, training paces, phased progression, and week-by-week workout schedules.

Generate a comprehensive HTML training dashboard showing progress toward half marathon training goals. Outputs a single self-contained HTML file with retro styling, ASCII charts, weekly mileage tracking, session completion rates, pace adherence, phase progress, and coaching commentary. Use when the athlete wants to see their training progress, check if they're on track, view their training statistics, or get an overview of their plan progress. Trigger phrases include "show my dashboard", "tra...

Downloads and syncs training data from Strava using the Strava MCP server. This is ONE option for importing training data - athletes can also use manual logs, other platforms (Garmin, Apple Health), or provide data conversationally. Use this skill specifically when the athlete wants to sync from Strava or when the running-coach indicates Strava as the chosen data source. Stores activities in the training-log folder as markdown summaries, including lap details for workout runs.

Weekly running coaching sessions that analyze training logs against the training plan, with primary focus on running and secondary analysis of cross-training activities. Works with multiple data sources including Strava, other platforms (Garmin, Apple Health, etc.), manual logs, or conversational input. Use when the athlete wants to review their training week, get coaching feedback, analyze how they performed, or check if they're on track with their training plan. Trigger phrases include "rev...

Run performance benchmarks. Arguments: asr, tts, vad, diarize (with optional --num-files N).

Closed-loop inverse-design validation. Given a target deformation field, solve the inverse problem on a trained surrogate (Ridge / linear), then run an Abaqus FEA verification and compare surrogate-predicted vs. true displacement field. Reports MSE / MAE / max-abs-error / NRMSE side-by-side, plus saturated-channel count, so you can quantify the surrogate-FEA gap. Use when the user wants to evaluate "is my surrogate good enough for inverse design?", "how big is the surrogate-FEA gap on this ta...

Convert per-case Abaqus FEA outputs into ML-ready (X, Y) wide-table CSVs. Pivots irregular FEA mesh node displacements onto a regular N×N grid via direct binning (structured mesh) or bilinear resampling, picks the final frame as the deformation target, and aggregates across many cases into X_amplitude.csv (design vectors) + Y_grid_uz.csv (flattened grid displacement). Use when the user has a folder of completed FEA cases and wants to train a Ridge / MLP / Gaussian Process surrogate on the (in...

Generate an Abaqus FEA training dataset for surrogate / ML models. Latin Hypercube Sampling (or sparse-pattern sampling) over a parameterized design vector, one case folder per sample, batch-submit Abaqus jobs via subprocess, recover from crashes, and write a unified dataset index. Use when the user wants to "build a training set for a surrogate model", "sweep design parameters in Abaqus", "run N FEA simulations", or "sample a design space".

Sentiment & Reputation Guard — comprehensive brand reputation audit through search engines and LLM perception analysis. Analyzes online reviews, forums, news, and social media to score brand reputation 0-100 across four dimensions: Search Sentiment, SERP Control, Platform Presence, and LLM Sentiment. Auto-detects industry niche (crypto, iGaming, SaaS, agency, ecommerce, fintech, etc.) and tailors the query matrix accordingly. Generates a visual inline HTML report with a prioritized recommenda...

Use when working with Ceres — a Rust harvest-first toolkit for open data portals. Covers harvesting and synchronization, optional embedding and search, CKAN and DCAT portal support, Ollama or hosted providers, CLI commands, REST API endpoints, portal configuration, architecture, extending via traits, and contributing to the Ceres codebase.

Use when performing the monthly HuggingFace dataset export for the Ceres Open Data Index. Covers the full workflow — data quality checks, parquet export, README dataset card update, versioning, and push to HuggingFace.

企业工商信息与人员关联查询（查企业查老板）。 Use when: 用户需要查企业、查公司、查老板、查股东、查高管、查法人、查对外投资、查人员任职、工商信息、主体识别等。

DataEase V2 全能技能 - 创建图表看板、查询数据集、管理组织、导出截图/PDF。支持柱状图、折线图、饼图、明细表，部署后自动截图展示。

Quantitative portfolio toolkit — multi-broker aggregation (Longport, Moomoo/Futu), institutional risk analytics (Sharpe, Sortino, VaR, CVaR, Beta, RSI), options strategy lifecycle, 20+ finance workflows, and CSV/JSON export. Use when the user asks about portfolio analysis, risk metrics, stock quotes, DCA strategy, earnings, options, or financial data export.

Query 50 Indonesian government APIs and data sources — BPJPH halal certification, BPOM food safety, OJK financial legality, BPS statistics, BMKG weather/earthquakes, Bank Indonesia exchange rates, IDX stocks, CKAN open data portals, pasal.id (third-party law MCP). Use when building apps with Indonesian government data, scraping .go.id websites, checking halal certification, verifying company legality, looking up financial entity status, or connecting to Indonesian MCP servers. Includes ready-...

⚠️ DEPRECATED — 이 스킬은 더 이상 사용하지 않는다. 수집/스크리닝/요약/번역은 기존 API 스크립트(collect-papers.ts, collect-community.ts, summarize.ts, digest-community.ts, translate.ts)를 직접 실행한다. 아래 내용은 레거시 참고용으로만 보존.

统一处理 Google Ads / Google Analytics 4 / Google Tag Manager 三件套的配置、调试、查数据、跨工具联动、周报闭环。覆盖以下场景必须触发——Ads 任何写操作或读分析（关键词、否定词、出价、预算、campaign、搜索词、质量分、Extensions、地理定向、周报、决策日志、维护计划、ads 优化）；GA 任何查询或分析（流量来源、渠道分布、Organic/Paid Search、落地页、转化、漏斗、归因、活跃用户、实时数据、property 配置）；GTM 任何配置或调试（tag、trigger、variable、container、版本发布、预览模式、埋点验证）；跨工具联动（Ads 改完怎么验、转化数据对不上、埋点发了 GA 没收到）；环境配置（google-ads.yaml、ADC 凭据、analytics-mcp 安装、GTM OAuth、新机器初始化、setup check）。也包含「Google 工具」「营销三件套」「跑一份报表」「拉一下数据」等模糊表达，只要触及 Ads/GA/GTM 任一就走本 skill。**项...

Help users make decisions among multiple alternatives using the Analytic Hierarchy Process (AHP). Use when the user needs to choose between multiple options, compare alternatives, rank choices, or decide which option is best for their situation. Triggers on phrases like "帮我选", "哪个更好", "帮我分析", "帮我决定", "优先级", "对比", "AHP", "层次分析法", or any multi-criteria decision scenario.

日本初のOSS（オープンソース）AVM、「AVM界のLinux」を目指す土地査定エンジン。日本の土地（更地・所有権）の査定価格をMLIT取引事例から統計的に算定する白箱AVMスキル。媒介査定・売却価格の検討・お客様への査定書作成・相場確認・取引事例比較・比準価格算定・公示価格との比較など、土地の価格を客観的かつ説明可能な形で出したい時は、ユーザーが「ヘドニック」「回帰」「白箱」と言わなくても必ずこのSkillを使うこと。トリガー語例：「土地の査定」「土地査定して」「査定書を作って」「査定書がほしい」「いくらで売れる」「売却価格」「査定価格」「相場感」「相場を出して」「マーケット価格」「取引事例から」「事例比較で」「比準価格」「公示価格と比べて」「お客さんに渡す査定書」「顧客提示用の資料」「土地価格査定クン」「土地査定クン」「重調クンの土地版」「説明できる査定」「根拠付きで査定」「AVM」「自動査定」「鑑定書ではなく査定書として」など。MLIT取引価格情報CSV と地価公示GeoJSON（L01）を入力に（基準地価は不可）、ヘドニック係数を都度回帰で算定して個別格差補正を全開示する...

Scrape Reddit posts by keyword, classify intent and sentiment by industry, translate to Chinese, and export a bilingual Excel intelligence report. Trigger when user says: scrape reddit, reddit analysis, reddit insights, monitor reddit, 爬reddit, reddit舆情, reddit帖子分析, 分析reddit, reddit监控, 帮我看看reddit上, reddit上有什么人说

Analyzes occupancy, utilization, and presence data from any source (Wi-Fi device counts, badge swipes, sensor headcounts, camera counts, manual surveys, room reservations) to surface trends, peaks, day-of-week patterns, anomalies, and prioritized recommendations. Use when the user shares headcount, foot-traffic, badge, Wi-Fi, occupancy, utilization, or facility usage data and asks to analyze, summarize, find patterns, detect a trend, identify peak times, build a heatmap, compare days, find un...

Fetch a public Polymarket wallet's activity, positions, and order-book context, then summarize behavior, exposure, and market focus from a JSON snapshot. Use when analyzing a public wallet or bot without generating copy-trade instructions or reverse-engineered parameter packs.