Data & Databases

Create, evaluate, and iterate on high-quality agent skills with structured guidance, quality scoring, and best-practice enforcement.

External API data — sentiment via Adanos, comprehensive data via Funda AI, Hormuz Strait monitoring, TradingView desktop reader, and Hyperliquid perp/spot reader.

Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.

Stock analysis, earnings, estimates, correlations, liquidity, ETFs, options payoff, and trading strategies via yfinance.

Read-only social media and research feeds — Twitter/X, Discord, LinkedIn, Telegram, Y Combinator, plus a generic opencli fallback covering 90+ finance/research sources (Yahoo Finance, Bloomberg, Reuters, Eastmoney, Xueqiu, Reddit, HackerNews, Substack, arXiv, etc.).

Analyze historical downtrend durations and generate interactive HTML histograms showing typical correction lengths by sector and market cap.

Multi-perspective startup analysis frameworks for VC investors, job applicants, and founders.

Build and open FinViz screener URLs from natural language requests. Use when user wants to screen stocks, find stocks matching criteria, filter by fundamentals or technicals, or asks to open FinViz with specific conditions. Supports both Japanese and English input (e.g., "高配当で成長している小型株を探したい", "Find oversold large caps with high ROE").

This skill should be used when analyzing market breadth charts, specifically the S&P 500 Breadth Index (200-Day MA based) and the US Stock Market Uptrend Stock Ratio charts. Use this skill when the user provides breadth chart images for analysis, requests market breadth assessment, positioning strategy recommendations, or wants to understand medium-term strategic and short-term tactical market outlook based on breadth indicators. Also works WITHOUT chart images by fetching CSV data directly from public sources. All analysis and output are conducted in English.

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Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.

Use this skill to track institutional investor ownership changes and portfolio flows using 13F filings data. Analyzes hedge funds, mutual funds, and other institutional holders to identify stocks with significant smart money accumulation or distribution. Helps discover stocks before major moves by following where sophisticated investors are deploying capital.

MCP server and Claude plugin for Postgres skills and documentation.

This skill retrieves upcoming earnings announcements for US stocks using the Financial Modeling Prep (FMP) API. Use this when the user requests earnings calendar data, wants to know which companies are reporting earnings in the upcoming week, or needs a weekly earnings review. The skill focuses on mid-cap and above companies (over $2B market cap) that have significant market impact, organizing the data by date and timing in a clean markdown table format. Supports multiple environments (CLI, Desktop, Web) with flexible API key management.

Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position). Scores each stock 0-100 and assigns A/B/C/D grades. Use when user asks about earnings trade analysis, post-earnings momentum screening, earnings gap scoring, or finding best recent earnings reactions.

MCP server and Claude plugin for Postgres skills and documentation.

Validate data quality in market analysis documents and blog articles before publication. Use when checking for price scale inconsistencies (ETF vs futures), instrument notation errors, date/day-of-week mismatches, allocation total errors, and unit mismatches. Supports English and Japanese content. Advisory mode -- flags issues as warnings for human review, not as blockers.

Monitor dividend portfolios with Kanchi-style forced-review triggers (T1-T5) and convert anomalies into OK/WARN/REVIEW states without auto-selling. Use when users ask for 減配検知, 8-Kガバナンス監視, 配当安全性モニタリング, REVIEWキュー自動化, or periodic dividend risk checks.

MCP server and Claude plugin for Postgres skills and documentation.

Fetch upcoming economic events and data releases using FMP API. Retrieve scheduled central bank decisions, employment reports, inflation data, GDP releases, and other market-moving economic indicators for specified date ranges (default: next 7 days). The script outputs raw JSON or text; the assistant filters, assesses impact, and generates the Markdown report.

MCP server and Claude plugin for Postgres skills and documentation.

MCP server and Claude plugin for Postgres skills and documentation.

Model Context Protocol (MCP) server for interacting with data.gouv.fr datasets and resources via LLM chatbots

MCP server and Claude plugin for Postgres skills and documentation.

Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications

Extract text from PDF files, translate it to a target language, and save the result as a Markdown file. Use this skill when the user wants to translate a PDF document or asks to "convert PDF to Chinese".

MCP server and Claude plugin for Postgres skills and documentation.

MCP server and Claude plugin for Postgres skills and documentation.

"Skill for searching and evolving the SQLite-backed Knowledge Graph.\

MCP server and Claude plugin for Postgres skills and documentation.

PostgreSQL documentation and best practices through semantic search and curated skills, including TimescaleDB and Tiger Cloud ecosystem tools.

Model Context Protocol server for Flint — compile, validate, and render semantic chart specs to Vega-Lite, ECharts, or Chart.js artifacts (PNG/SVG) in-process.

Use when: the user asks to make or render charts with flint-chart, visualize tabular data, generate a ChartAssemblyInput, validate/render through MCP, or add Flint to a JS/TS project. Author the semantic spec, transform data before Flint when needed, install/import Flint only when executable code is needed, and reserve backend-specific style tweaks for after compiling from Flint.

Extract Feishu (Lark) Docs, Wiki pages/collections, spreadsheets, and Minutes (妙记) transcripts into faithful local Markdown via the lark-cli API (no LLM rewriting of the body; browser-DOM fallback when lark-cli can't reach the content). Use whenever the source is a Feishu/Lark URL and fidelity matters — 导出飞书文档/合集/妙记转写, 把飞书 wiki/知识库转 markdown, archiving a Feishu collection, exporting a 妙记 transcript, or saving a Feishu page — even if the user only says clipping, archiving, converting, or "save this". Also covers the owner-exported .docx → faithful Markdown path.

Fetch comprehensive, login-free data for any Bilibili (B站) video — title, UP name and follower count, publish date, partition, tags, per-part cids, live stats (view, like, coin, favorite, share, reply, danmaku), and full danmaku (bullet-comment) text. Use this skill whenever working with a Bilibili video and needing real, citable numbers or metadata — ingesting a Bilibili source into a knowledge base, analyzing why a video performed, verifying a creator's claimed metrics, building a case study, or any time a Bilibili view/like/favorite count is about to be written into a document — fetch it, never hand-type or estimate it. Accepts BVID, av numbers, b23.tv short links, or full URLs. Subtitles are also covered but require the user's Bilibili login.

Use for anything involving the Synalinks neuro-symbolic LM framework (Keras-inspired) — DataModel/Field/Input, JSON operators (+ & | ^ ~), synalinks.ops, LanguageModel/EmbeddingModel and provider prefixes (openai/anthropic/ollama/groq/openrouter/bedrock/...); the Program class and its four building APIs (Functional/Sequential/Subclassing/Mixed), save/load, summary; generation modules (Generator, ChainOfThought, SelfCritique, Identity, PythonSynthesis) and custom Module subclassing; control flow (Decision, Branch, And/Or/Xor, parallel branches, self-consistency, XOR guards); agents (FunctionCallingAgent, RecursiveLanguageModelAgent/RLM, DeepAgent, Tool, MCP, subagents); KnowledgeBase/RAG (DuckDB, EmbedKnowledge/UpdateKnowledge/RetrieveKnowledge, hybrid search); training (compile/fit/evaluate/predict, callbacks, ProgramCheckpoint); rewards & metrics (ExactMatch, CosineSimilarity, LMAsJudge, ProgramAsJudge, F1Score, custom rewards, masking); optimizers (RandomFewShot, OMEGA/DNS); datasets (gsm8k, hotpotqa, arcagi) and visualization. Synalinks is Keras-shaped, so without guidance LMs mix Keras/LangChain/DSPy syntax — this skill constrains usage to idiomatic Synalinks.

分析一段时间内健康数据的趋势和模式。关联药物、症状、生命体征、化验结果和其他健康指标的变化。识别令人担忧的趋势、改善情况，并提供数据驱动的洞察。当用户询问健康趋势、模式、随时间的变化或"我的健康状况有什么变化？"时使用。支持多维度分析（体重/BMI、症状、药物依从性、化验结果、情绪睡眠），相关性分析，变化检测，以及交互式HTML可视化报告（ECharts图表）。

分析减肥数据、计算代谢率、追踪能量缺口、管理减肥阶段

Alpaca Trading API integration for Model Context Protocol (MCP). V2 generates 60+ tools directly from Alpaca's OpenAPI specs using FastMCP.

分析旅行健康数据、评估目的地健康风险、提供疫苗接种建议、生成多语言紧急医疗信息卡片。支持WHO/CDC数据集成的专业级旅行健康风险评估。

分析运动数据、识别运动模式、评估健身进展，并提供个性化训练建议。支持与慢性病数据的关联分析。

分析中医体质数据、识别体质类型、评估体质特征,并提供个性化养生建议。支持与营养、运动、睡眠等健康数据的关联分析。

分析职业健康数据、识别工作相关健康风险、评估职业健康状况、提供个性化职业健康建议。支持与睡眠、运动、心理健康等其他健康数据的关联分析。

分析康复训练数据、识别康复模式、评估康复进展，并提供个性化康复建议

分析营养数据、识别营养模式、评估营养状况，并提供个性化营养建议。支持与运动、睡眠、慢性病数据的关联分析。

分析健康目标数据、识别目标模式、评估目标进度,并提供个性化目标管理建议。支持与营养、运动、睡眠等健康数据的关联分析。

分析家族病史、评估遗传风险、识别家庭健康模式、提供个性化预防建议

分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。